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THE RUNNER-UP
On the role of
the mineralocorticoid receptor
in human cognition
Susanne Vogel
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THE RUNNER-UP
On the role of the mineralocorticoid receptor
in human cognition
Proefschrift
ter verkrijging van de graad van doctor
aan de Radboud Universiteit Nijmegen
op gezag van de rector magnificus prof. dr. Th.L.M. Engelen,
volgens besluit van het college van decanen
in het openbaar te verdedigen op vrijdag 18 september 2015
om 12.30 uur precies
door
Susanne Vogel
geboren op 26 augustus 1987
te Dresden (Duitsland)
Promotoren:
Prof. dr. Guillén S.E. Fernández
Prof. dr. Marian Joëls (UMC Utrecht)
Copromotor:
Dr. Floris Klumpers
Manuscriptcommissie
Prof. dr. Aart H. Schene
Prof. dr. Karin Roelofs
Prof. dr. Serge A.R.B. Rombouts (Universiteit Leiden)
THE RUNNER-UP
On the role of the mineralocorticoid receptor
in human cognition
Doctoral Thesis
to obtain the degree of doctor
from Radboud University Nijmegen
on the authority of the Rector Magnificus prof. dr. Th.L.M. Engelen,
according to the decision of the Council of Deans
to be defended in public on Friday, September 18, 2015
at 12.30 hours
by
Susanne Vogel
Born on August 26, 1987
in Dresden (Germany)
Supervisors:
Prof. dr. Guillén S.E. Fernández
Prof. dr. Marian Joëls (UMC Utrecht)
Co-supervisor:
Dr. Floris Klumpers
Doctoral Thesis Committee:
Prof. dr. Aart H. Schene
Prof. dr. Karin Roelofs
Prof. dr. Serge A.R.B. Rombouts (Leiden University)
For my family
Contents
1
General introduction
11
2
Linking genetic variants of the mineralocorticoid receptor and negative
memory bias: interaction with prior life adversity
27
3
Blocking the mineralocorticoid receptor in humans prevents the stress-induced
enhancement of centromedial amygdala connectivity with the dorsal striatum
41
4
Cortisol mediates a stress-induced shift towards stimulus-response learning via
the human mineralocorticoid receptor
61
5
A stress-induced shift from trace to delay conditioning depends on the
mineralocorticoid receptor
83
6
General Discussion
103
Appendix
References
123
Nederlandse samenvatting
137
Deutsche Zusammenfassung
141
Donders graduate school for cognitive neuroscience series
145
Acknowledgements Dankwoord Danksagung
155
Publication list
159
Curriculum Vitae
161
Chapter 1
Stress is a condition known to everyone. Certainly, everybody could rapidly come up with
an example of an experience where he or she felt ‘stressed’. Situations like giving a speech
in front of others, fighting against strict deadlines, or undergoing a job interview are often
experienced as stressful. To start with an everyday example, a stressful situation that I remember particularly well was my driving test. Although this is now almost ten years ago, I still
vividly remember my heart beating fast, my sweaty hands clung to the driving wheel, my
general feeling of strong anxiousness, and the tendency to just step out of the car and avoid
the situation. These sensations were the result of the so-called stress response my brain had
initiated. But the hormones which are responsible for the sweaty hands also enter the brain
and change how we behave in stressful situations and what we remember about them. In
this thesis, I set out to better understand what happens in our brain when we encounter
something stressful.
The introduction of this thesis starts with an attempt to define stress. Thereafter, the
biological response to stress is described in more detail including the main messengers and
their way of action. The focus will be on the stress-hormone cortisol and one of its receptors,
the mineralocorticoid receptor (MR). I will briefly touch upon the link between stress and
psychiatric disorders, and how genetic variants in the MR might play a role in this association.
I will also describe a current theory on the function of acute MR-activation, which we tested
in several experiments. Finally, an outline of this thesis is presented.
What is stress?
Although almost everybody can easily come up with an example of a stressful situation, scientifically defining and quantifying stress has not been easy. Apparently, what is stressful for one
person is not necessarily stressful for another person. While the driving test was stressful for
me, somebody else might not have been concerned at all, but might have been most afraid
to give a speech.
Hans Selye, one of the founding fathers of stress research, defined stress as the ‘nonspecific
response of the body to any demand made upon it’ (1979, p. 12). According to this definition, any
threat to the organism’s well-being can serve as a stressor. Examples may be the encounter
of a hungry wild animal, starving for food, or fighting at war. But stress is not only limited to
acute physical threats. In the driving test example, my life was not at stake as I had received
extensive training and was able to drive. It was the worrying thought, the pure imagination
of failing the exam or causing an accident which triggered the stress response. An important
addition to Selye’s definition came therefore from Richard Lazarus, who emphasized the
importance of cognitive appraisal (Lazarus, 1993). He argued that stress occurs when the individual concludes that there is an imbalance between the demands posed and the response
capabilities available in a given situation. Stress therefore results from the judgment that specific demands threaten physical or psychological well-being because they might challenge or
13
1
Chapter 1
exceed the coping capacities available. In contrast to our use of the word stress in everyday
life, this definition does not only apply to negative events. Events that are generally considered to be favorable, such as starting a new job or getting married also have the potential to
bring us out of balance. However, the stressors with the strongest effects are the negative
ones, especially if they contain social evaluative threat and are perceived as uncontrollable
(Dickerson & Kemeny, 2004). We can use this knowledge on the factors resulting in a stressful
experience to experimentally induce mild stress in the laboratory and investigate how this
affects participants’ behavior.
Detection of threat and the stress response
In the driving test example I mentioned some symptoms of being stressed such as the sweaty
hands and the increased heart rate. However, these are only few endpoints of a larger coordinated stress response, which includes behavioral, cognitive, and autonomic changes aimed
at coping with the challenge and reinstalling homeostasis. But what exactly happens in our
body if we encounter a stressor?
The core brain structure for detecting threat, initiating emotional responses, and remembering these emotional events is the amygdala. The amygdala receives sensory inputs and detects
stimuli that are important for the organism. Stimuli such as fearful or angry faces signal potential danger and reliably activate the amygdala (Davis & Whalen, 2001) which in turn initiates
the stress-response. In line with its important role in threat detection and emotional response
generation, the amygdala was shown to be hyperactive in patients with anxiety disorders who
suffer from excessive emotional reactions and hypervigilance to salient stimuli (Etkin & Wager,
2007). Neural plasticity in the amygdala is also crucial in fear learning and fear expression
(LeDoux, 2000). However, the amygdala is not only involved in memories specific to fear, it also
mediates the effects of emotion on memories which are encoded in other brain regions. For
example, amygdala activation enhances declarative memory consolidation in the hippocampus (McGaugh et al, 1996) and appears to shift the memory systems underlying spatial memory
(Packard & Teather, 1998) and procedural memory (Schwabe et al, 2013c) towards a dominance
of habitual, automated systems at the cost of complex, flexible systems underlying memory.
This shift will be discussed in further detail below. First, I will consider the initiation of the stress
response via amygdala outputs to the autonomous nervous system and the hypothalamus.
The stress response has been well described over years of intense research (Hermans et al,
2014b; Joëls & Baram, 2009), and I will focus here on two major stress-systems. The first system
relates to catecholamines such as epinephrine and norepinephrine being released by activation of the locus cœruleus and the autonomous nervous system (Aston-Jones, 1985; Sara &
Bouret, 2012; Valentino & Van Bockstaele, 2008). Catecholamines lead to increased heart rate,
blood pressure, and breathing rate to prepare the body for fight-or-flight reactions (Bremner
et al, 1996; Kalat, 2001). They decrease other bodily functions which are not necessary for
14
immediate survival such as digestion (Dünser & Hasibeder, 2009) or reproduction (Kraut et
al, 2004). On a neural level, norepinephrine rapidly leads to an upregulation of the ‘salience
network’, a set of brain regions important in detecting and processing important stimuli (e.g.
emotional faces). At the same time, norepinephrine down-regulates the ‘executive control
network’, another set of brain regions implied in complex, flexible cognition such as goaldirected planning or working memory (Hermans et al, 2014b). Together, these norepinephrine
effects lead to improved vigilance such that salient stimuli are better detected, but it comes at
the cost of decreased elaborate cognition.
The second system activated under stress, the hypothalamus-pituitary-adrenal (HPA) axis,
leads to the release of corticotropin releasing hormone (CRH) in the hypothalamus within
seconds. CRH in turn drives the pituitary gland to secrete adrenocorticotropic hormone
(ACTH) into the blood stream. ACTH then reaches the adrenal glands located on top of both
kidneys and induces the production and release of glucocorticoids (mainly corticosterone in
rodent and cortisol in humans) (Kalat, 2001). Glucocorticoids in turn have several functions
in the stress response, for example the increase of blood glucose levels and the suppression
of the immune system. However, glucocorticoids also affect brain function, which will be
described in detail below. The activation of this second system is somewhat slower than the
first one, as glucocorticoid concentrations take about fifteen minutes to rise to their peak
levels. The release of glucocorticoids is tightly regulated by negative feedback loops at the
level of the hypothalamus and the pituitary, but also the hippocampus (Jacobson & Sapolsky,
1991). These feedback loops prevent the system from overshooting in response to stress and
normalize hormone levels after the stressful situation has passed (e.g. Dallman & Yates, 1969;
de Kloet, 2014; DeKloet & Reul, 1987).
The two stress systems do not act independently of each other. For example, the sympathetic nervous system acts upon the adrenal cortex, where glucocorticoids are produced,
and can influence glucocorticoid secretion (Ulrich-Lai & Engeland, 2005). As another example,
some of the effects of glucocorticoids on memory depend on the presence of catecholamines
(Roozendaal et al, 2006a). This suggests a close interplay and complementary action of the
two major stress systems.
To summarize, the amygdala detects threat and rapidly activates the stress response. This
response affects cognition by enhancing vigilance, impairing complex cognition, and shifting the balance of systems underlying memory formation. The somatic effects of the quickly
released catecholamines together with the slower but longer acting glucocorticoids prepare
us optimally to cope with challenging situations by increasing heart rate, blood pressure, and
available energy for fight and flight responses. Other bodily functions are decreased under
stress such as digestion and immune function, but also pain perception (Guillemin et al,
1977). For survival it is important to rapidly activate the stress-response when it is needed, but
also to efficiently terminate it once the stressful situation has passed. Otherwise, the initially
beneficial effects of the stress-response might become maladaptive.
15
1
Chapter 1
It is important to realize that the brain initiates this stress response to a multitude of
stressors. Our coordinated and complex stress response is activated whenever we encounter
something we appraise as stressful, irrespective of whether it is a lion or a driving examiner
standing in front of us. As Ron de Kloet, one of the Dutch pioneers in stress research, stated:
‘Stress is a state of tension that reflects not so much what happens but rather how one takes it’
(de Kloet, 2014). It is also important to keep in mind that our stress response has presumably
evolved for short-term physical stressors, which can be dealt with by enhanced vigilance,
muscle power, automated behaviors, and well-learned habits.
Two receptor types for glucocorticoids
In this thesis, I will mainly focus on the action of glucocorticoids. They bind to two receptor
types in the brain, named mineralocorticoid (MR) and glucocorticoid (GR) receptor. These
receptors differ in their distribution in the brain, function, and affinity for glucocorticoids, i.e.
how easily glucocorticoids can bind to them. The MR is mostly expressed in the hippocampus,
the amygdala, lateral septum, parts of the prefrontal cortex, and the cerebellum (Arriza et al,
1988). These structures are among others associated with cognitive functions such as spatial
orientation, detection of threat, fear memory, and regulation of the stress response, which
might therefore all be modulated by the MR. The MR has an approximately tenfold higher affinity for cortisol than the GR (DeKloet & Reul, 1987; Rupprecht et al, 1993), which led researchers
to assume that the MR would be continuously activated, even under non-stressful conditions.
Therefore, it was thought that the MR would not contribute to stress-related changes in brain
and behavior. Rather, the MR was thought to maintain baseline firing activity of neurons,
to enable low baseline levels of cortisol through negative feedback, and to determine the
sensitivity of the HPA axis (de Kloet et al, 2008a; DeKloet & Reul, 1987; Joëls et al, 2008). The GR
on the other hand is more widely distributed in the brain and has a lower affinity for cortisol,
making it more suitable to mediate stress-induced changes. Therefore, stress research has
focused on the GR for a long time. Importantly though, in recent years it was discovered that
both MR and GR can also be found at the plasma membrane, as opposed to the cytoplasm
or nucleus of neurons (Joëls et al, 2008; Karst et al, 2005). Interestingly, in this form the MR
appears to have a much lower affinity for cortisol, allowing it to respond to stress-induced
cortisol increases just like the classical GR (Joëls et al, 2008; Karst et al, 2005).
What happens if cortisol binds to these receptors? Both MR and GR have two modes of action, a rapid non-genomic mode and a slow genomic mode, mediated by receptors residing
at the membrane or in the cytoplasm, respectively (Joëls et al, 2012). The rapid non-genomic
effects of the MR were only discovered recently and seem to be involved in the appraisal
of new situations, HPA axis activation, and enhancement of catecholaminergic effects (Groeneweg et al, 2011; Joëls et al, 2008; Tasker et al, 2006). For example, they enhance excitability
of the amygdala and hippocampus in-vitro within minutes and increase alertness (Groc et al,
16
2008; Karst et al, 2010; Karst et al, 2005; but see Henckens et al, 2011; Henckens et al, 2012b). The
slow effects, attributed mainly to the GR, seem to suppress catecholamine action (e.g. Pu et
al, 2007), decrease excitability, reinstall homeostasis, decouple the amygdala from other brain
regions involved in the stress response, and promote stress recovery and memory formation
by activating or repressing different gene pathways (Henckens et al, 2011; Henckens et al,
2012; Herman et al, 2012; Joëls & Baram, 2009). To summarize it in a simplified fashion, rapid activation of the MR might mediate permissive effects of cortisol whereas the slow action of the
GR results in normalizing the stress response and storing important information in memory.
However, as yet, little is known about rapid glucocorticoid effects on human cognition
presumably mediated by membrane-bound MRs. Given the expression pattern, it is conceivable that the MR might play a role in the processing of salient stimuli and memory formation.
The aim of this PhD project was therefore to learn more about the cognitive functions
of the human MR, the ‘runner-up’ receptor for glucocorticoids. While this was the main goal
driving this thesis, there is a secondary, long-term goal which involves the clarification of the
neurobiology underlying stress-related mental disorders. Therefore, I will now briefly discuss
the association between stress and psychiatric disorders.
The link between stress and psychopathology
It has been repeatedly shown that stress can increase the risk for onset or maintenance of
psychiatric disorders. For example, stressful negative life events such as losing a loved one or
being victim of a crime can enhance the vulnerability to develop major depressive disorder
(commonly known as depression, Kendler et al, 1999). They are also a defining criterion for
post-traumatic stress disorder (PTSD), and they enhance the risk for other anxiety disorders,
borderline personality disorder, schizophrenia, addiction, and bipolar disorder (Agid et al, 1999;
Koob, 2013; Pietrek et al, 2013). Moreover, the stress systems outlined above are disturbed in
patients with various psychiatric disorders (de Kloet et al, 2005) indicating an important role for
stress in psychopathology (Varghese & Brown, 2001; Young, 2004). Stress-related mental disorders are common and the life-time prevalence, i.e. the likelihood that an individual develops
a certain disorder during its life, is at 20.8% for mood disorders of which three quarters can be
attributed to depression (values for the United States of America; Kessler et al, 2005; values for
other countries in Smith, 2014). That means that one out of five individuals will have to handle
a mood disorder at least once. The life-time prevalence for anxiety disorders is estimated at
18.8%, of which PTSD amounts to a third (values for the United States of America; Kessler et
al, 2005). There is a certain overlap between these disease categories (so the total amount of
affected individuals is likely less than the sum of the two numbers), but still these statistics are
worrying. Furthermore, these disorders are among the leading causes of worldwide disability
(Kessler et al, 2005; Mathers et al, 2008; Smith, 2014) and a further increase of their prevalence
is predicted (Mathers & Loncar, 2006). Next to psychopathology, stress also increases the risk
17
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Chapter 1
of somatic diseases such as diabetes, ulcers, or cardiovascular problems (Sapolsky, 2004).
Considering the societal costs related to all these diseases, a better understanding of stress
and the stress-response is of great importance in order to better treat or even prevent health
problems related to stress (Collins et al, 2011).
Despite its role in diseases, it is important to keep in mind that stress is not inherently
harmful or dysfunctional. Initially, the stress-response will often lead to increased cognitive
performance, due to the extra energy that is being mobilized, enhancing focus and vigilance.
Also, when considering the prevalence rates of stress-related mental disorders, one should
keep in mind that most individuals who encounter stressful life events (approximately 50%
in the United States of America; Breslau et al, 2004) do not develop problems. Or, as Lazarus
and Folkman put it, ‘Stress is an inevitable aspect of the human condition, it is coping that makes
the big difference in adaptational outcome’ (Lazarus & Folkman, 1984, page 6). Also, the stress
systems have evolved over thousands of years suggesting that their effects are not harmful
per se. On the contrary, the stress response was most likely beneficial in evolution and still is
today, by allowing us to survive acute stressors and remembering their impact for future use.
Given the large individual differences in response to stress, an important question is why
some individuals can successfully cope with stressful life events while in other individuals
these events trigger psychiatric or somatic diseases. Investigating these inter-individual
differences in response to stress can provide important information on what makes some
individuals more vulnerable and others more resilient. This knowledge might be useful when
developing new prevention or treatment strategies.
One factor that might play a role in these inter-individual differences is the genetic makeup of an individual. It is well-known that specific mutations cause certain diseases, for example
bleeding disorder (hemophilia) or trisomy 21 (Down syndrome). For complex stress-related
mental disorders such as depression the underlying genetic mechanisms appears to be less
straightforward. Although it is known that these disorders are heritable to a considerable
extent, there is most likely not a single gene coding for each of them, but many variations
with small effects each, which in combination affect our risk to fall ill. On top of that, genes
might not link directly to stress-related disorders, but instead modulate vulnerability, which
can then be aggravated or relieved by the interaction with environmental factors (Caspi et
al, 2003; Ingram & Luxton, 2005; Karg & Sen, 2012). Finally, disorders such as depression are
multifactorial and have heterogeneous phenotypes, i.e. the specific causes and characteristics
we can observe might differ between individual patients. For example, while one patient
might be characterized by strong negative mood, weight loss, and problems sleeping and
concentrating, another patient might suffer more from a strong loss of interest in the things
he or she enjoyed before, hypersomnia (sleeping too much), and weight gain. Both patients,
however, might be assigned the same disease label ‘major depressive disorder’. The genetic and
phenotypic complexity make it difficult to identify causal genetic variants when comparing
patients with stress-related mental disorders to healthy controls (Ripke et al, 2013).
18
A promising approach is therefore to investigate the link between common genetic variants
and dimensional traits which can be associated to psychopathology, but can also be studied
in large samples of healthy participants. Such traits can be considered as endophenotypes.
Endophenotypes represent heritable phenotypic constructs which are presumably more
directly affected by genetic variations than heterogeneous and complex disease categories
such as major depressive disorder (Gottesman & Gould, 2003; Kendler & Neale, 2010). One
cognitive endophenotype for depression which can also be assessed in healthy individuals is
‘negative memory bias’. This conceptualizes the tendency of depressed individuals to preferably
encode and remember sad and pessimistic information. Negative memory bias is assumed
to be a main cognitive risk and maintenance factor for depression (Beck, 2008; Mathews &
MacLeod, 2005) and heightened in individuals with a vulnerability to develop depression,
e. g. highly neurotic individuals or siblings of patients (Chan et al, 2007; van Oostrom et al,
2013). On top of that, negative memory bias was associated to enhanced amygdala volume
and decreased hippocampal volume in healthy adults (Gerritsen et al, 2011). Similar structural
changes have been reported for patients with depression, further supporting the relevance of
negative memory bias as an endophenotype which can be used to study genetic risk factors
for psychopathology in healthy individuals.
Genetic differences in MR-functionality: the MR as a resilience factor?
Given the link between stress and psychiatric disorders, the receptors for glucocorticoids
are an interesting target for genetic studies. In this thesis, I will focus on the MR, the reader
interested in the effects of genetic variants in the GR and its pathway is referred to recent reviews on this subject (Koper et al, 2014; Zannas & Binder, 2014). First, I will summarize findings
from rodent experiments. An advantage of working with rats and mice is that the effects of
strong changes in MR-functioning such as overexpression and knockout can be investigated.
Although the findings were not always consistent and sex differences were partially present,
enhanced forebrain MR-expression was in general associated to smaller cortisol responses
to stress, and prevention of the HPA from overshooting (Harris et al, 2013; Rozeboom et al,
2007). Furthermore, memory was found to be enhanced in these animals (Arp et al, 2014;
Harris et al, 2013; Lai et al, 2007), anxiety reduced, and neuronal loss after ischemia attenuated
(Lai et al, 2007). Also rats overexpressing the MR specifically in the amygdala displayed lower
stress-induced cortisol and anxiety levels (Mitra et al, 2009). Finally, MR overexpression aided in
protecting against the impairing effects of glucocorticoids on memory (Ferguson & Sapolsky,
2008). Together, these studies suggest protective effects of MR overexpression. In contrast,
animals without forebrain MR due to knock-out showed enhanced arousal after stress (Brinks
et al, 2009) and some evidence points to enhanced baseline cortisol levels (ter Horst et al,
2012b) but similar HPA axis responsivity after stress (Berger et al, 2006). Furthermore, MRknockout animals were found to have impaired spatial and contextual fear memory (Arp et al,
19
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Chapter 1
2014; Brinks et al, 2009; Zhou et al, 2010), arguing for a possible role of the MR in spatial and fear
memory. Finally, MR-knockout animals were found to employ different strategies in exploring
novel information (Arp et al, 2014). In sum, these results point towards a role for heightened
MR functioning being associated to reduced cortisol responses and anxiety levels, improved
spatial and contextual fear memory, and enhanced neuronal survival. However, one should
keep in mind that overexpressing or knocking out genes are strong manipulations which
induce large-scale changes and might model a much stronger phenotype than common
variations in healthy humans would induce.
For humans, several studies have by now investigated the effects of common variations in
the gene coding for the MR, called NR3C2 (nuclear receptor subfamily 3, group C, member 2).
It is located on chromosome 4 and several variants in this gene have been reported. Although
the findings were again not always consistent, a picture emerged which is often in line with
the findings in rodents. Variants leading to a loss in function were associated to higher baseline cortisol levels and a higher cortisol awakening response (Klok et al, 2011c; Kuningas et al,
2007; Muhtz et al, 2011; but see van Leeuwen et al, 2010). The cortisol awakening response is a
natural increase in morning cortisol levels in most individuals which can be used as a marker
for HPA axis activity (Wüst et al, 2000b) and is enhanced by chronic stress or worries (Wüst
et al, 2000a). Furthermore, loss-in-function variants might be linked to lower stress-induced
cortisol levels, accompanied by higher perceived stress (van Leeuwen et al, 2011), but see
(Bouma et al, 2011). Interestingly, genetic variants in the MR were also related to changes in
heart rate after stress (DeRijk et al, 2006; van Leeuwen et al, 2011), suggesting an interaction
with catecholamines. In terms of psychiatric disorders, it was found that loss-in-function variants might be related to a stronger feeling of hopelessness and higher depression rates in
pre-menopausal women (Klok et al, 2011b) and old age (Kuningas et al, 2007), higher levels of
neuroticism (DeRijk et al, 2011), and stronger amygdala reactivity to emotional faces (Bogdan
et al, 2012). These findings suggest again a role for the MR in psychopathology and the processing of biologically salient stimuli.
The rodent and human studies summarized above suggest that common variations in
the gene coding for the MR might have effects on the stress systems, memory, vigilance to
salient cues, and mental health. In line with the gene-environment framework, these effects
might be triggered, exaggerated, or relieved by environmental influences. Studying geneenvironment interactions might therefore help us to understand why some individuals can
adapt to stressful events better than others. The first question we investigated in this
thesis is whether common variants in the gene coding for the MR are related to an
enhanced susceptibility to develop psychiatric disorders such as depression (chapter
2). This was tested in a group of almost 500 healthy participants by assessing not only their
genotype, but also their ‘negative memory bias’. The research question was twofold: Is there a
relationship between variants in the MR gene and negative memory bias as endophenotype
for a heightened susceptibility to develop depression? Furthermore, if such an association is
20
present, is it affected by stressful life events as predicted by the gene-environment-interaction
framework? Answering these questions might explain parts of the striking inter-individual
variance in response to stressful life events, leading to vulnerability in some individuals and
resilience in others.
Acute effects of MR-activation
After establishing cognitive consequences of stable variations of MR functionality (chapter 2),
I will turn to the effects of acute activation or blockade of this receptor in humans. Such acute
manipulations of MR-activation can be achieved by means of pharmacology, using drugs
that either activate the MR (agonists such as fludrocortisone) or block it (antagonists such as
spironolactone). Studies acutely manipulating MR-activity are generally in line with the effects
of genetic variations described above. In line with a role of the MR in regulating the HPA axis,
activation of the MR was repeatedly shown to decrease cortisol and ACTH levels (Groch et al,
2013; Otte et al, 2010; Otte et al, 2006; Otte et al, 2014). MR blockade on the other hand increased levels of cortisol, with mixed effects on ACTH levels (Cornelisse et al, 2011; Deuschle et
al, 1998; Otte et al, 2010; Otte et al, 2007; Rimmele et al, 2013; Schwabe et al, 2013b; Schwabe et
al, 2013c; Young et al, 2003; Young et al, 1998). Acute MR-blockade under baseline conditions
(without an additional stress induction) impaired declarative memory retrieval (Otte et al,
2007; Rimmele et al, 2013), selective attention (Cornelisse et al, 2011; Otte et al, 2007), and - at
trend-level - cognitive flexibility (Otte et al, 2007). Conversely, pharmacological MR activation
(without an additional stress induction) enhanced declarative memory (Groch et al, 2013; Otte
et al, 2014), emotional empathy (Wingenfeld et al, 2014), and executive function (Otte et al,
2014). These findings again suggest that the MR is involved in HPA axis regulation, memory,
and emotional processing. However, little is known about the effects of the MR in acutely
stressful situations. The few studies which included a stress-induction and pharmacological
manipulation of the MR suggest that this receptor mediates the stress-induced enhancement
of response inhibition (Schwabe et al, 2013b) and striatal learning at the expense of hippocampal learning (Schwabe et al, 2013c) and working memory (Cornelisse et al, 2011).
One hypothesis derived from these latter findings suggests that the MR may be involved in
a general stress-induced reallocation of neural resources causing a shift away from complex,
flexible, and goal-directed systems, and towards more habitual, reflexive, and automated
systems (Packard & Teather, 1998; Packard & Wingard, 2004). This stress-induced shift is assumed to promote survival by relying on well-learned reflexive behavior in the face of acute
threat and limited cognitive resources. In a state of acute threat it is assumed that long and
resourceful thinking of all response options might often be dangerous whereas fast responding and a focus on short-term survival would be beneficial. Some of the first studies on this
stress-induced shift were conducted by Packard and colleagues (e.g.Kim et al, 2001; Packard
& Teather, 1998; Packard & Wingard, 2004). They observed that stress caused rats to use a
21
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Chapter 1
simpler, more automated system to learn information. Further studies supported their results
also for humans. It seems that stress impairs hippocampus-dependent and executive control
network functions (Henckens et al, 2012b; Pruessner et al, 2008) but enhances processing in
the amygdala and the dorsal striatum (Cousijn et al, 2012; Kim et al, 2001; Schwabe et al, 2013c;
van Marle et al, 2009). When this PhD project started, a seminal study had recently shown
that a stress-induced shift towards more automated spatial memory was dependent on availability of the MR in mice (Schwabe et al, 2010a). Therefore, we set out to investigate the role
of the human MR in stress-induced changes in three cognitive domains which might
involve the MR, i.e. the processing of salient stimuli, spatial memory, and fear memory.
We hypothesized that stress leads to a broad shift in behavior and cognition, mediated by the
amygdala and dependent on cortisol interacting with MRs. To test this, we set up a large study
with 101 healthy male participants who underwent either a standardized stress-induction
protocol or control procedures. Half of the participants received the MR-antagonist spironolactone before testing, whereas the other half received placebo. During the stress response,
they performed three tasks (see Figure 1.1) while undergoing functional magnetic resonance
imaging (fMRI), a non-invasive way to measure brain activity and connectivity (i.e. the crosstalk
between brain-regions). Given earlier findings involving the MR in the processing of salient
stimuli, spatial, and fear learning, the tasks were chosen to cover these three cognitive domains. The findings presented in chapter 3, 4, and 5 are therefore based on data acquired
using different tasks in one large-scale pharmacological project. The chapters are related in
that they all target the role of the MR in shifting neural resources under stress. However, they
investigate distinct research questions which are outlined below.
With the first task, we aimed at investigating functional connectivity of the amygdala
while participants were processing salient stimuli, i.e. pictures of faces with angry and fearful expressions. We tested if stress leads to rapid changes in the crosstalk of the amygdala
Figure 1.1. Experimental setup implemented for the pharmacological studies described in chapter 3, 4, and 5.
Participants were administered with either an MR-antagonist or placebo before undergoing a stress-induction
or non-stressful control procedures. Three tasks followed investigating the processing of emotional faces, spatial memory, and fear memory. Finally, an anatomical scan was acquired. Time is indicated in minutes after
stress induction. See main text for details.
22
with other brain regions, and how this would be affected by MR-blockade (chapter 3). We
hypothesized that stress, mediated by cortisol interacting with the MR, would rapidly lead to
stronger amygdala connectivity to regions supporting automated behavior, i.e. the striatum.
Such a finding would be in line with a stress-induced shift of neural resources towards system
supporting habitual behavior. Moreover, we investigated which sub-region of the amygdala
drives this stress-induced shift. The amygdala is not a homogenous structure, but consists of
several subnuclei with different functions and connectivity patterns (McDonald, 1998). The
basolateral amygdala complex (BLA) is assumed to be the input structure receiving sensory
information and detecting salient environmental stimuli. If threat is detected, the BLA will
activate the centro-medial amygdala (CMA) which constitutes the output structure and is
connected to the brainstem, hypothalamus, and the striatum, the latter mediating habitual
responses (Fudge et al, 2002; Han et al, 1997). Given these different functions and connections
of the amygdala sub-regions, we assumed that the stress-induced shift towards an enhanced
engagement of the striatum might arise from the CMA. Such a result would support and
refine earlier studies on the stress-induced shift orchestrated by the amygdala and dependent
on the MR in humans.
The second task in this project served to investigate spatial memory formation as the MR
has been implicated in spatial memory in rodents (chapter 4). Learning about our spatial environment and navigating through it is a very important skill we use every day without thinking
much about it. When we commute to work, go to the supermarket, or visit friends, we rely on
our brain to ‘know’ the way so that we do not get lost. We can see how debilitating it is to lose
this ability in patients with orientation problems, for example in Alzheimer’s disease. While this
thesis is not about the basic mechanisms of how we are able to navigate, we tested the effects
of stress on spatial memory formation. In short, we focused on two systems which can be
used to learn about our spatial surroundings (Packard et al, 1989; Packard & McGaugh, 1992).
One of them, relying on the dorsal striatum, is about learning which specific responses (e. g.
turn left, turn right) lead to an intended goal location. Reference objects (so-called landmarks)
are represented in relation to one’s own body in an egocentric perspective. For example, if
you describe the way to your home to someone else, you might advise to drive until the first
traffic light (landmark #1), then turn left (response #1) until you reach the city hall (landmark
#2), and then turn right again (response #2). This system is assumed to use fewer resources
and to be very efficient, provided the surroundings do not change. However, if there would be
an unexpected road closure, the visitor might get lost. Therefore, we have a second system in
place, relying on the hippocampus, for learning about more complex representations of space
using configurations of landmarks and the geometry of the environment independent of our
personal viewpoint (Morris et al, 1982). The result will be a map-like representation which we
can flexibly use to find possible shortcuts or bypasses. I will refer to the latter as place learning (other terms used are viewpoint-independent processing, allocentric processing, spatial
learning; van Hoogmoed, 2014), whereas the former will be referred to as stimulus-response
23
1
Chapter 1
learning. In line with the idea of a stress-induced shift towards habit-like learning, stress has
been shown to increase reliance on the simpler stimulus-response learning in spatial memory
tasks (Kim et al, 2001; Packard & Wingard, 2004; Schwabe et al, 2007; Schwabe et al, 2010a).
This effect might be familiar to those who have moved within a city and might have found
themselves cycling to the old address if they were stressed before cycling home. Using a task
which allowed us to dissociate the two learning systems in the MRI scanner, we set out to
test the effects of stress on spatial memory formation and their underlying neural basis and
dependence on the MR in humans. We hypothesized a stress-induced shift towards stimulusresponse learning in behavior, brain activity, and connectivity. Again, we then tested whether
this shift depends on the availability of MRs, as predicted by earlier findings in rodents.
Finally, the third task investigated the role of the MR in emotional memories, more precisely
fear memories (chapter 5), as previous literature implicated the MR in fear learning. The link
between stressful events and fear learning is very important for psychiatric disorders such as
PTSD, but hardly investigated. However, stress and emotion have strong influences on memory.
In general, we remember emotional events better than neutral events (Hermans et al, 2014a;
McGaugh, 2013), which is assumed to be adaptive for survival: we should remember the
important events (negative and positive) to prepare ourselves for similar events in the future.
But how is fear learning affected by stress? Does stress enhance or impair fear learning? And
why can fear learning under stressful conditions become maladaptive and harmful such as in
PTSD? Just as spatial memory, also fear learning can be supported by several systems in the
brain, differing in their complexity and therefore being possibly affected by a stress-induced
shift towards more automated systems. In line with the shift model, we assumed that stress
would enhance simpler stimulus-threat associations at the cost of more complex associations.
Such a finding could possibly explain clinical observations in patients with PTSD, who have
very strong memories for single cues, but who have difficulties in bringing these fragmented
memories into a coherent sequence of events (Acheson et al, 2012). Again, we tested the
underlying neural mechanisms of this shift and its possible mediation by MR-activation.
Together, these three chapters on the effects of acute MR-activation and MR-blockade will
provide us with more information on the role of the MR in stress-induced changes in the
human brain. We will show that stress indeed seems to induce a rapid shift towards more
automated forms of learning, that this is associated to specific changes in brain activity and
connectivity, and that the MR appears to play a crucial role in this shift.
Summary of the introduction
In this thesis, we aimed at gaining a better understanding of the role of the human MR in
cognition by combining a genetic and a pharmacological approach. We first tested how
stable natural differences in MR-functioning caused by genetic variations in the MR affect
the susceptibility for stress-related mental disorders in healthy individuals. Thereafter, we
24
set out to enhance our knowledge on the cognitive effects of acute manipulations of MRfunctioning. We therefore challenged the MR by investigating acute stress-induced changes
in participants with available MRs and in participants who received an MR-antagonist. By
learning more about the effects of stable and acute differences in MR-functioning, we hoped
to better understand the role of the MR in human cognition.
The following four chapters will describe the projects outlined above in detail. As already
indicated, the studies conducted in chapter 3, 4, and 5 were based on different data acquired
in the framework of one large-scale project with the same participants. Nonetheless, each
chapter will contain a full description of the methods and results in order to give a full,
comprehensive account on each separate project. In the concluding general discussion, I will
integrate and summarize the findings, discuss potential implications and point out remaining
open questions.
25
1
Chapter 2
Linking genetic variants of the
mineralocorticoid receptor and negative
memory bias: interaction with prior life
adversity
Theworkinthischapterwaspreviouslypublishedas:S.Vogel,L.Gerritsen,I.vanOostrom,
A.Arias-Vásquez,M.Rijkpema,M.Joëls,B.Franke,I.Tendolkar,G.Fernández(2014).
Psychoneuroendocrinology40,181-190.
Chapter 2
Abstract
Substantial research has been conducted investigating the association between life adversity
and genetic vulnerability for depression, but clear mechanistic links are rarely identified and
investigation often focused on single genetic variants. Complex phenotypes like depression,
however, are likely determined by multiple variants in interaction with environmental factors.
As variations in the mineralocorticoid receptor gene (NR3C2) have been related to a higher
risk for depression, we investigated whether NR3C2 variance is related to negative memory
bias, an established endophenotype for depression, in healthy participants. Furthermore, we
explored the influence of life adversity on this association. We used a set-based analysis to
simultaneously test all measured variation in NR3C2 for an association with negative memory
bias in 483 participants and an interaction with life adversity. To further specify this interaction,
we split the sample into low and high live adversity groups and repeated the analyses in both
groups separately. NR3C2 variance was associated with negative memory bias, especially in
the high life adversity group. Additionally, we identified a functional polymorphism (rs5534)
related to negative memory bias and demonstrating a gene-by-life adversity interaction.
Variations in NR3C2 are associated with negative memory bias and this relationship appears
to be influenced by life adversity. As negative memory bias is implicated in the susceptibility to depression, our findings provide mechanistic support for the notion that variations in
NR3C2 - which could compromise the proper function of this receptor - are a risk factor for the
development of mood disorders.
28
Linking genetic variants of the mineralocorticoid receptor and negative memory bias
Introduction
Mood disorders such as major depressive disorder (MDD) result in increased mortality risk and
an immense burden for patients, their families, and society. The lifetime prevalence for depression in the United States of America amounts to 16.5% (Kessler et al, 2005) and further increase
is predicted (Mathers & Loncar, 2006). Thus, the search for risk factors for mood disorders is of
particular importance (Collins et al, 2011).
To find genetic risk factors for heterogeneous and complex diseases such as MDD, the
endophenotype approach has gained increasing recognition (Franke et al, 2009; Hasler et al,
2004). Endophenotypes represent heritable phenotypic constructs which are presumably
more directly affected by genetic variations than disease categories or symptoms (Gottesman & Gould, 2003). In contrast to overt clinical phenotypes, they appear less complex and
more homogenous (Kendler & Neale, 2010). Endophenotypes can be conceptualized and
measured on different levels. For example, they can be found at the level of cell functioning,
variations of brain function or structure, or at the level of behavior (Franke et al, 2009). Several
endophenotypes have been proposed for MDD, among them negative memory bias (Beck,
2008; Hasler et al, 2004; Mathews & MacLeod, 2005), i.e. the tendency of depressed individuals
to show enhanced memory for sad and pessimistic information. This bias forms a main cognitive risk and maintenance factor for MDD (Mathews & MacLeod, 2005), persists after remission
(Leppanen, 2006) and is heightened in individuals with vulnerability to develop MDD (Chan
et al, 2007; van Oostrom et al, 2013). Negative memory bias has also been associated with
comparable structural brain variations as frequently found in MDD, i.e. increased amygdala
volume and decreased hippocampal volume (Gerritsen et al, 2011).
Using negative memory bias as endophenotype for MDD, we investigated specifically
whether genetic variation in a receptor for the stress hormone cortisol, the mineralocorticoid
receptor (MR), is associated with a higher vulnerability for MDD. MRs act together with glucocorticoid receptors (GRs) to regulate the hypothalamus-pituitary-adrenal (HPA) axis, one of
the major stress systems, which is altered in MDD (Joëls et al, 2008). In the nuclear version,
MRs have such a high affinity for glucocorticoids that they appear to be substantially activated even at baseline levels of cortisol (Joëls et al, 2008). For a long time, research therefore
primarily focused on the lower-affinity GRs (Anacker et al, 2011). However, it was recently
shown that MRs also locate in the membrane of neurons. There, MRs appear to have a lower
affinity, so that they respond to stress-induced increases of cortisol and play a functional
role in mediating stress effects (Joëls et al, 2008). In terms of psychopathology, brain MR
expression is reduced in depressed patients (Klok et al, 2011a) and administration of MR
agonists accelerates pharmacotherapeutic effects in MDD (Otte et al, 2010). Variations of the
MR gene (also called nuclear receptor subfamily 3, group C, member 2, NR3C2) associated
with loss-in-function or reduced expression have been related to hopelessness and higher
MDD rates in pre-menopausal women (Klok et al, 2011b), neuroticism (DeRijk et al, 2011),
29
2
Chapter 2
decreased HPA axis responsiveness (van Leeuwen et al, 2011), and higher amygdala reactivity
(Bogdan et al, 2012).
Beyond genome-wide approaches, most previous studies investigating the influence of
a candidate gene on MDD used single-SNP-based testing. More complex phenotypes like
MDD or memory bias, however, are likely determined by several SNPs which each contribute
small effects (Franke et al, 2009). Single-SNP analyses could therefore not optimally explain the
heritability of such traits (Schwender et al, 2011). A newer approach is testing the combined
effect of all genetic variations within a set of SNPs in a single analysis (Bralten et al, 2011;
Deelen et al, 2011). This approach needs less power than genome-wide testing and also allows unbiased identification of SNPs within this set that were not yet known to be associated
with the phenotype of interest.
It has repeatedly been shown for psychiatric disorders including MDD that genes might
not link directly to disease, but instead genes modulate the vulnerability for such diseases.
Thus, gene-by-environment interaction seems to be the prototypical mechanism leading to
the development of several mental disorders (Caspi et al, 2003; Karg & Sen, 2012). This idea is in
contrast to a genetic main-effect hypothesis that assumes the direct causation of a disorder by
a specific genetic variation. The gene-by-environment interaction framework postulates that
environmental factors cause disorders to occur and that genetic variants influence vulnerability as well as resilience to these factors, leading to psychopathology in some individuals only.
It is well established that the experience of life adversity is an important environmental
factor in the etiology of MDD (Klengel & Binder, 2013). For example, a study by Kendler and
colleagues demonstrated a causal relationship between stressful life events and the onset of
MDD (Kendler et al, 1999). Cortisol has been implicated in mediating this interaction between
life adversity and MDD (Wilkinson & Goodyer, 2011). The MR may play a particular role in this
interaction as it was implicated in the stress response and feedback processes of the HPA axis
(DeRijk & de Kloet, 2008; DeRijk et al, 2011), in emotional memory (Zhou et al, 2010), and anxiety (Brinks et al, 2009). Thus, we set out to test whether variation in the MR as a receptor for the
stress hormone cortisol could serve as a risk factor for the development of mood disorders,
by probing the endophenotype negative memory bias. The association between negative
memory bias and all SNPs genotyped in NR3C2 was tested simultaneously in a large sample
of healthy adults. We hypothesized a gene-by-life adversity interaction such that a potential
association between NR3C2 and negative memory bias would be stronger for individuals who
experienced more life adversity.
Methods
Participants
The present behavioral study was part of the Brain Imaging Genetics (BIG) project conducted
at the Donders Institute. Memory bias was assessed in 483 participants of BIG (62.3% females),
30
aged 18-35 years (mean 22.4 years [SD=3.16], Table 1) who volunteered to complete a webbased test battery. All participants were of European descent (Stein et al, 2012; Supplementary
Table 7) and fluent in Dutch. They were screened using a self-report questionnaire for the
following exclusion criteria: history of somatic disease potentially affecting the brain; current
or past psychiatric or neurological disorder; medication (except hormonal contraceptives) or
illicit drug use during the past six months; history of substance abuse; current or past alcohol
dependence; pregnancy; lactation, and menopause. Specific diagnostic interviews (including
criteria for other disorders) were not part of BIG; however, as there is no evidence for clinically relevant psychiatric disorders in our participants, we refer to them as ‘healthy’. BIG was
approved by the regional medical ethics committee. All participants gave written informed
consent and were financially compensated for participation.
Genotyping
Genetic analyses were performed as described in earlier publications from BIG (Bralten et al,
2011). DNA was extracted from saliva using Oragene kits (DNA Genotek, Kanata, Canada).
Genotypes for NR3C2 were available from genotyping using AffymetrixGeneChip SNP 6.0
(Affymetrix Inc., Santa Clara, California) arrays. Quality control steps were equal to earlier
publications on BIG (Bralten et al, 2011; Stein et al, 2012) except for the minor allele frequency,
which was lower for those studies (0.01). We used a frequency cutoff of 0.05 as we are using
a smaller subset of BIG. The call rate threshold for the arrays was set at 90% following recommendations of the manufacturer. SNPs were excluded if the call rate was <95% or they failed
the Hardy-Weinberg equilibrium test (p<10-6 genome-wide). 346 SNPs within NR3C2 and an
additional 10-kb window on both sides (capturing regulatory sequences) were genotyped, of
which 309 survived the quality checks. Participants were excluded if the call rate per individual
was <95%.
Self-Referent Encoding/Evaluation Task (SRET)
Memory bias was assessed using a web-based version of the SRET (Hammen & Zupan, 1984),
programmed in Flash (details see Gerritsen et al, 2011). During encoding, twelve negative and
twelve positive trait adjectives (e.g. the Dutch words for ‘optimistic’ or ‘unhappy’) were presented one by one on a screen, in the same order for each participant. Participants were instructed
to remember these words for a subsequent memory test, and asked to indicate whether each
word was self-referent or not by pressing one of two buttons to ensure self-referent encoding.
Following a distraction task of 2.5 minutes (mental arithmetic task), participants had 3 minutes
to freely recall and type in the studied adjectives they could remember. The two first and last
adjectives on the encoding list were filler items and excluded from analyses to avoid primacy
and recency effects, leaving 20 items in the analyses. Spelling errors were permitted since all
responses that were not absolutely correct were checked manually. Three outcome variables
were calculated: amount of adjectives recalled (overall memory performance), proportion of
31
2
Chapter 2
self-referent negative recall (negative memory bias) and proportion of self-referent positive
recall (positive memory bias). The latter two variables were calculated by dividing the number
of adjectives endorsed as self-referent and recalled in a given valence category by the total
number of self-endorsed adjectives. These variables have the advantage of controlling for
differences in overall rates of endorsement (Symons & Johnson, 1997). As especially negative memory bias measures had a skewed distribution, both memory bias measures were
log transformed which resulted in reduced kurtosis and skewness (negative memory bias:
skewness before / after transformation: 2.09 / 1.89, kurtosis: 4.34 / 3.14; positive memory bias:
0.05 / -0.25 and -0.83 / -0.98, respectively).
Life adversity
Life adversity was assessed retrospectively using an adapted version of the List of Threatening
Life events developed by Brugha (1985) in the same web-based test battery as the SRET (Gerritsen et al, 2011; van Oostrom et al, 2012). This inventory entailed life events which are likely to
occur frequently and pose significant long-term threat. Participants had to indicate whether
they had experienced a set of 21 specified life events (e.g. parental loss or sexual abuse) before
age 16, after age 16 and within the last year. Life adversity was calculated by summing all
experienced events over all categories. As this variable was not normally distributed, we log
transformed the scores. For further analyses we stratified our sample according to the number
of adverse events using a median split into high life adversity (above the median of four events,
n=221, 66.5% females) and low life adversity (maximally four events, n=262, 58.8% females).
Gene-wide analysis
Association of variation in NR3C2 with overall memory performance and memory bias was assessed with the set-based test with an additive genetic model in PLINK software, version 1.07
(Purcell et al., 2007). PLINK calculates a mean SNP statistic for each SNP set from the single SNP
statistics of a specified maximum amount of independent SNPs (50) with a p-value smaller
than 0.05 (Deelen et al, 2011). If the linkage disequilibrium (LD, calculated as r2) between SNPs
was higher than 0.8 (i.e. they were not independent), the SNP with the lowest p-value in
the single SNP statistics was chosen per high-LD block. The analysis was then repeated with
10.000 simulated SNP sets for which the phenotype values were permuted over individuals
using the --mperm command in PLINK. Age and sex were included as covariates.
To test for a gene-by-life adversity interaction, we first pruned our data-set using the --indep
command in PLINK as suggested in the manual. SNPs within a 50 SNP window that had r2>0.5
(corresponding to a variance inflation factor [VIF] of two) with all other SNPs in the window
were removed. This step removed 276 SNPs, leaving 33 in the analysis. Thereafter, we tested
for a gene-by-life adversity interaction using the set-based analysis and adding life adversity
(group membership) and the interaction term to the model. As it is not possible to obtain a
whole-set p-value for this interaction, we calculated the likelihood of at least the amount of
32
nominally significant SNPs given the null hypothesis, i.e. the probability to be significant per
SNP being 0.05 using a binominal test. We repeated the interaction test using the continuous
variable ‘log transformed sum of life events’ instead of ‘group membership’, since the median
split on life events could create arbitrary differences between scores. To further specify the interaction results, we repeated the original set-based testing in the low and high life adversity
groups separately.
Post-hoc SNP based analysis
Following the gene-wide statistical approach, we examined the most promising SNP that
showed significant associations with negative memory bias, using a general linear model
(GLM) with age and sex as covariates. Again, additive genetic models were used. We tested
for an interaction with life adversity using life adversity group membership as additional
independent variable and again repeated the analysis using the ‘log transformed sum of life
events’.
Haplotype analysis
Several haplotypes in NR3C2 are known to be functional (e.g. DeRijk et al, 2011; Klok et al,
2011b; van Leeuwen et al, 2011). Thus, we also investigated whether common haplotypes
were associated with negative memory bias: haplotype block 1: rs5522/rs2070951 in exon
2 and extending into the promoter region (Klok et al, 2011b) and haplotype block 2: rs5534/
rs2871 in exon 9 of NR3C2 (DeRijk et al, 2011). Individual haplotypes were reconstructed
using the haplo.stats package (version 1.4.4) within R (version 2.12.0). Haplotypes were successfully reconstructed for all participants (posterior probabilities > 0.9). Separate GLMs were
conducted to test the effects of having zero, one or two copies of each haplotype on negative
memory bias.
Statistical testing was done using SPSS 19 (Armonk, IBM Corp.). The significance level was
set at 0.05 for all analyses and all tests were two-sided.
Results
Gene-wide analysis
Table 2.1 displays general characteristics of our sample. T-tests and Mann-Whitney U-tests
were conducted where appropriate to examine sex differences in age, positive memory bias,
negative memory bias, and number of life events. No differences between sexes emerged
except for female participants having a stronger positive memory bias than males (U=22979,
p=0.003). As expected in a healthy population, participants endorsed on average 7 positive
items and 1 negative item (ranges 2-10 and 0-10, respectively). They recalled on average 41%
[SD 26.9] of the endorsed positive items, and 23% [SD 40.3] of the endorsed negative items.
Across both valence groups, participants recalled significantly more items which they rated as
33
2
Chapter 2
Table 2.1. General characteristics of the study sample for both life adversity groups and sexes, including age,
memory bias, and life events.
low life adversity
male (N=108)
high life adversity
female (N=154)
male (N=74)
mean (SD) min max mean (SD) min max
female (N=147)
mean (SD) min max mean (SD) min max
age
22.0 (2.7)
18
33
21.7 (2.6)
18
34
22.8 (3.5)
18
35
23.1 (3.6)
18
33
negative bias
.05 (.09)
.00
.40
.04 (.07)
.00
.44
.04 (.07)
.00
.40
.06 (.10)
0
.45
positive bias
.34 (.25)
.00 1.00
.40 (.27)
.00
1.00
.32 (.22)
.00
.90
.39 (.25)
.00
.89
life events
2.7 (1.1)
2.8 (0.9)
0
4
7.9 (3.0)
5
19
7.3 (2.8)
5
23
0
4
self-descriptive: They successfully retrieved 42% of the items which were endorsed and 35%
of the items which were not endorsed (p<0.001).
Analyzing the gene-wide association using set-based testing showed an association
for negative memory bias (p=0.017) with 27 SNPs having p-values<0.05 of which six were
independent (see Table S1 for more information). There was no association with positive
memory bias or overall memory performance (both p>0.5). To address the potential concern
that our sample size might have been too small to detect linear effects using a minor allele
frequency of 0.05, we repeated the analysis with frequency cutoffs of 0.10 and 0.15, which did
not change the results.
Testing for an interaction with life adversity (group membership) resulted in six out of 33
SNPs showing a significant interaction (see Table S2). A binominal test revealed that the likelihood for at least six SNPs being significant given the null hypothesis, is p=0.005. We repeated
the analysis using the log transformed sum of life events as dependent variable revealing a
similar result (five SNPs significant, p=0.023). Analyzing the high and low life adversity group
separately resulted in a significant association in the high life adversity group (p=0.0004, 47
SNPs significant, 14 independent, Table S1) but not in the low life adversity group (p>0.8, four
SNPs significant, three independent).
Post-hoc SNP-based analysis
Figure 2.1 displays SNP-by-SNP associations between NR3C2 and negative memory bias. Two
significant SNPs, rs5534 and rs2871, were located in coding regions.
As rs5534 (frequency of the minor allele A: 0.442) has been shown to be functional (Nossent et al, 2011), we focused on this SNP as a promising marker for negative memory bias.
In our sample, 166 participants were homozygous for the G allele, 226 participants were
heterozygous, and 91 participants were homozygous for the A allele (Table 2.2). rs5534 was
associated with negative memory bias (F=4.526, df=2, p=0.011) and post-hoc tests showed
that participants with the AA genotype had an almost 100% stronger negative memory bias
34
3.5
3
-log10(p-value)
2.5
2
1.5
2
1
0.5
0
149210964 149260964 149310964 149360964 149410964 149460964 149510964 149560964
SNP basepair position
intronic region gene ARHGAP10
near gene region
intronic region
UTR
exonic region
Figure 2.1. Gene-wide and SNP-by-SNP associations between NR3C2 gene variants and negative memory
bias. Note: Gene-wide significance was observed using PLINK. SNP: single nucleotide polymorphism, UTR: untranslated region. SNPs which were used to calculate the gene-wide p-value are indicated by open diamonds,
rs5534 is shown as gray triangle, other SNPs are depicted as black diamonds. SNP function is indicated by gray
shaded bars according to http://www.scandb.org/.
Table 2.2. General characteristics of the rs5534 genotype groups.
genotype
GG (N=166)
AG (N=226)
AA (N=91)
mean (SD)
min
max
mean (SD)
min
max
mean (SD)
min
max
age
22.8 (3.4)
18
35
22.0 (2.8)
18
33
22.5 (3.5)
18
34
negative bias
.04 (.07)
0
.44
.04 (.08)
.0
.45
.07 (0.11)
.0
.40
positive bias
.38 (.26)
0
1
.38 (.27)
0
1
.34 (.24)
0
1
live events
4.7 (2.7)
0
17
5.0 (3.3)
0
23
5.3 (3.4)
1
16
than both AG and GG (p=0.018 and p=0.003, respectively; Figure 2.2) with no difference between the latter two groups (p>0.3). This SNP was not associated with positive memory bias
or memory performance (both p>0.5).
We found a significant rs5534-by-life adversity (group membership) interaction on negative memory bias (F=9.838, df=2, p<0.0001). Using the log transformed sum of life events as
the independent variable did not change the result (F=2.26, df=26, p=0.0005). Post-hoc
tests indicated that the genetic groups did not differ in negative memory bias if participants
had low life adversity (all p>0.15, Figure 2.3). However, within the high life adversity group,
participants homozygous for the A allele of rs5534 had a stronger negative memory bias
than both other groups (both p<0.0001), with no difference between the latter two groups
(p>0.5). Furthermore, participants with the AA genotype of rs5534 and high life adversity
35
Mean (1±SEM) Negative Memory Bias (log transformed)
Chapter 2
0.04
0.03
0.02
0.01
0.00
GG
N=166
AG
(N=226)
AA
(N=91)
rs5534 groups
Mean Negative Memory Bias (±1 SEM), log-transformed
Figure 2.2. Comparison of negative memory bias between participants with genetic differences in rs5534.
** p<0.01, * p<0.05.
0.06
Low life adversity group
High life adversity group
0.05
0.04
0.03
0.02
0.01
0.00
(N=91)
(N=75)
GG
(N=124) (N=102)
AG
(N=47)
(N=44)
AA
rs5534 groups
Figure 2.3. Interaction of rs5534 and life adversity on negative memory bias. *** p<0.001, ** p<0.01.
36
had a stronger negative memory bias than participants of the same genotype but low life
adversity (p=0.0002). Within the other genetic groups, negative memory bias did not differ
between life adversity groups (both p>0.7).
Haplotype Analysis
Three haplotypes were observed in block 1 (rs2070951/rs5522), with frequencies as reported
previously (DeRijk et al, 2011): haplotype 1 (G-A) was most common (52%), followed by haplotype 2 (C-A, 37%) and haplotype 3 (C-G, 11%). No haplotype was related to negative memory
bias (all p>0.6).
Four haplotypes were found in block 2 (rs5534/2871), with similar frequencies as reported
previously (DeRijk et al, 2011): haplotype 1 (A-G) was most present (57%), followed by haplotype 2 (G-A, 31%), haplotype 3 (G-G, 11%), and haplotype 4 (0.4%). As haplotype 4 had such a
low frequency, we limited our analyses to haplotype 1, 2, and 3.
Haplotype 1 was significantly associated with negative memory bias (F=4.218, df=2,
p=0.015). Post-hoc comparisons showed that participants with no copy of haplotype 1 had
a stronger negative memory bias than participants with one or two copies (p=0.029 and
p=0.004, respectively). Furthermore, there was a trend for an association between haplotype 2
and negative memory bias (F=2.961, df=2, p=0.053). Post-hoc comparisons showed that participants with no copy of haplotype 2 had a smaller negative memory bias than participants
with two copies of haplotype 2 (p=0.029). Finally, both haplotype 1 and haplotype 2 in block
1 showed an interaction with life adversity (group membership; F=10.435, df=2, p<0.0001
and F=6.828, df=2, p=0.001, respectively). Post-hoc tests revealed that in the low life adversity
group, participants with different haplotype 1 or haplotype 2 frequencies did not differ in
negative memory bias (all p>0.1). However, in the high life adversity group, participants with
no copy of haplotype 1 had a stronger negative memory bias than participants with one
or two copies of this haplotype (both p<0.0001). Additionally participants with two copies
of haplotype 2 in the high life adversity group had a stronger negative memory bias than
participants with either one or no copy of haplotype 2 (p=0.003 and p=0.001, respectively).
No other comparison in block 2 was significant (all p>0.1).
Discussion
This study provides initial evidence that variation in NR3C2 is related to negative memory bias
in healthy adults, especially given a history of life adversity. As negative memory bias is an
endophenotype for MDD, our finding supports the notion that common NR3C2 variation is a
risk factor for developing mood disorders (DeRijk & de Kloet, 2008), especially in combination
with life adversity.
Additionally, we found a functional variation as a potential marker for heightened MDD
risk, rs5534, which was associated to negative memory bias and showed an interaction with
37
2
Chapter 2
life adversity. This coding SNP in exon 9 of NR3C2 (van Leeuwen, 2010) was associated to a
reduced efficiency of an upstream binding site for microRNA hsa-miR-383 in two human cell
lines (Nossent et al, 2011). In vitro, the A allele of rs5534 led to increased microRNA-induced
repression of MR expression (Nossent et al, 2011). This SNP could thus be a marker for impaired
regulation of MR expression which might in turn result in dysfunctional changes of cortisol
regulation after stressful events. Furthermore, impaired regulation of MRs could influence excitability and structural integrity of amygdala and hippocampus (Gass et al, 2000; Groeneweg
et al, 2011) whose pathophysiology has been associated with mood disorders (Bremner et al,
2000; Frodl et al, 2002) and negative memory bias (Gerritsen et al, 2011).
The significant post-hoc main effect for rs5534 itself was expected given the significant
finding in the gene-wide analysis, and thus its p-value is putatively inflated (Kriegeskorte et
al, 2009). However, we want to emphasize that rs5534 was chosen as a promising marker,
because of an earlier report of its functionality (Nossent et al, 2011). It forms a haplotype with
the second exon SNP that was associated with negative memory bias (rs2871) and this haplotype was associated with negative memory bias. This association seems counterintuitive
concerning an earlier finding where two copies of haplotype 2 were associated with lower
neuroticism scores in a sample of 100 patients with MDD or anxiety disorders (DeRijk et al,
2011). However, this same study found no association of haplotype 2 and neuroticism in
healthy controls. More research is certainly needed to understand the role of the functional
haplotype blocks in NR3C2 and their relation to psychopathology.
Earlier publications on variations in NR3C2 have found two other SNPs, rs5522 and
rs2070951, and their haplotypes to be associated with reward learning, HPA axis functioning,
hopelessness, increased depressive symptoms in individuals above age 85 and pre-menopausal women (see e.g. DeRijk et al, 2011; Klok et al, 2011b; Kuningas et al, 2007). However,
we did not find any associations of these SNPs or haplotypes in our study in line with several
previous publications (Bouma et al, 2011; Supriyanto et al, 2011; Velders et al, 2011).
We found a gene-by-life adversity interaction such that associations between genetic
variants and negative memory bias were highly pronounced in individuals with high life
adversity, who, in general, are more likely to develop mood disorders (Kendler et al, 1999). So
far NR3C2 has not directly been implicated in the risk for MDD in large scale GWAS studies
(Ripke et al, 2013). This missing association may have many reasons, one of which might be
the role life adversity seems to play in linking risk genes to clinical outcomes. Our finding thus
supports the importance of gene-by-environment interactions in the search for risk factors for
psychiatric disorders such as MDD.
As an emotionally negative cognitive bias might also affect recall of negative events from
autobiographical memory, causality is difficult to assign. However, the questionnaire used to
assess life events limits the effects of recall biases, because it asks for the occurrence of several
factual and specific events, like divorce or death of parents, that require a clear yes or no
answer (Brugha et al, 1985). Certainly, depressed or sad individuals recall more negative events
38
from autobiographical memory, but these negative recalled events tend to be overgeneralized and not factual or detailed in time and place (King et al., 2010). It is therefore very unlikely
that negative memory bias will affect recall of the kind of factual information asked for in our
study. However, only prospective longitudinal studies will allow drawing definite conclusions
regarding causality.
Several reports have also related variations of NR3C2 to pseudohypoaldosteronism type
1 and hypertension (Abercrombie & Jacobs, 1987; Geller et al, 1998; Riepe et al, 2006; Riepe
et al, 2004; van Leeuwen, 2010). Unfortunately, we do not have data on blood pressure or
aldosterone levels available for our cohort. More studies are certainly needed to clarify their
relationships with cognition.
It could be criticized that life events and memory bias were assessed using a web-based
test battery. This approach allowed the participants to determine themselves when and
where to do the test and offers less organizational and time constraints, but it gave less
control over the experimental setting, for example time of day. However, more and more
studies are appearing using web-based tests and surveys. For example, a recent study using
cognitive and perceptual tasks demonstrated that data acquired using web-based testing is
comparable in reliability and validity to data from laboratory tests, even if the participants are
unsupervised (Germine et al, 2012). Furthermore, we mitigated potential variance by the focus
on relative measures (memory bias) instead of absolute measures (memory performance),
because we can assume that recall of both negative and positive words is affected in the
same way. Finally, it is unlikely that noise potentially introduced by the web-based approach
is affecting a certain genotype more than another. Thus, the web-based approach allowed
us to test a large sample of subjects for self-referenced negative memory bias with putatively
sufficient reliability and validity.
It could also be argued that our sample was rather young and might thus have experienced
few life events. However, a comparison with other studies using the original version of the List
of Threatening Life Events (Brugha et al, 1985) contradicts this argument: A recent study in over
1000 Dutch participants (mean age 53.5 years) found not only satisfactory construct validity
and stability over two years, but also a median lifetime event score of 5 and a maximum of 21,
which is very similar to our study (Rosmalen et al, 2012).
A particular strength of our study was the gene-wide statistical approach, which takes into
account all observed variation in NR3C2 simultaneously. Especially for complex phenotypes
that might depend on several genetic factors and where possible allelic heterogeneity makes
it difficult to find associations, this approach can increase statistical power (Bralten et al, 2011;
Deelen et al, 2011; Schwender et al, 2011). Another strong point of this study is the fact that it
was conducted in a sample of healthy participants so that the associations are not influenced
by psychopathology, chronic disease processes, or therapy. Rather, we report a relationship
between genetic variation with an established cognitive endophenotype for MDD, which
might predispose individuals to develop MDD later in life.
39
2
Chapter 2
To the best of our knowledge, this is the first study that investigated the effect of common
variants in NR3C2 on negative memory bias using a gene-wide approach. Variation in NR3C2
was associated with this cognitive endophenotype, especially in participants with high life
adversity. Common variations in NR3C2 - which may compromise proper functioning of the
receptor - could thus be a risk factor for developing mood disorders, especially in combination
with life adversity. Our results extend earlier findings that MRs might be critically involved in
the pathophysiology of mood disorders, and may thereby provide new avenues for pharmacological interventions.
This study was supported by grants from the Netherlands Organization for Scientific Research
(NWO) awarded to G.F.: 918.66.613 and 433.09.251. Supplementary tables are available via
ScienceDirect: http://www.sciencedirect.com/science/article/pii/S0306453013004265
40
Chapter 3
Blocking the mineralocorticoid receptor
in humans prevents the stress-induced
enhancement of centromedial amygdala
connectivity with the dorsal striatum
Theworkinthischapterwaspreviouslypublishedas:S.Vogel,F.Klumpers,H.J.Krugers,
Z.Fang,K.T.Oplaat,M.S.Oitzl,M.Joëls,G.Fernández(2015).
Neuropsychopharmacology 40,947-956.
Chapter 3
Abstract
Two research lines argue for rapid stress-induced reallocations of neural network activity
involving the amygdala. One focuses on the role of norepinephrine in mediating the shift
towards the salience network and improving vigilance processing, while the other focuses on
the role of cortisol in enhancing automatic, habitual responses. It has been suggested that the
mineralocorticoid receptor (MR) is critical in shifting towards habitual responses, which are
supported by the dorsal striatum. However, until now it remained unclear whether these two
reallocations of neural recourses might be part of the same phenomenon and develop immediately after stress onset. We combined methods used in both approaches and hypothesized
specifically that stress would lead to rapidly enhanced involvement of the striatum as assessed
by amygala-striatal connectivity. Furthermore, we tested the hypothesis that this shift depends
on cortisol interacting with the MR, by employing a randomized, placebo-controlled, fullfactorial, between-subjects design with the factors stress and MR-blockade (spironolactone).
We investigated 101 young healthy men using functional MRI after stress induction, which
led to increased negative mood, heart rate, and cortisol levels. We confirmed our hypothesis
by revealing a stress-by-MR-blockade interaction on the functional connectivity between the
centro-medial amygdala (CMA) and the dorsal striatum. Stress rapidly enhanced CMA-striatal
connectivity and this effect was correlated with the stress-induced cortisol response, but
required MR-availability. This finding might suggest that the stress-induced shift described
by distinct research lines might capture different aspects of the same phenomenon, i.e. a
reallocation of neural resources coordinated by both norepinephrine and cortisol.
42
Blocking the mineralocorticoid receptor in humans
Introduction
Encountering acute threat appears to shift neural network balance (Hermans et al, 2011). This
reallocation of neural resources alters cognition and behavior, preferring sensory processing
and fast responding over elaborate, flexible behavior. The amygdala is crucial in detecting
threat and activates the locus cœruleus (LC), the major central source of norepinephrine
(NE, Aston-Jones, 1985; Sara & Bouret, 2012; Valentino & Van Bockstaele, 2008). NE leads to an
upregulation of the salience network and downregulation of the executive control network,
improving vigilance at the cost of elaborative cognition (Hermans et al, 2014b). Activation of
this LC-NE system is supposed to enhance chances for survival by improving threat detection
and reducing elaboration to enable fast responding.
Along intriguingly similar lines, researchers from the memory field have argued for a stressinduced shift in neural processing. This shift, again orchestrated by the amygdala, favors
automatic, habitual responding mediated by the dorsal striatum at the expense of controlled,
flexible responding mediated by hippocampus and prefrontal cortex (PFC; Kim et al, 2001;
Packard & Teather, 1998; Schwabe & Wolf, 2013). This process is likewise supposed to promote
survival by relying on well-learned reflexive behavior in the face of acute threat. The mechanistic focus of this research line is on the hypothalamus-pituitary-adrenal (HPA) axis. There is
initial evidence that the mineralocorticoid receptor (MR), one receptor for cortisol, is crucial for
this shift in rodents (Schwabe et al, 2010a) and humans (Schwabe et al, 2013c).
Despite the striking similarities of these two reallocations of neural resources involving the
amygdala, the research lines remained largely separate. According to their respective focus
on either the fast-acting NE or the somewhat slower-acting cortisol, researchers used different stress-induction procedures to either increase arousal and NE levels (e.g. violent movies,
Hermans et al, 2011) or cortisol levels (e.g. social evaluation or cold pressure tasks, Schwabe
et al, 2007). Moreover, these lines of research investigated stress-effects in different time
domains. Research on NE focused on the immediate effects of stress to ensure high NE levels,
but given the early time frame this leads to relatively low cortisol levels. Research on cortisol
effects, in contrast, usually took place at least twenty minutes after stress induction to ensure
high cortisol levels. Finally, different tasks were used targeting either vigilance processing or
memory formation.
Until now, it remained unclear whether these two stress-induced phenomena might be related reallocations of neural resources in the face of threat. With this background in mind, we
aimed at better understanding fast effects of cortisol and designed an experiment crossing
the borders between the two research lines described. We investigated whether the socially
evaluated cold pressure test (Schwabe et al, 2008b) leads to a rapid reallocation of neural
resources to areas supporting habitual responses, the dorsal striatum, in a task probing vigilance processing. Furthermore, if such a shift would be present, we expected it to depend on
MR-availability. We focused on rapid glucocorticoid effects given recent findings suggesting
43
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Chapter 3
that the fast non-genomic effects, mediated primarily by the MR, lead to increased amygdala
excitability and facilitation of adaptive behavior (Karst et al, 2010). Finally, we hypothesized
that the amygdala would drive this reallocation of resources. However, the amygdala is not
a homogenous structure, but consists of subnuclei with different functions and connectivity
patterns (McDonald, 1998). Whereas the basolateral amygdala complex (BLA) is assumed to
be the input structure and stores cue-threat associations (Johansen et al, 2011), the centromedial amygdala (CMA) constitutes the output structure which is connected, among others, to the striatum, mediating habitual responses (Fudge et al, 2002; Han et al, 1997). This
model was supported in humans using functional connectivity analyses on resting state data,
demonstrating that the BLA is functionally connected to cortical regions, whereas the CMA
is connected to more subcortical regions, including the dorsal striatum (Roy et al, 2009). We
thus assumed that the stress-induced reallocation of neural resources to the striatum would
be driven primarily by the CMA rather than the BLA. Therefore, we expected stronger connectivity between CMA and striatum during stress, depending on MR-availability. To test this,
we employed a full-factorial design investigating the effects of acute stress and MR-blockade
(spironolactone) on vigilance processing, task-related brain activity, and amygdala sub-region
specific connectivity changes.
Methods
The study was approved by the local ethical committee (NL37819.091.11), registered in the
Dutch Trial Registry (3595), and the European Clinical Trials Database (2011-003493-85).
Participants
Healthy, right-handed male volunteers (N=101) with a body mass index within normal range
(18.5 ≤ BMI ≤ 30) were screened for the following exclusion criteria: psychiatric, neurological,
cardiovascular or endocrine disease, irregular sleep/wake rhythm, non-admissibility to the MRI
scanner, smoking (>5 cigarettes weekly), alcohol consumption (>21 beverages weekly), use of
recreational drugs (>weekly), psychotropic medication, and hepatic, cardiovascular, or renal
impairments. Athletes were excluded because of a possible positive doping test result after
spironolactone intake. All participants had normal or corrected-to-normal vision and normal
hearing. They had to refrain from any medication other than paracetamol for acute pain and
recreational drugs for 72h, alcohol for 24h, and coffee for 3h before testing.
All participants gave written informed consent and were financially compensated. Two
participants had to be excluded due to panic attacks during scanning and one participant
did not comply with study instructions (participation in another drug study). This resulted in a
final number of 98 participants (mean age 21.9 years [SD=2.9], Table 3.1).
44
Table 3.1: General characteristics of the study sample.
measure
MR-available
control
N
Age
MR-blocked
stress
control
average
stress
24
24
26
24
21.6 (2.2)
21.9 (4.0)
22.5 (2.8)
21.5 (2.4)
21.9 (2.9)
Body-mass-index
23.4 (2.4)
22.5 (1.9)
22.7 (2.4)
22.3 (2.5)
22.7 (2.3)
Trait anxiety
28.4 (5.5)
29.0 (5.1)
28.5 (6.1)
29.5 (5.2)
29.0 (5.8)
180 (1)
135 (59)***
180 (2)
155 (51)*
163 (43)
Time in water in s
Notes: Trait anxiety was assessed using the Dutch translation of the Spielberger State Trait anxiety inventory (Spielberger et
al, 1983). Trait anxiety and body-mass-index were assessed during screening. Values represent mean (standard deviation).
***p<0.001, *p<0.05 compared to control in the same drug group.
Study Design
We used a placebo-controlled, full-factorial between-subjects design with the factors stress
(stress vs. control) and MR-blockade (400mg spironolactone vs. placebo). Participants were
randomly assigned to one of the four experimental groups. The factor MR-blockade was
manipulated in a double-blind fashion. However, the subjects were informed about their
assignment in terms of the stress factor before scanning (see below).
General procedure
Drug administration and adaptation phase
All testing took place in the afternoon. After baseline cortisol, subjective mood, and vital signs
(blood pressure, heart rate) measurements, participants were administered either placebo
or 400mg spironolactone (tablets) orally in four capsules (Teva Pharmachemie, Haarlem, the
Netherlands; half-life in plasma approximately 1.5h). This dosage is in accordance with other
studies investigating the MR in humans (Cornelisse et al, 2011; Rimmele et al, 2013). A waiting
period of 80 minutes (min) followed ensuring adaptation to the lab environment and absorption of the drug. Participants rested, cortisol and vital signs were measured every 30 min.
Experimental phase
Participants were taken to the MRI room, and those assigned to the stress condition were
informed that they would do the ice water task. They were exposed to an MRI scanner compatible version of the socially evaluated cold pressure task (SECPT, Schwabe et al, 2008b) or a
non-stressful control procedure (control group). Immediately afterwards, a saliva sample and
mood assessment were obtained before participants were instructed about and performed
an emotional face-matching task assessing vigilance processing in the MRI scanner. The delay
between stress onset and task onset was on average 9 min, 56 s (±110 s [SD]). Two other
45
3
Chapter 3
tasks followed, which will be reported elsewhere. Participants were debriefed about the stress
induction procedure and could leave after a final assessment of well-being and vital signs.
Stress induction
The SECPT is an established method to induce stress by asking participants to immerse one
hand into ice water while being socially evaluated (Schwabe et al, 2008b). We adapted it to
an MRI compatible version to avoid changing the context between stress induction and task,
and to minimize the delay in-between. Participants were placed in a supine position on the
scanner bench, immersed the right foot into ice water (0-2°C) up to and including the ankle,
and held it there as long as possible. During foot immersion, participants looked into a camera
while being closely observed by two experimenters in white laboratory coats (at least one
female) acting in a neutral and non-supportive way. In the control group, warm water was
used (35-37°C), there was no camera, and the experimenter was friendly and casually dressed.
If participants did not remove their foot earlier, the experimenter stopped the task after 3
min. The stress group underwent a difficult mental arithmetic test (counting aloud backwards
from 2059 in steps of 17) approximately 40 min after the initial stress induction to maintain
heightened stress levels for subsequent tasks. Participants in the control condition did a
simple control task (counting forwards in steps of 10).
Stress measurements
Negative mood, cortisol levels, and vital signs were measured repeatedly throughout the experiment. For negative mood, the Positive and Negative Affect Scale (Watson et al, 1988) was
administered either on paper or presented on the screen in the MRI scanner, programmed in
Presentation® (Neurobehavioral Systems, Inc.). Sum scores for negative mood were calculated
per time point. Vital signs were measured using an automatic blood pressure monitor with
arm cuff (Intellisense®, OMRON, the Netherlands) outside the MRI scanner. Heart rate during
scanning was acquired using the heart rate device of the MRI scanner, peak-scored using
in-house software and averaged over four time bins of 1min to cover the task. To measure cortisol levels, saliva samples were taken using Salivettes® (Sarstedt, Germany). For each sample,
participants were asked to chew the cotton swab gently for 1 min. The samples were stored
at -24°C until they were analyzed by the Dresden LabService (Germany). After thawing, the
samples were centrifuged and analyzed using a commercially available chemiluminescence
immunoassay with high sensitivity (IBL Inc.). The cortisol levels were not normally distributed
and normalized using log-transformation.
Emotional Face-Matching Task
To assess vigilance processing, we employed a commonly used emotional face-matching task
(Hariri et al, 2002; van Wingen et al, 2007) which contrasts an emotional condition with a visuomotor control condition, alternating in a blocked fashion. Each block lasted 30s and consisted
46
of six 5s-trials. Each trial comprised three simultaneously presented stimuli, either emotional
faces (http://www.macbrain.org) or ellipses made of scrambled faces. A cue stimulus was
presented above a target and a distractor stimulus. The participant had to indicate which of
the latter two matched the cue by pressing one of two buttons. In the emotion condition,
participants had to identify the target face which displayed the same emotional expression
as the cue face (angry or fearful). In the visuomotor condition, the target was the ellipse with
the same geometrical orientation as the cue (horizontal or vertical). Two emotion blocks were
interleaved with three visuomotor blocks.
Magnetic Resonance Imaging
MRI measurements were acquired using a 1.5T Avanto Scanner (Siemens, Germany) equipped
with a 32-channel head coil. Blood-oxygenation-level-dependent (BOLD) T2*-weighted
multi-echo GRAPPA images (Poser et al, 2006) were obtained with the following parameters:
Repetition Time (TR)=2.14s, Echo Times (TEs)=9/21/33/44/56ms, 34 transversal slices, ascending acquisition, distance factor 17%, effective voxel size=3.3x3.3x3.0mm, Field of View
(FOV)=212mm. We used this multiecho sequence for its improved BOLD sensitivity and lower
susceptibility for artifacts, especially for ventral regions (Poser et al, 2006). Additionally, we
acquired high resolution T1-weighted anatomical image (TR=2.73s, TE=2.95ms, 176 sagittal
slices, FOV=256mm, voxel size=1.0x1.0x1.0mm).
Behavioral and physiological analysis
All behavioral and physiological analyses were performed in SPSS 19 (Armonk, IBM Corp.).
The alpha level was set to 0.05 for all analyses (two-tailed) and Greenhouse-Geisser correction
was applied when necessary. Missing cortisol data (seven non-adjacent measurements in six
participants) were interpolated by averaging across the two neighboring measurements. For
negative mood, missing items (six items in five participants) were replaced by the individual
mean score per time point. In line with previous work (Muehlhan et al, 2011), participants
naive to the MRI scanner environment showed a stress response to the scanning procedure
itself as indicated by higher heart rate and cortisol levels than non-naive participants (both
p<0.05). Given that our experimental groups had different percentages of naive participants
[58% stress/MR-blocked, 50% stress/MR-available, 62% control/MR-blocked, 25% control/
MR-available], we included scanner naivety as covariate of no interest in all of our analyses,
including fMRI analyses.
Negative mood, cortisol, heart rate, blood pressure
To test for successful adaptation to the laboratory environment, the scores of these variables
during adaptation were entered separately into repeated measures ANOVAs (rmANOVAs)
with the within-subjects factor time and the between-subjects factors stress (yes, no), and
MR-blockade (available, blocked). For the experiment phase, the scores were baseline cor47
3
Chapter 3
rected to the last measurement of the adaptation phase (-25min) and entered into rmANOVAs
with the within-subjects factor time and the between-subjects factors as described above.
Performance in the emotional face-matching task
Reaction times and percentage hits were calculated per task condition (emotion, control) and
entered separately in rmANOVAs with the within-subjects factor condition and the betweensubjects factors stress and MR-blockade.
fMRI analysis
Two participants (stress/MR-blocked, control/MR-blocked) were excluded for fMRI analyses
due to technical failure and excessive head motion (>3.3mm). Data were analyzed in SPM8
(Wellcome Trust Centre for Neuroimaging, London, UK) using general linear modeling. For spatial realignment, head motion was estimated on the first echo using least-squares estimation
and applied to the 5 echoes acquired for each excitation using 6 rigid-body transformation
parameters. The echo images per volume were then combined into a single volume using an
optimized echo weighting procedure (Poser et al, 2006). The functional images were coregistered to the structural image using rigid-body transformations. The T1-image was segmented
into cerebral spinal fluid (CSF), white matter (WM) and gray matter, and used to normalize
functional and structural scans to MNI space with the unified segmentation procedure. Finally,
the images were spatially smoothed using an 8mm FWHM Gaussian kernel.
To assess the effects of stress and MR-blockade on neural responsivity, the task conditions
(emotion, visuomotor) were modeled as 30s box-car regressors and button presses were
modeled as spikes, all convolved with the canonical HRF. Additionally, six realignment parameters were included. Contrast images subtracting the visuomotor condition from the emotion
condition were analyzed in a full-factorial design to test for group differences. For the explorative whole-brain analyses, we used a threshold of pFWE<0.05 (cluster-level). To employ small
volume correction (SVC) for our a priori regions of interest (ROIs), i.e. amygdala sub-regions,
we used an initial threshold of p<0.005, uncorrected, followed by FWE-correction (p<0.05)
for multiple comparisons within the ROIs as implemented in SPM. Although our hypothesis
primarily contrasted CMA and BLA, for completeness we also included the superficial amygdala (SFA) as ROI. Masks of the bilateral sub-regions were taken from the Anatomy Toolbox for
SPM (version 18, Institute of Neuroscience and Medicine, Jülich, Germany) which is based on
probabilistic cytoarchitectonic maps derived from ten post-mortem brains. The masks were
thresholded at 50% (Amunts et al, 2005) to include only voxels with at least 50% probability to
belong to each sub-region.
To investigate connectivity of amygdala sub-regions during the emotional face-matching
task, we extracted the first eigenvariate of the time-courses of the bilateral sub-regions using the
‘Physio/Psycho-Physiologic Interaction’ tool as implemented in SPM8. Subsequently, we added
each time-course separately as covariate of interest in addition to the first-level regressors. Cor48
relating this time series to activity in the rest of the brain provides information on regions that
show similar activation patterns and are therefore supposedly functionally connected. Global
signal fluctuations were accounted for by extracting signal from individually defined WM- and
CSF-masks, and adding these two regressors to the model. A full-factorial design was used to
investigate group differences in connectivity of the amygdala sub-regions. Based on our hypotheses, our ROI was the dorsal striatum (caudate nucleus and putamen). As these regions are
not included in the Anatomy Toolbox, masks were defined using the Automated Anatomical
Labeling (AAL) atlas (Tzourio-Mazoyer et al, 2002) as implemented in the Wake Forest University
PickAtlas (version 2.4). Again, for SVC, we used an initial threshold of p<0.005, uncorrected,
followed by FWE-correction (p<0.05). To control for the multiple testing problem inherent in
testing three seed regions, only results with pSVCcorr<0.017 will be considered significant.
Results
The experimental groups did not differ significantly in age, body-mass-index, or trait anxiety (Table 3.1). Participants in the stress condition kept their foot in the ice water for over 2
min on average, which was nevertheless shorter than participants in the control procedure
(F(1,93)=20.123, df=1, p<0.001). Importantly, there was no influence of MR-blockade on the time
in water.
Stress measures in the adaptation phase indicate adaptation to the laboratory
environment
Decreases throughout the adaptation phase in negative mood ratings, cortisol levels, heart
rate, and blood pressure indicate successful adaptation to the laboratory environment (all
main effects of time p<0.001). Furthermore, within drug groups there was no difference
between stress and control groups in any measure prior to stress induction (all p>0.1). In
line with a regulatory role of the MR on HPA axis activity, the MR-blocked groups had higher
cortisol levels 25 min before stress than the MR-available groups (t96=3.126, p=0.002).
Stress measures in the experiment phase indicate successful stress induction
Stress-related increases in negative mood, cortisol, and heart rate evidenced successful stress
induction in both drug groups (Figure 3.1).
Negative Mood
We found main effects of stress (F(1,91)=10.907, p=0.001) and time (F(2.4,218.4)=12.954, p<0.001),
and a time-by-stress interaction (F(2.4,218.4)=9.812, p<0.001). Post-hoc tests revealed stronger
negative mood in the stress groups at 5 min (trend level, p=0.096) and 45 min after stress
induction (p<0.001) compared to the control groups. No other significant group differences
were found. However, we found a trend for a time-by-stress-by-MR-blockade interaction
49
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Chapter 3
Cortisol (nmol/l), log transformed
0.4
time*stress p < 0.001
time*MR availability p = 0.001
0.3
0.2
0.1
0.0
-0.1
-0.2
10
-25
0
5
15
45
70
100
time
100
time
45
100
time
Control/MR blocked
Stress/MR blocked
stress p = 0.033
Heart rate
5
0
-5
-10
0
9 10 11 12
time*stress p < 0.001
time*stress*MR availability p = 0.057
4
2
-2
SECPT
0
-25
0
Control/MR available
instructions
Negative mood
6
-25
face
matching
task
5
Stress/MR available
other tasks
Figure 3.1. Stress measurements over the course of the experiment. Participants arrived and were randomly
assigned to one of four groups: control/ MR-available, stress/ MR-available, control/ MR-blocked, stress/ MRblocked. After drug or placebo ingestion and habituation to the laboratory environment, participants entered
the MRI room and underwent either a stress induction (SECPT) or a control procedure. This was immediately
followed by the emotional face-matching task during which fMRI data was acquired for subsequent connectivity analyses. The figure shows cortisol levels (top), heart rate (middle), and negative mood (bottom) for all
experimental groups over the course of the experiment. Time is indicated in minutes after stress induction.
All measurements were baseline corrected to the last time point during habituation (-25 minutes). Light gray
shaded areas indicate stress induction (or non stressful control procedure), intermediate gray shaded areas
indicate the time of MRI scanning, dark gray shaded area indicates the emotional face-matching task. SECPT
Socially evaluated cold pressure task. Mean values are depicted, error bars represent 1 SEM.
50
(F(2.4,218.4)=2.692, p=0.060). Post-hoc tests revealed a time-by-stress interaction only in the
MR-available groups (F(2.2,93.5)=10.269, p<0.001), but no significant stress-related increase in
negative mood over time in the MR-blocked groups.
Cortisol
One participant from the Control/MR-blocked group was removed from this analysis because of excessive cortisol levels (both cortisol at 100 min after stress and the increase of
cortisol from 70 to 100 min exceeded the mean + 3SD of the group). We found main effects
of stress (F(1,92)=13.004, p=0.001) and MR-blockade (F(1,92)=15.013, p<0.001), time-by-stress
(F(2.5,229.5)=8.927, p<0.001), and time-by-MR-blockade (F(2.5,229.5)=6.217, p=0.001) interactions, but
no time-by-stress-by-MR-blockade interaction. Post-hoc tests showed time-by-stress interactions in both drug groups (MR-available: F(2.5,114.4)=3.018, p=0.041; MR-blocked: F(2.3,105.6)=6.933,
p=0.001). In the MR-available groups, the stress group had higher cortisol levels than the
control group at 45 min (p=0.044) and at trend level 70 min after stress (p=0.085). Within the
MR-blocked groups, stressed individuals had higher cortisol levels from 15 min after stress
onwards (all p<0.05). Drug effects were found from 5 to 100 min, with the MR-available groups
having lower cortisol levels than the MR-blocked groups (all p<0.05) in line with a role of the
MR in setting the threshold for HPA axis activation. No other significant group differences were
found.
Heart rate and blood pressure
We found main effects of stress (F(1,88)=4.665, p=0.033) and time (F(2.9,252.3)=14.721, p<0.001) on
heart rate. Stressed participants had higher heart rates than control participants, indicative of
heightened NE levels. No significant main effect of or interaction with MR-blockade was present. Both systolic and diastolic blood pressure were unaffected by stress and MR-availability.
Behavioral measures are not affected by stress or MR-blockade
As expected, participants displayed almost perfect performance in the emotional facematching task with no significant difference between conditions (mean hit rate emotion:
91.9% [SD: 15.85], visuomotor: 92.1% [SD: 15.6]). In terms of reaction times, participants were
faster in matching orientations of ellipses than emotional expressions of faces (mean reaction
time emotion 1.89s [0.47], visuomotor 1.08s [0.33], F(1,93)=165.210, p<0.001). However, neither
stress nor MR-blockade significantly affected hit rate or reaction time.
Task-related brain activity is not influenced by stress or MR-blockade
Task-related brain activity
When comparing emotion versus visuomotor blocks, we found a bilateral cluster of activation
including the inferior frontal gyrus and the ventral visual stream reaching into the fusiform
gyrus, hippocampus, putamen, caudate, and amygdala (AMY, all pFWE<0.05, Figure 3.2, Table
51
3
Chapter 3
FFG
IFG
30
AMY
T
3
Figure 3.2. Brain areas activated by processing emotional faces versus ellipses plotted on the average anatomical scan of all participants. Bilateral activation was found in visual areas, the fusiform gyrus (FFG), amygdala
(AMY) and inferior frontal gyrus (IFG). All p<0.05, FWEcluster-level corrected.
S3.1). Importantly, this activation was highly significant in all three amygdala sub-regions (bilateral, all pFWE<0.05). The opposite contrast (control > emotion) revealed activations in regions
considered to be part of the default mode network (Fox & Raichle, 2007): medial PFC, posterior
cingulate and parietal cortex (pFWE<0.05). Neither the whole brain nor the ROI analyses (SVC)
of brain activity led to reliable main effects of stress or stress-by-MR-blockade interactions.
However, when extracting the parameter estimates for the contrast emotion over control
from critical ROIs of the salience network (bilateral insula, dorsal anterior cingulate cortex,
defined using the AAL atlas), we found that stress led to stronger activity in the bilateral insula
(F(1,91)=4.05, p=0.047). This effect was not influenced by MR-availability.
Stress enhances connectivity between CMA and dorsal striatum depending on the
availability of MRs
Brain connectivity
First, we investigated differential connectivity of both CMA and BLA (see Figure S3.1 for connectivity of the sub-regions separately). As illustrated by Figure 3.3, regions showing stronger
connectivity to the CMA than the BLA included frontal regions, the dorsal striatum, bilateral
insula, dorso-medial PFC, the ventral visual stream, the midbrain, and the supplementary
motor area (all pFWE<0.05). Regions showing stronger connectivity with the BLA included
the ventro-medial and ventro-lateral PFC and large parts of the temporal lobe. This analysis
confirms differential connectivity of amygdala sub-regions, and connectivity between CMA
and dorsal striatum. Interestingly, we found that the CMA was also coupled to parts of the
salience network.
52
SMA
insula
dorsal
striatum
temporal
lobe
visual
stream
dmPFC
5
3
T
CMA > BLA
midbrain
5
3
T
BLA > CMA
vmPFC
4
2
T
parameter estimates (a.u.)
Figure 3.3. Brain areas showing differential connectivity to either the centro-medial (CMA) or the basolateral amygdala (BLA) during an emotional face-matching task, plotted on the average anatomical scan of all
participants. SMA supplementary motor area, dmPFC dorso-medial prefrontal cortex, vmPFC ventro-medial
prefrontal cortex. All p<0.05, FWE corrected (cluster level).
Control
Stress
0.15
0.10
0.05
0.00
MR available
MR blocked
Figure 3.4. Stress-by-MR-blockade interaction on the connectivity between the centro-medial amygdala and
the caudate nucleus. Left: Analysis of the stress-by-MR-blockade interaction revealed increased connectivity
between the centro-medial amygdala seed (CMA, indicated in green) and the caudate nucleus during stress
in the MR-available group (displayed using tangential slice). However, this effect was abolished in the MRblocked groups. Right: For all groups, we extracted the data from the cluster showing significantly stronger
connectivity with the CMA in the stress/MR-available group than the control/MR-available group. Error bars
represent 1 SEM. For visualization, the statistical parametric map is plotted on the average anatomical scan of
all participants and thresholded at p<0.005, uncorrected.
Next, we investigated main effects of stress on connectivity between the amygdala subregions and the rest of the brain, but no significant effects emerged. However, we found a
stress-by-MR-blockade interaction in the connectivity between the CMA and the dorsal
striatum, more specifically the caudate nucleus (pSVCcorr=0.005, k=21, Tmax=4.58, Figure 3.4).
Post-hoc tests revealed that stress in the MR-available group was associated with stronger
connectivity between the CMA and the caudate, extending to the putamen (pSVCcorr=0.001,
k=39, Tmax=5.08). This stress-induced increase in connectivity was absent in the MR-blocked
groups. Importantly, this cluster showed stronger connectivity to the CMA as compared to
the BLA (pSVC<0.05, corrected for the anatomical caudate mask). No other regions showed sig53
3
Parameter estimates for CMA - caudate connectivity (a.u.)
Chapter 3
.40
Stress/MR available
Control/MR blocked
Stress/MR blocked
.30
.20
.10
.00
-.10
-.20
-1000
0
1000
AUCi cortisol
2000
3000
Figure 3.5. Correlation between cortisol reactivity (area under the curve with respect to the increase, AUCi)
and functional connectivity between the centro-medial amygdala (CMA) and the striatal cluster showing a
stress-by-MR-availability interaction in Figure 3.4.
nificant stress-by-MR-blockade interactions (whole-brain or SVC in our ROIs) in connectivity
to the CMA. Also, no significant stress-by-MR-blockade interactions on functional connectivity
from the other two seed regions, BLA and SFA, were found.
Finally, we investigated whether inter-individual differences in stress-induced increases in
cortisol levels were associated with stress-related changes in connectivity. To this end, we
calculated the area under the curve with respect to the increase in cortisol during the experimental phase (AUCi; Pruessner et al, 2003) and correlated this to the parameter estimates
extracted from the cluster shown in Figure 3.4 (threshold p<0.005, uncorrected).
We found a positive correlation in the stress/MR-available group (r=0.448, p=0.028, Figure
3.5), indicating that participants with higher cortisol reactivity showed stronger connectivity
between the CMA and the dorsal striatum when the MR was available. We did not find this
association in any other group (all p>0.4), and the correlation in the Stress/MR-available group
was significantly stronger than in the Control/MR-available group (Fisher’s z=-2.05, p=0.040),
but not in comparison to the other groups. Interestingly, this association in the stress/
MR-available was already present at the first measurement after stress induction (ρ=0.417,
p=0.042), i.e. not driven purely by cortisol increases after the task.
54
Discussion
Our results reveal a stress-induced shift in amygdala connectivity with regions supporting
automatic, habitual behavior. Stress enhanced connectivity between the CMA and the dorsal
striatum (independent of task condition) and the strength of this effect was correlated with
the strength of the stress-induced cortisol response across subjects when the MR was available. Moreover, we demonstrate a rapid onset of this shift within few minutes after stress
induction, when NE should still be active and cortisol levels are rising. This finding might
suggest that the two shifts in brain networks during stress attributed to NE (Hermans et al,
2014b) and cortisol (Schwabe et al, 2013c), might be related, coordinating reallocations of
neural resources involving amygdala processing.
Stress-induced changes are brought about by different waves of neuromodulators (Joëls
& Baram, 2009). Activation of the NE-LC system together with the peripheral sympathoadrenomedullary system leads to the rapid release of catecholamines, exciting the salience
network in humans (Hermans et al, 2011) and enhancing vigilance. Activation of the HPA axis,
conversely, results in a somewhat slower action of corticosteroids which bind to two receptors
in the brain, the glucocorticoid (GR) and mineralocorticoid receptor (MR). Both receptors have
two modes of action, a rapid non-genomic mode and a slow genomic mode, mediated by
receptors residing at the membrane or in the cytoplasm, respectively, as demonstrated mostly
in rodents or in-vitro (Joëls et al, 2012). Whereas the slow genomic effects promote reinstalling
homeostasis in humans and rodents (presumably GR-mediated, Henckens et al, 2011; Herman
et al, 2012), rapid non-genomic (MR-mediated) effects seem to enhance catecholaminergic
effects within minutes, for example by enhancing excitability of the amygdala in-vitro (Karst
et al, 2010). Studies investigating emotional memories in rodents have already demonstrated
that both stress-systems have synergistic effects in the amygdala (e.g. Roozendaal et al, 2006a).
Importantly, we did not measure NE levels directly. However, considering the stress-induced
increase in heart rate during the task, an activation of the LC-NE system is strongly suggested.
Thus, our findings support the idea of interactive effects of NE and cortisol, potentially affecting resource allocation to different brain networks during an acutely stressful experience.
More specifically, our data support that rapid, non-genomic effects of cortisol mediated by
the MR are involved in changing functional brain connectivity.
A similar stress-induced shift in brain connectivity between amygdala and the right caudate was observed earlier in a probabilistic classification learning task (Schwabe et al, 2013c).
However, while the previous study showed this effect forty minutes after stress induction, here
we demonstrate its appearance even within a few minutes after stress induction. Considering
that such a shift should help individuals to handle stressful situations and spare resources by
relying on automatic, well-learned behavior, it seems plausible that this would happen immediately. Furthermore, we could show the stress-induced shift depending on MR-blockade
in a task probing emotional vigilance. This led us to carefully conclude that the stress-induced
55
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Chapter 3
reallocation of neural resources might be more general in nature, activating the salience network, increasing connectivity between the salience network and the dorsal striatum, and at
the same time it might inhibit prefrontal processing. Finally, we could refine the earlier result
(Schwabe et al, 2013c), by showing that a particular sub-region of the amygdala, the CMA,
orchestrates the stress-induced shift in brain connectivity.
Only few studies have investigated differential connectivity of amygdala sub-regions in
humans. Roy and colleagues revealed that the CMA preferentially connects to subcortical
brain regions, whereas the BLA preferentially connects to cortical regions in healthy adults
(Roy et al, 2009). A study in social anxiety disorder patients showed increased gray matter
volume in the CMA and less distinct connectivity patterns of the sub-regions (Etkin et al,
2009) compared to healthy controls. In another study, post-traumatic stress disorder patients
showed enhanced connectivity between BLA and regions of the salience network (Brown
et al, 2014). These studies highlight a potential clinical relevance of these distinct regional
amygdala circuits. It is important to note that our definition of the amygdala sub-regions
was based on probability maps and due to inherent methodological limitations, we cannot
be certain that we have optimally mapped these structures in each individual participants.
However, the distinct connectivity patterns we found indicate our ability to separate signals
coming from different sub-regions.
It has been reported before, mostly in rodents, that the MR is also involved in reactivity to
novel situations, behavioral flexibility, and coping strategies (Berger et al, 2006; de Kloet et
al, 1999; Oitzl & Dekloet, 1992; Oitzl et al, 1994). In humans this effect of MR-blockade on appraisal is not supported yet. We found a trend for MR-blockade to prevent the stress-induced
increase in negative mood, which might serve as first evidence supporting a role for the MR
in appraisal of novel or stressful situations in humans. However, a replication of this finding is
certainly needed.
Our study supports the role of the MR in stress-related changes in cognition and behavior.
Specifically, the MR appears to mediate a shift to more striatal control over behavior, favoring
well-learned habit-like responses and stimulus-response learning over controlled, flexible behavior guided by long-term goals (Schwabe et al, 2010b). This might have implications for our
understanding of stress-related mental disorders. For example, the shift might be relevant in
preventing relapse in addiction, or the reappearance of maladaptive behavior in anxiety disorders. A stress-induced shift towards habitual behavior and short-term outcomes, together
with impaired control mechanisms, might facilitate these symptoms (Arnsten, 2009). If this
model would hold, one may speculate that the MR might serve as a drug target affecting for
example amygdala-striatum interactions.
The findings of this study should be viewed within its strengths and limitations. Strong
points are the large sample size, its full-factorial design, and a pharmacological manipulation
enabling us to investigate the effects of stress depending on current MR-availability. However,
one should keep in mind that our measure of functional connectivity is correlational in nature
56
and provides no information on causality. Accordingly, it cannot be concluded that the CMA
‘drives’ the stronger connectivity to the caudate. However, animal studies point to a causal role
of the amygdala in the stress-induced shift towards striatal control over behavior (Packard &
Wingard, 2004). While the CMA is the critical output structure of the amygdala when it comes
to fear memory and its modulatory effects on behavior and autonomic responses (LeDoux et
al, 1988), in the animal literature the BLA was suggested to be critical in modulating different
memory systems in the spatial domain (Packard & Teather, 1998; Packard & Wingard, 2004).
Although our results point to a critical role of the CMA, the BLA might also be involved in
shifting brain networks under stress.
Interestingly, we did not find main effects of stress on amygdala reactivity which is in
contrast to studies reporting enhanced amygdala activity under stress (van Marle et al, 2009).
However, this latter study tested female participants only and there is initial evidence for
sex-specific effects of stress-related neuromodulators on face processing in the amygdala
(Schwabe et al, 2013a). Furthermore, the study by van Marle used a different stress induction procedure (violent movies), which has strong effects on arousal and the NE system, but
is less effective in activating the HPA axis. One might also speculate why we did not find a
stress-induced decrease in connectivity between amygdala and the hippocampus as shown
previously (Schwabe et al, 2013c), which might be related to differences in timing and task.
Whereas the latter study had a delay of 40 min between stress induction and testing, we
tested immediately after stress-induction, and without changing the context. Possibly, the
decrease of amygdala-hippocampus connectivity needs more time to develop. Furthermore,
the emotional face-matching task does not contain a direct explicit memory component.
While we did find that stress increased task-related activity in the insula, we did not observe
an increase in salience network activity in general, which is in contrast to earlier reports (e.g.
Hermans et al, 2011). This might be explained by differences in the design (movie watching
versus specific task) and differences in delay between stress induction and data acquisition
(during the first two minutes of stress induction as opposed to during a task starting ten
minutes after stress induction onset). Future studies directly targeting amygdala-LC interactions would be important to better understand the mechanisms underlying stress-induced
changes in neural networks.
The stress-induced increase of connectivity between CMA and caudate and its blockade
by spironolactone might be partially due to other, indirect endocrine effects of the drug. The
administration of spironolactone led to a rise in cortisol levels, both in the control and in
the stress group. This is in line with previous studies (Cornelisse et al, 2011; Otte et al, 2007;
Schwabe et al, 2013c) and can be interpreted as verification of drug action. The heightened
cortisol levels most likely resulted from blocked negative HPA-axis feedback, where the MR
is a critical regulator (de Kloet et al, 2005). Importantly, however, MR-blockade did not affect
the cortisol response to stress (no time-by-stress-by-MR-blockade interaction, p>0.3). This was
reported before (Cornelisse et al, 2011; Schwabe et al, 2013c) and is not due to our baseline
57
3
Chapter 3
correction, as the interaction was also absent using uncorrected, raw values (p>0.2). Spironolactone might also change levels of adrenocorticotropic hormone (ACTH) and corticotropin
releasing factor (CRF). Interestingly, no adjustment of ACTH levels was found after spironolactone application in recent human studies (Otte et al, 2007; Rimmele et al, 2013). Nevertheless,
possible differences in CRF release could have potentially affected the stress response (Sajdyk
et al, 1999). Furthermore, after MR blockade, more cortisol will be available for binding to GRs,
and GR-activation or the ratio between MR and GR activation might thus have contributed
to our effects. Therefore, our results may be interpreted in terms of a decrease in the relative
balance between MR and GR activation rather than the consequence of MR-blockade only.
Finally, spironolactone primarily binds to MRs, but can also affect other receptors, for example
progesterone receptors (Schane & Potts, 1978). Animal experiments with more specific drugs
might help here in further specifying the role of the MR in the stress-induced shift.
Regardless of these limitations, our results are in line with a model in which stress induces
a rapid reallocation of neural resources towards vigilance processing, which enhances CMA
connectivity with the striatum. Furthermore, we could show that this shift depends critically
on the availability of MRs in the early stages of stress. Most importantly, we suggest that the
two stress-induced shifts in brain networks involving amygdala processing might be part of
the same underlying process, i.e. a coordinated reallocation of neural resources preferring the
salience network and the striatum at the expense of the executive control network. Future
studies are needed to obtain a more comprehensive picture of the different neuromodulators
and neural networks involved in the stress-induced shift, their correlates in cognition and
behavior, and the precise neural mechanisms associated.
(NWO) awarded to GF, MJ, and MSO: (433-09-251). Supplementary information is available via the
Nature Publishing Group: http://www.nature.com/npp/journal/vaop/ncurrent/abs/npp2014271a.
html
58
Supplementary material
Table S3.1: Peak voxels and corresponding T-values of significantly activated clusters during the emotional
face-matching task.
Brain Region
cluster
size
MNI coordinates
x
y
z
peak
T
Emotion > visuomotor
extended cluster covering inferior, middle, and superior occipital gyrus;
fusiform gyrus; calcarine fissure and surrounding cortex; Lingual gyrus;
inferior, middle, and superior temporal gyrus; inferior parietal; angular
gyrus; precuneus, cuneus; middle and superior temporal pole; thalamus;
hippocampus; parahippocampal gyrus; amygdala; putamen; cerebellum
(all L,R) superior parietal gyrus; inferior frontal gyrus, opercular, triangular,
and orbital part; middle frontal gyrus including orbital part; precentral
gyrus; insula; rolandic operculum (all L)
51370***
28
-96
-6
26.01
inferior frontal gyrus, opercular, triangular, and orbital part, middle frontal
gyrus, precentral gyrus, insula (all R)
4386***
48
26
20
12.58
supplementary motor area (L,R); superior frontal gyrus, medial (L,R),
median cingulate (L,R); superior frontal gyrus, dorsolateral (L)
811***
-8
14
48
7.87
inferior and superior parietal gyrus, angular gyrus, middle occipital gyrus
(all L)
661***
-30
-58
42
7.40
gyrus rectus
409**
0
56
-16
6.29
angular gyrus, inferior parietal gyrus, middle occipital gyrus (all R)
252*
34
-56
42
5.66
median and posterior cingulate gyrus (L,R)
1930***
4
-32
42
10.40
Middle frontal gyrus, orbital part; gyrus rectus; anterior cingulate; superior
and middle frontal gyrus (all L,R); superior frontal gyrus, orbital (L,R) and
medial part (L)
8345***
12
46
-2
9.08
angular gyrus; supramarginal gyrus; inferior parietal; rolandic operculum;
superior temporal gyrus (all R)
2324***
60
-48
36
8.58
inferior parietal; supramarginal gyrus; angular gyrus; middle occipital
gyrus; superior temporal gyrus (all L)
1523***
-58
-52
36
6.92
368**
-56
-4
4
4.86
Emotion < visuomotor control
superior temporal gyrus; heschl gyrus; rolandic operculum (all L)
Table shows all local maxima > 8.0mm apart. R right; L left; MNI Montreal Neurological Institute. All labels are taken from the
Automatic Anatomical Labeling (AAL) atlas. *** pFWEcorr<0.001; ** pFWEcorr<0.01; * pFWEcorr<0.05
59
3
Chapter 3
CMA
T
T
BLA
5
53
5
70
Figure S3.1: Brain areas showing connectivity to the basolateral amygdala (BLA, left) or the centro-medial
(CMA, right) during the emotional face-matching task, plotted on a template brain. All p<0.05, FWE corrected
(peak level).
60
Chapter 4
Cortisol mediates a stress-induced shift towards
stimulus-response learning via the human
mineralocorticoid receptor
S.Vogel,F.Klumpers,T.NavarroSchröder,K.T.Oplaat,
H.J.Krugers,M.S.Oitzl,M.Joëls,C.Doeller&G.Fernández.
In preparation.
Chapter 4
Abstract
Stress is assumed to cause a shift from flexible ‘cognitive’ memory systems to more rigid ‘habit’
memory systems. For spatial memory, previous work showed that stress improves stimulusresponse learning based on the striatum at the cost of place learning depending on the
hippocampus. While the neural basis of this shift is still investigated, previous evidence points
towards cortisol interacting with the mineralocorticoid receptor (MR) to affect amygdala
functioning. The amygdala is in turn assumed to orchestrate the stress-induced shift. To test
this hypothesis in humans, we combined functional neuroimaging of a spatial memory task,
stress-induction, and administration of an MR-antagonist in a full-factorial, placebo-controlled
between-subjects design in a large sample of healthy males (n=101). We demonstrate that
a stress-induced increase in cortisol levels leads to enhanced stimulus-response learning,
accompanied by increased amygdala activity and connectivity to the striatum. Importantly,
this shift depends on MR-availability as it was prevented by an acute administration of the MRantagonist spironolactone. Our findings support the importance of the MR in a stress-related
shift towards habit memory systems, which might have important implications for every-day
life and stress-related mental disorders.
62
Cortisol mediates a stress-induced shift towards stimulus-response learning
Introduction
Encountering stressful events triggers a well-described cascade of neural changes (Joëls &
Baram, 2009), which ultimately also affects memory. While earlier studies focused on stress
effects on the quantity of information encoded and retrieved, recent studies showed that
stress also changes memory quality by shifting the balance of systems underlying learning
(Hermans et al, 2011; Schwabe & Wolf, 2013) and retrieval (Elliott & Packard, 2008). Under stress,
memory formation and recall are supposed to be dominated by systems supporting inflexible, ‘habitual’ forms of learning such as stimulus-response learning based on the striatum. In
contrast, the contribution of a rather controlled ‘cognitive’ memory system centered at the
hippocampus appears reduced under stress (Packard & Teather, 1998; Packard & Wingard,
2004; Wingard & Packard, 2008). This shift towards habit memory is assumed to be adaptive
by enabling rapid learning and recall of simple stimulus-response associations in the face of
limited cognitive resources and high external demands.
Despite its assumed beneficial effects in acutely threatening situations, this shift might
prove relevant for several psychiatric disorders involving well-learned but maladaptive responses to salient cues. For example, habitual, often generalized stimulus-fear associations,
concurrent with impaired hippocampal memory functioning might underlie post-traumatic
stress disorder (Acheson et al, 2012). Furthermore, patients suffering from addiction or obsessive compulsive disorder might be prone to relapse by habitually retrieving maladaptive
thoughts and behaviors. These maladaptive habitual responses are increased when patients
are confronted with stressful situations (Herman & Polivy, 1975; Weiss et al, 2001).
The stress-induced shift towards more reflexive, habitual memory systems was first discovered in rodents in the spatial memory domain. Under stress, rodents and humans preferentially encode and retrieve simple responses towards salient landmarks which are encoded
egocentrically and depend on the striatum (Elliott & Packard, 2008; Kim et al, 2001; Packard &
Teather, 1998; Packard & Wingard, 2004; Schwabe et al, 2007; Schwabe et al, 2010a; Wingard &
Packard, 2008). However, stress impairs the acquisition of more complex spatial representations which are based on the hippocampus (Morris et al, 1982). These hippocampus-based
representations are encoded allocentrically and include the relationships between multiple
landmarks in order to gain a full ‘cognitive map’ and navigate flexibly in the environment. To
summarize, stress appears to deteriorate the more complex spatial memory system while
leaving habit-like memories intact (Wingard & Packard, 2008).
The underlying neural mechanism of this stress-induced shift towards habit memory is still
rather unclear in humans. It was demonstrated in rodents that the shift is mediated by the
amygdala which enhances stimulus-response learning and decreases place learning (Packard
& Teather, 1998; Packard & Wingard, 2004). A behavioral study in mice suggested that this
shift depends on glucocorticoids binding to the mineralocorticoid receptor (MR) (Schwabe
et al, 2010a). Another study illustrated that glucocorticoids can rapidly activate the amygdala
63
4
Chapter 4
in-vitro via an MR-mediated mechanism (Karst et al, 2010), which might contribute to the
stress-induced shift. Here, we set out to translate these findings to the human domain and
reveal the neural mechanisms underlying a stress-induced shift in human spatial memory. We
hypothesized that a stress-induced shift would be mediated by cortisol activating the MR and
leading to a dominance of striatal stimulus-response learning. Furthermore, we expected this
shift to affect amygdala activity and connectivity as this structure is assumed to orchestrate
the stress-induced shift and we recently showed a stress-induced upregulation of amygdalastriatal connectivity (Vogel et al, 2015). To test these hypotheses, we used a full-factorial design,
investigating the effects of both acute stress and MR-blockade on the systems contributing to
spatial memory in a large sample of healthy men (n=101).
Methods
The study was approved by the local ethical committee (NL37819.091.11) and registered in
the Dutch (3595) and European trial registry (2011-003493-85). The current study was part of
a large-scale study investigating stress-effects depending on MR-availability. Two other data
sets acquired in the same participants using independent tasks investigated the role of the
MR in emotional face processing (Vogel et al, 2015) and fear learning (Vogel et al, accepted
for publication).
Participants
Healthy right-handed male volunteers (N=101) with normal weight (18.5 ≤ body mass index
≤ 30) were included after being screened for the following exclusion criteria: irregular sleep/
wake rhythm, non admissibility to the MRI scanner, smoking (>5 cigarettes weekly), alcohol
consumption (>21 beverages weekly), use of recreational drugs (>weekly), history of psychotropic medication, history of or current psychiatric, neurological, cardiovascular, endocrine, or
hepatic disease, bradycardia or tachycardia (heart rate <50 or >100 bpm at rest), hyperkalemia
(potassium levels > 5.0 mEq/L), and impaired renal function (creatinine levels > 1.1 mg/dl).
Athletes were excluded because of a possible positive doping test result after spironolactone
intake. All participants had normal or corrected-to-normal vision and normal hearing. They
were told to refrain from medication (other than paracetamol for acute pain) and recreational
drugs for 72h, alcohol for 24h, and coffee for 3h before testing. All participants provided
written informed consent and were financially compensated. Two participants had to be
excluded due to panic attacks and motion sickness during scanning and one participant did
not comply with study instructions (participation in another drug study). This resulted in a
final number of 98 participants (mean age 21.9 years [SD=2.9], Table 3.1). All participants had
experience of playing first-person perspective video games.
64
General procedure
Participants were randomly assigned to one of four experimental groups (control/MR-available, stress/MR-available, control/MR-blocked, stress/MR-blocked) not differing in age, body
mass index, or trait anxiety (Table 3.1.). While the factor MR-availability was manipulated in a
double-blind fashion, the factor stress was not blinded.
Drug administration and adaptation phase
Testing took place in the afternoon to ensure stable endogenous levels of cortisol. After
obtaining baseline salivary cortisol, subjective mood, and vital signs (blood pressure, heart
rate) assessments, participants were orally administered four capsules containing either
100mg of the MR-antagonist spironolactone each (tablets; Teva Pharmachemie, Haarlem, the
Netherlands) or placebo. This dosage is in accordance with other studies investigating the
MR in humans (Cornelisse et al, 2011; Rimmele et al, 2013). Afterwards, they practiced the
spatial memory task and rested for 80 minutes (min) to ensure adaptation to the laboratory
environment and absorption of the drug. Cortisol and vital signs were measured every 30 min.
Experimental phase
Participants were brought to the MRI scanner and underwent a stress induction or a nonstressful control procedure (described below). A short task probing processing of emotional
faces followed (‘task1’; Vogel et al, 2015). Approximately 17(±4) min after stress induction
participants started with the spatial memory task. After another task probing fear memory
formation (Vogel et al, accepted for publication), participants were debriefed about the stress
induction procedure and left after a general assessment of well-being.
Stress induction
We adapted the socially evaluated cold pressure task (SECPT, Schwabe et al, 2008b) to an MRI
scanner compatible version. In contrast to the original SECPT, participants were in a supine
position on the scanner bench, immersed their right foot into ice water (0-2°C) up to and
including the ankle, and held it there as long as possible (task stopped after 3 min). During foot immersion, participants looked into a camera while being closely observed by two
non-supportive experimenters in white laboratory coats. To ensure sustained elevations of
stress levels for the subsequent task, participants underwent a difficult mental arithmetic test
(counting aloud backwards from 2059 in steps of 17) after the spatial memory paradigm. For
the control group, warm water was used (35-37°C), there was no camera, the experimenter
was friendly and casually dressed, and the arithmetic test was simple (counting forwards in
steps of 10).
65
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Chapter 4
Stress measurements
Negative mood, salivary cortisol levels, and vital signs were assessed repeatedly. For negative
mood, the Positive and Negative Affect Scale (Watson et al, 1988) was administered either
on paper or presented digitally in the MRI scanner, programmed in Presentation® (Neurobehavioral Systems, Inc.). Sum scores for negative mood were calculated per time point. Vital
signs were measured using an automatic blood pressure monitor with arm cuff (Intellisense®,
OMRON, the Netherlands) outside the MRI scanner. Heart rate during scanning was acquired
using the heart rate device of the MRI scanner, peak-scored using in-house software and averaged over time bins of 1min to cover the task. To measure cortisol levels, saliva samples were
taken using Salivettes® (Sarstedt, Germany). For each sample, participants were asked to chew
the cotton swab gently for 1 min. The samples were stored at -24°C until they were analyzed by
the Dresden LabService (Germany). After thawing, the samples were centrifuged and analyzed
using a commercially available chemiluminescence immunoassay with high sensitivity (IBL
Inc.). Cortisol levels were not normally distributed and normalized using log-transformation.
One participant (control/MR-blocked) was removed from the cortisol analysis because of
exceeding cortisol measurements (last cortisol sample and cortisol increase to that sample
exceeded mean+3SD of the group). Remaining missing cortisol data (seven non-adjacent
measurements in six participants) were interpolated by averaging across two neighboring
measurements. For negative mood, missing items (six items in five participants) were replaced
by the individual mean score per time point.
Spatial memory task and virtual environment
We used a spatial memory task based on the Morris watermaze (Morris et al, 1982) which
has been shown to allow for distinction between striatal and hippocampal learning systems
(Doeller et al, 2008, Figure 4.1). UnrealEngine2 Runtime software (Epic Games) was used to
present a virtual first-person perspective view of a sandy squared environment surrounded
by dunes (practice environment) and a grassy plane surrounded by a circular cliff (boundary,
experimental environment) with a background of mountains, clouds, and the sun. The background cues were presented at infinity to allow orientation but not location within the arena.
A traffic cone was used as intramaze landmark. Landmark and boundary were rotationally
symmetric, leaving the background cues as main source of orientation. Participants navigated
with their right hand operating four buttons to ‘move forward’, ‘turn left’, ‘turn right’, or ‘drop’
objects. The viewpoint was ≈2 virtual meters (vm) above the ground, the arena was ≈32vm in
diameter and the virtual location was recorded every 100ms.
Practice
The practice environment was used to familiarize participants with the task outside the MRI
scanner. Participants saw four everyday objects sequentially, once each, at different locations
in the arena and were instructed to collect them by ‘walking’ over them and to remember
66
A
Drop
Path
Orientation cues
Boundary
x
.
Landmark
Object
B
study
test 0
.
Cue
2
x
+
Recall
~8 (max 16)
.
dL
Landmark
.
+
x
dB
o
Feedback
16 trials
o
Correct
~6
+
+
position predicted
by boundary
test 1
x
new landmark
location
+
Object
x
o
o
+
landmark move
16 trials
.
o
Start
position
position predicted
by landmark
Boundary
landmark move
test 2
16 trials
+
ITI
4
time[s]
Figure 4.1. AVirtualarenainafirst-personperspective(left),andviewedfromtopbeforethelandmarkmove
(middle)andthereafter(right).Thisgraphshowstheexperimentalenvironmentincludingtheintramazelandmark(traffi
ccone),boundary(circularwall),orientationcues(mountains,clouds),andanexampleofanexperimentalobject(vase).BSchematicoverviewoftheexperimentalphasesandthesetupofatrial.During
study,everyexperimentalobjectwasshownonceandcollectedbytheparticipants.Duringtest,participants
hadtorecalltheobjectlocationsandreceivedfeedbacktoimprovetheirperformance(objectappearedafter
response).Trialsetupwasidenticalforstudyandtest.Aftertest0andtest1,thelandmarkwasmovedwithin
thearenarelativetotheothercues.dLdistancebetweenresponseandthelocationpredictedbythelandmark,
dBdistancefromthelocationpredictedbytheboundary.
their locations. Subsequently, participants were presented with two trials per object.Trials
wereseparatedintomini-blocks(unknowntotheparticipants),eachobjectoccurringonce
per mini-block. Object order within the mini-blocks was semi-random with no object appearingintwosuccessivetrialsacrossmini-blocks.Eachtrialstartedwithacueshowingthe
objecttoberecalled(2s)afterwhichparticipantshadto‘move’fromarandomstartposition
towheretheythoughttheobjecthadbeenpreviously(recall).Theywereinstructedtodo
soasfastandaccuratelyaspossible.After‘dropping’ theobjectbybuttonpress,theobject
appearedonitscorrectlocation(feedback)sothatparticipantscouldcollectitandimprove
theirperformance.Trialswereseparatedbyanintertrialintervalshowingafixationcross(4s).
Study
ForthemaintaskintheMRIscanner,fourneweverydayobjectswerepresentedsequentially,
onceeach,inanarenawithdifferentlandmark,boundary,andorientationcues.Participantswere
againinstructedtocollecttheobjectsby‘walking’overthemandtoremembertheirlocations.
67
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Chapter 4
Test
After study, participants performed three test blocks (test 0, 1, and 2) with 16 trials each, four
per experimental object. The trial timing and separation into mini-blocks was similar to the
practice task. Critically, without the participants’ prior knowledge, the landmark was moved
relative to the boundary after test 0 and test 1, allowing the differentiation of object types
from test 1 onwards. Two experimental objects maintained a fixed position relative to the
boundary (boundary-based objects) and the two other objects remained in a fixed position
relative to the landmark (landmark-based objects). On average, participants took 16 min (± 1
min) to complete the task.
Recorded variables were reaction time (until object drop), recall error (in vm), and the
relative influence of boundary or landmark on drop location. As our hypotheses were specifically targeted at the dissociation between the two learning systems, we focused on the
influence parameter (Doeller et al, 2008). This is an index of which cue and therefore which
memory system (hippocampal-based place learning using the boundary or striatum-based
stimulus-response learning using the landmark) dominated performance in a given trial. It
was calculated for all trials in test 1 and 2 according to (Doeller et al, 2008) as dL/(dL+dB), with dL
being the distance of the response from the location predicted by the landmark and dB being
the distance from the location predicted by the boundary (Figure 4.1). The resulting influence
parameter varies between 0 (fully relying on the landmark) and 1 (fully relying on the boundary). For test 2, i.e. after the second landmark move, the incorrect cue predicts two different
locations, we used whichever difference to the response location was smaller (Doeller et al,
2008). A trial of special interest is the first trial of test 1 (after the first landmark move), as it
gives an indication of the preference for either memory system during initial learning. Similar
approaches have been implemented in earlier human and rodent experiments using this trial
after learning to determine per participant which memory system has been used (Schwabe
et al, 2008a).
All behavioral and physiological analyses were performed in SPSS 19 (Armonk, NY: IBM Corp.).
Scores for negative mood, cortisol, heart rate, and blood pressure during the experimental
phase were baseline corrected to the last measurement of the adaptation phase (-25 min) before analyses. To improve sensitivity of our analyses on the spatial memory task, we excluded
trials in which the participants did not change virtual location (no performance, 1.2% of trials,
on average 0.58 ±1.16 trials per participant). We averaged over the two objects per object-type
to reduce the amount of missing data resulting from rejecting trials with no performance.
Nevertheless, five participants had to be excluded due to no performance in two subsequent
trials within the same object-type, which all directly followed a landmark move. Furthermore,
one participant had to be excluded due to technical issues with performance logging.
68
Repeated measures ANOVA (rmANOVAs) were implemented to analyze behavioral and
physiological data including the within-subject factors time and object-type (only for the
influence parameter) and the between-subjects factors stress and MR-availability. The alpha
level was set to 0.05 for all analyses (two-tailed), Greenhouse-Geisser correction was applied
when necessary. As participants naive to the MRI scanner environment can show a stress
response to the scanning procedure itself (Muehlhan et al, 2011) and our experimental
groups differed incidentally in their percentage of naive participants [58% stress/MR-blocked,
50% stress/MR-available, 62% control/MR-blocked, 25% control/MR-available], we included
scanner naivety as covariate of no interest in all of our analyses, including the fMRI analyses.
This approach was supported by the fact that naive participants had both higher heart rate
(p<0.001) and cortisol levels (p<0.05) than non-naive participants.
Magnetic resonance imaging (MRI) and analysis
MRI measurements were acquired using a 1.5tesla Avanto Scanner (Siemens, Germany)
equipped with a 32-channel head coil. Blood-oxygenation-level-dependent (BOLD) T2*weighted multi-echo GRAPPA sequence (Poser et al, 2006) images were obtained with the following parameters: Repetition time (TR)=2.14s, echo times (TEs)=9/21/33/44/56ms, 34 transversal slices, ascending acquisition, distance factor 17%, effective voxel size=3.3x3.3x3.0mm,
field of view (FOV)=212mm. We used a multi-echo sequence for its improved BOLD sensitivity
and reduced distortion artifacts, especially in ventral regions such as the amygdala or the
hippocampus (Poser et al, 2006). We also acquired a high resolution T1-weighted anatomical
image (TR=2.73s, TE=2.95ms, 176 sagittal slices, FOV=256mm, voxel size=1.0x1.0x1.0mm).
All fMRI data were analyzed using general linear modeling in SPM8 (Wellcome Trust Centre
for Neuroimaging, London, UK). For spatial realignment of the fMRI data, head motion was
estimated on the first echo using least-squares estimation and movement correction was
applied to the five echoes acquired for each excitation using six rigid-body transformation
parameters. The echo images per volume were combined into a single volume using an
optimized echo weighting procedure based on the first 30 volumes (Poser et al, 2006) and
coregistered to the structural image. The T1-image was segmented into cerebral spinal fluid
(CSF), white matter (WM), and gray matter and used to normalize functional and structural
scans to MNI space with the unified segmentation procedure. Finally, the images were spatially smoothed using an 8mm FWHM Gaussian kernel.
Two participants had to be excluded from fMRI analyses due to technical failure and excessive head movement (>3.3mm). To test the main task effects and the dissociation of memory
systems, we set up a model including regressors for study (one regressor covering the whole
study phase), cue, recall, and feedback, all convolved with the canonical hemodynamic response function (HRF). The regressors for cue, recall, and feedback were split into two parts,
one covering test 0 (before the first landmark move) and one covering both test 1 and test
2. This split was implemented because the difference between objects being either related
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Chapter 4
to the landmark or the boundary was only present after the first landmark move. In line with
previous work, the regressor modeling recall in test 1 and test 2 was parametrically modulated
by the influence of boundary or landmark on the response (Doeller et al, 2008). We also added
a similar parametric modulator for the cue phase in order to explore anticipatory activation
being related to the influence of boundary and landmark on the subsequent response. The
feedback phase was split for boundary-based and landmark-based objects and parametrically
modulated by the amount of information learned (performance increase in the following trial
with the same object; Doeller et al, 2008). All parametric modulators were mean-centered and
set to 0 for trials in which the participants did not change virtual location. Six realignment
parameters were included to account for residual motion.
A second model was used to investigate brain regions in which activation was associated
with the stress-induced cortisol increase during recall and feedback for both object-types. This
model contained regressors for recall and feedback, split for boundary-based and landmarkbased objects, cue, and study, all convolved with the HRF, but no parametric modulators.
Again, we included six realignment parameters. Over subjects, we then correlated brain activity during feedback and recall to the increase in cortisol, calculated as area under the curve
with respect to the increase until the end of the spatial memory task (AUCi; Pruessner et al,
2003). A full-factorial design was used to investigate group differences in the correlation between cortisol increase and brain activity. This analysis showed evidence for a stress-induced
upregulation of amygdala activity present during recall and feedback, mediated by cortisol
interacting with the MR (see results). To test the hypothesis concerning enhanced amygdalastriatal connectivity under stress, we extracted the time course of amygdala activity using
the ‘Physio/Psycho-Physiologic Interaction’ tool as implemented in SPM8. The bilateral seed
was functionally defined as the combination of stress-by-MR-availability interactions on the
association with cortisol AUCi in the amygdala (see results). Correlating this time series to
activity in the rest of the brain provides information on regions that show similar activation
patterns and are therefore supposedly functionally connected with the amygdala. We added
the amygdala time series to the first level models and accounted for global signal fluctuations
by adding two regressors modeling the signal from individually defined WM and CSF masks.
A full-factorial design was used to test group differences in the correlation between cortisol
increase and amygdala connectivity.
For whole-brain analyses, the significance threshold was set to pFWE<0.05 (cluster-level).
For regions included in our a-priori hypotheses (bilateral hippocampus, putamen, caudate,
and amygdala), we implemented small volume correction (SVC), using an initial threshold of
p<0.005, uncorrected, followed by FWE-correction (p<0.05) for multiple comparisons within
regions of interest (ROIs). Anatomical masks were taken from the Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al, 2002) using Wake Forest University PickAtlas version
2.4. Given our hypothesis concerning an MR-dependent stress-induced shift, we focus the
results on effects of stress, possibly mediated by MR-activation.
70
Results
Stress measures in the adaptation phase
Negative mood, cortisol levels, heart rate, and blood pressure decreased throughout the
adaptation phase indicating successful adaptation to the laboratory environment (all main
effects of time p<0.001). MR-blockade led to higher cortisol levels before stress induction
(time-by-MR-availability interaction (F(1.7,155.4)=13.33, p<0.001; t96=3.13, p=0.002) in line with a
regulatory role of the MR on cortisol release (de Kloet et al, 2005). Importantly, within both
medication groups (MR-available, MR-blocked) there was no significant difference between
stress and control in any measure prior to stress induction (all p>0.1).
Stress measures in the experiment phase
The stress group immersed their foot in water for less time than the control group (F(1,93)=20.12,
df=1, p<0.001), but importantly, there was no influence of MR-availability (main effect or
interaction). Stress-related increases in negative mood, cortisol, and, at trend-level, heart
rate demonstrated successful stress induction (Figure 4.2, negative mood: stress main effect
(F(1,91)=10.91, p=0.001), time-by-stress interaction (F(2.4,218.4)=9.81, p<0.001); cortisol: stress main
effect (F(1,92)=13.00, p=0.001), time-by-stress interaction (F(2.5,229.5)=8.93, p<0.001); heart rate:
time-by-stress interaction (F(6.0,525.9)=2.00, p=0.065)). Stress-related increases were comparable
in both medication groups, although there was a trend for MR-blockade to reduce stressinduced negative mood (time-by-stress-by-MR-blockade interaction F(2.4,218.4)=2.69, p=0.060,
MR-available: F(2.2,93.5)=10.269, p<0.001, MR-blocked: p>0.1).
As expected, MR-blockade led to heightened cortisol levels (MR-blockade main effect
(F(1,92)=15.01, p<0.001), time-by-MR-blockade interaction (F(2.5,229.5)=6.22, p=0.001)) (de Kloet et
al, 2005), but did not affect blood pressure or heart rate.
To conclude, stress induction was successful leaving cortisol and negative mood levels
elevated during the spatial memory task, whereas the weaker heart-rate increase under stress
wore off rapidly. MR-blockade led to heightened cortisol levels but did not affect stress-related
increases in cortisol or heart rate significantly.
Dissociating hippocampal and striatal memory systems for boundary and landmarkrelated objects
We first tested whether the participants acquired the task and whether we could dissociate
the underlying learning systems. In line with previous findings (Doeller et al, 2008), response
locations were influenced by both boundary and landmark cues, and participants learned
over time to use the correct cue for each object (main effect time: F(5.2,455.9)=9.83, p<0.001;
object: F(1,87)=255.47, p<0.001; time-by-object interaction: F(5.2,454.3)=50.29, p<0.001, Figure 4.3).
Accordingly, the influence of the boundary increased over trials for boundary-based objects
and decreased for landmark-based objects, demonstrating that participants learned to differ71
4
Chapter 4
0.4
0.3
0.2
0.1
0.0
-0.1
Spatial memory
Emo-Face
S1
S2
Fear memory
-0.2
-25
0
5
15
45
70
100
time
100
time
Heart rate (bpm)
10
5
0
-5
S1
Emo-Face
Spatial memory
0
10 11 12 13
18 19 20 21 22 2324 25 26 27 28 29 30
Emo-Face
Spatial memory
S2
Fear memory
-10
-25
Negative mood
6
4
2
0
S1
-2
-25
0
5
S2
Fear memory
45
Stress/MR-available
Control/MR-blocked
100
time
Stress/MR-blocked
Figure 4.2. Cortisol levels (top), heart rate (middle), and negative mood (bottom) over the course of the experiment. Participants were randomly assigned to one of four groups: control/MR-available (gray dotted lines),
stress/MR-available (black dotted), control/MR-blocked (grey solid), stress/MR-blocked (black solid). After pill
ingestion and habituation to the laboratory environment, participants were brought to the MRI room and underwent either the socially evaluated cold pressure test (S1) or a non-stressful control procedure. After another
task (Vogel et al, 2015), participants performed the spatial memory task. Stress-related increases in negative
mood, cortisol, and, at trend level, heart rate displayed successful stress induction in both drug groups (details
in text). Time is indicated in minutes after stress induction, all measurements are baseline corrected to the last
measurement during habituation (-25 minutes). Mean values are depicted, error bars represent 1 SEM, bpm
beats per minute, MR mineralocorticoid receptor.
entiate the object types. The second landmark move affected landmark-based objects more
than boundary-based objects, suggesting that participants learned that boundary-based
objects remained stationary in the arena even when the landmark moves.
72
landmark boundary
influenceparameter
1.0
influence of boundary
0.8
4
0.6
y=-12
T
y=-8
0.4
influence of landmark
4
0.2
0.0
2
1 2 3 4 5 6 7 8
y= 12
T
2
Figure 4.3: Left:Relativeinfluenceofboundaryandlandmarkonthereplacelocationduringtest1and2
forobjectslocatedrelativetotheboundary(black)andobjectslocatedrelativetothelandmark(gray).The
influencewascalculatedaccordingto(Doelleret al,2008)asdL/(dL+dB),withdLbeingthedistanceofthe
responsefromthelocationpredictedbythelandmarkanddBbeingthedistancefromthelocationpredicted
bytheboundary.Consequently,theinfluencevariesbetween0(fullyrelyingonthelandmark)and1(fully
relyingontheboundary).Landmarkmovesareindicatedbytraffi
cconeicons.Errorbarsrepresent1SEM.
Right:Activityduringrecallismodulatedbytheinfluenceoftheboundaryinthehippocampusand(attrend
level)bytheinfluenceofthelandmarkintheputamen.Forillustrativepurposes,theimagesarethresholded
atp<0.005,uncorrected.
We then aimed at disentangling the contributions of the hippocampal and the striatal
systemtospatialmemoryindependentofstressorMR-availability.Inlinewithpreviousfindings,weexpectedthemtocontributebothtotheretrieval(recall)andupdating(feedback)
ofspatialmemoriesasshownbyDoellerandcolleagues(2008).Indeed,brainactivityduringrecalltrackedtheinfluenceofboundaryversuslandmarkcuesonthereplacelocation.
Hippocampalactivitywasenhancedintrialswithastrongerinfluenceoftheboundarycue
ontheresponselocation(pSVC=0.021,k=11,Tmax=3.99[left],pSVC=0.052,k=10,Tmax=3.68[right,
trend-level],Figure4.3).Whentheresponselocationwasinfluencedmorebythelandmark,
we found a trend for enhanced activity in the dorsal striatum (putamen: pSVC=0.084, k=29,
Tmax=3.49, Figure 4.3).We also explored a possible modulation by the influence parameter
onpreparatorybrainactivityduringthecuephase,butdidnotfindcorrespondingevidence.
Finally,wealsofoundenhancedactivityinthestriatumwhenparticipantsreceivedfeedback
aboutlandmark-basedobjectswhichimprovedtheirperformance(caudate:pSVC=0.010,k=56,
Tmax=4.16[left],pSVC=0.037,k=31,Tmax=3.72[right],FigureS4.1).
To summarize, we confirmed that participants learned to use distinct memory systems
whenpredictingthelocationoflandmark-basedandboundary-basedobjects.Asexpected,
thehippocampuswasmoreactivewhenparticipantsreliedontheboundarytorecallanobject’slocation,indicativeofplacelearning.Incontrast,whenparticipantsreliedmoreonthe
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Chapter 4
landmark during recall or updated their memories for landmark-based objects, the striatum
was more engaged, indicative of stimulus-response learning.
Stress and MR-availability affect the relative use of memory systems
After establishing that participants successfully used both memory systems to solve the task,
we investigated whether stress and MR-availability affected the use of memory systems. In
line with our hypothesis of a stress-induced shift, we found a time-by-stress-by-MR-blockade
interaction on the influence parameter (F(5.2,455.9)=3.15, p=0.007) independent of object-type.
Simple tests indicated that this interaction originated from a significant stress-by-MRavailability interaction in the first trial after the first landmark move (F(1,92)=5.91, p=0.017) but
not in later trials. As expected, stress led to a numerically increased reliance on the landmark
in the MR-available group, and the effect was reversed in the MR-blocked groups (stress/MRblocked vs. control/MR-blocked: p=0.023; stress/MR-blocked vs. stress/MR-available: p=0.006,
see Figure S4.2). In line with a stress-induced shift towards increased use of the landmark,
the stress/MR-available group was the only group with a significant difference from 0.5
which would represent an equal influence of both memory systems (p=0.007). To account
for the large inter-individual differences in the spontaneous use of spatial memory strategies (e.g. Iaria et al, 2003), we repeated the analysis using per subject z-transformed influence
parameters. These can be interpreted as variation from the within-participant average reliance
on boundary or landmark cues. We again found effects of the factors time, object, and the
time-by-stress-by-MR-availability interaction (main effect of time: p<0.001, object: p<0.001;
object-by-time interaction: p<0.001; time-by-stress-by-MR-availability: p=0.020). Stress again
landmark boundary
z(influence of boundary, trial 1 of test 1)
0.2
0.0
-0.2
-0.4
Control
Stress
MR-available
Control
Stress
MR-blocked
Figure 4.4. Mean influence of the boundary and the landmark on the first trial in test 1 across all objects (also
shown in Figure 4.3). The relative use of boundary or landmark in this trial directly after the first landmark move
can be interpreted as an indicator for the strategy used to learn the object locations during study and test
0. This plot shows the within-subject z-transformed values, which can be interpreted as variation from the
within-participant average reliance on boundary or landmark cues over all trials. Mean values are depicted,
error bars represent 1 SEM, MR mineralocorticoid receptor, **p<0.01, *p<0.05.
74
increased reliance on the landmark in the MR-available groups (p=0.013, Figure 4.4) in the
first trial and this effect was reversed in the MR-blocked groups (p=0.001; stress/MR-blocked
vs. stress/MR-available: p=0.001; stress-by-MR-availability trial 1: p=0.002). Finally, this analysis
also revealed a main effect of stress (p=0.017), with the stress group showing a stronger influence of the landmark than the control group.
In summary, as expected, stress led to increased reliance on the landmark and decreased
reliance on the boundary. This indicates enhanced use of the striatum-based stimulusresponse learning system and reduced reliance on the hippocampus-based place learning
system under stress. In line with our hypothesis, this stress-induced shift towards stimulusresponse learning depended on MR-availability as it was reversed by an acute administration
of spironolactone.
Stress-induced increases in cortisol levels activate the amygdala depending on MRavailability
We then investigated how the stress-induced shift in behavior might arise on the neural level.
We first tested whether the engagement of the striatum or the hippocampus during recall
or feedback was affected by a stress-by-MR-blockade interaction similar to the behavioral
finding. However, we did not find such effects, also not for the amygdala (all pSVC>0.4). In
a subsequent step, given our hypothesis that the interaction between cortisol and the MR
mediates the stress-induced shift, we assessed how inter-individual differences in the cortisol
response to stress relate to brain activity. In line with a rapid stress-induced, MR-dependent
upregulation of amygdala activity we found stress-by-MR-availability interactions on the correlation between cortisol increase and amygdala activity across recall and feedback for both
object-types (Table 4.1, Figure 4.5).
In line with our hypothesis, participants with higher cortisol increases after stress in the MRavailable group showed stronger responding of the amygdala relative to those with smaller
Table 4.1. Stress-by-MR-availability interactions on the correlation between cortisol increase and amygdala
activity.
task phase
hemisphere
Tmax
k
pSVC
#
recall landmark-based objects
left
2.95
5
0.080
recall boundary-based objects
left
3.15
7
0.049*
right
2.83
2
0.089#
left
4.92
38
<0.001***
right
4.26
40
0.002**
left
3.72
36
0.012*
right
4.01
34
0.005**
feedback landmark-based objects
feedback boundary-based objects
***p<0.001, **p<0.01, *p<0.05.
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Stress/
MR-blocked
Control/
MR-blocked
Stress/
MR-available
Control/
MR-available
cortisol AUCi
-500
0
500
-.8 -.6 -.4 -.2 .0
.2
Amygdala activity
-500
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500
1000
cortisol AUCi
1000
2
-.8 -.6 -.4 -.2 .0
.2
Amygdala activity
T
2
4
4
T
recall boundary-based object
-500
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500
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-.8 -.6 -.4 -.2 .0
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Amygdala activity
T
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feedback landmark-based object
cortisol AUCi
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-500
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500
1000
-.8 -.6 -.4 -.2 .0
.2
Amygdala activity
T
2
4
feedback boundary-based object
cortisol AUCi
recall landmark-based object
Chapter 4
Figure 4.5 (Opposite page). Top: Stress-by-MR-availability interactions on the correlation between cortisol
increase and bilateral amygdala activity during recall and feedback for landmark-based and boundary-based
objects. For illustrative purposes, the images are thresholded at p<0.005, uncorrected. Bottom: For illustrative
purposes, we then extracted the parameter estimates from the amygdala clusters displaying a stress-by-MRavailability interaction (at p<0.005, uncorrected) and plotted the correlation with the cortisol increase (area
under the curve with respect to the increase, AUCi). The different task phases (recall and feedback for both
object types) are plotted separately for the different experimental groups. All correlation plots use the same
scale for both axes which is indicated for the control/MR-available group. MR mineralocorticoid receptor.
cortisol increases (recall boundary-based objects pSVC=0.087, k=7, Tmax=2.91 [trend]; feedback
landmark-based objects pSVC=0.010, k=10, Tmax=3.78 [left], pSVC=0.004, k=28, Tmax=4.08 [right];
feedback boundary-based objects: pSVC=0.025, k=18, Tmax=3.47). Crucially, in line with a critical
role for the MR in driving this association in the stress group, the correlation was abolished
in the stress group where the MR was pharmacologically blocked (no significant voxel). Unexpectedly, we also found a positive association between the cortisol increase over time and
amygdala activity in the absence of stress in the control/MR-blocked group (all pSVC<0.05 for
recall and feedback of both object-types).
Stress and MR-availability differentially affect amygdala connectivity
4.5
T
2.0
Parameter estimates for the correlation with cortisol increase
(peak voxel, a.u. and 90% CI)
Considering our finding of amygdala activity being correlated to the stress-induced cortisol
increase, we tested whether the amygdala drives the stress-induced shift towards enhanced
stimulus-response learning based on the striatum (Vogel et al, 2015). We found a stress-by-MRavailability interaction on the correlation between cortisol increase and amygdala connectivity
to a large cluster covering both putamen and caudate (putamen: pSVC=0.003, k=17, Tmax=4.61,
caudate: pSVC=0.004, k=47, Tmax=4.53, Figure 4.6). In line with our hypothesis, the association
6
x 10
control
−4
stress
4
2
0
−2
−4
−6
MR-available
MR-blocked
−8
Figure 4.6. Left: The association between amygdala-striatal connectivity and cortisol increase measured as
area under the curve was affected by stress and MR-availability. For illustrative purposes, the image is thresholded at p<0.005, uncorrected. Right: For illustrative purposes only, we plot the parameter estimates for the
correlation between amygdala-striatal activity and cortisol increase.
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between cortisol increase and amygdala-striatal connectivity was significantly stronger in the
stress group as compared to the control group, but only when the MR was available (putamen:
pSVC=0.037, k=18, Tmax=3.79, caudate: pSVC=0.013, k=28, Tmax=4.14; MR-blocked: no significant
voxel). This suggests that participants with stronger stress-induced cortisol increases show
not only enhanced amygdala activity, but also strengthened functional connectivity between
amygdala and striatum. We did not find group differences in the relationship between cortisol
increase and amygdala connectivity with other brain regions.
Discussion
We set out to investigate how stress-related activation of the MR shifts the use of different
spatial memory systems. We demonstrated successful stress-induction and replicated earlier
findings of a neural dissociation of two learning systems involved in spatial memory formation (Doeller et al, 2008). In line with our hypothesis concerning a stress-induced shift, we
found that stress induced a behavioral bias towards enhanced stimulus-response learning.
This shift may be orchestrated by the amygdala given that stress-induced increases in cortisol
levels were associated with heightened amygdala activity and enhanced amygdala-striatal
connectivity. Importantly, both the behavioral and neural stress effects could be prevented by
an acute administration of the MR-antagonist spironolactone. Therefore, our results provide
first evidence that stress produces an increased dependence on stimulus-response learning
in humans that is sensitive to blockade of the MR and involves an upregulation of amygdala
activity and connectivity to the striatum.
The actions of glucocorticoids are mediated by two receptor types, the MR and the glucocorticoid receptor (GR). Both MR and GR can induce rapid, non-genomic and slow, genomic
effects mediated by receptors residing presumably at the membrane or in the cytoplasm,
respectively (Joëls et al, 2012). In general, the fast non-genomic effects mediated mainly by
the MR are assumed to be permissive for the stress response, possibly in interaction with
norepinephrine (Groeneweg et al, 2011). An in-vitro study illustrated that MR-activation
can rapidly enhance excitability of the amygdala (Karst et al, 2010). Other studies in rodents
demonstrated that amygdala activation can in turn modulate the balance of spatial memory
systems from hippocampal to striatal control over behavior (Packard & Teather, 1998; Packard
& Wingard, 2004; Wingard & Packard, 2008). We translated these findings to the human domain by demonstrating behaviorally a stress-induced MR-dependent shift towards enhanced
stimulus-response learning based on the striatum. On the neural level, this shift might be
driven by increased amygdala activity and connectivity with the striatum, especially in those
participants with high stress-induced cortisol responses. Considering the rapid timing of these
effects, i. e. within 45 min after stress onset, we tentatively conclude that these stress-induced
effects likely result from rapid, non-genomic pathways, supposedly mediated by MRs located
at or close to the plasma membrane.
78
The finding that a stress-induced amygdala activation depends on the MR was never
shown in humans, but was suggested by in-vitro experiments (Karst et al, 2010). These authors
demonstrated rapidly enhanced amygdala excitability after corticosterone administration,
which was driven by MR-activation, and subsequently sustained by GR-activation. This increase of amygdala activity was interpreted as enabling enhanced memory encoding during
and directly after stressful events. However, we show that MR-dependent amygdala upregulation might as well lead to a qualitative shift in the use of different memory systems. We also
found that heightened cortisol levels induced by MR-blockade under non-stressful conditions
were associated with enhanced amygdala activity. Given that the MR was blocked in these
participants, this suggests that also the GR can enhance amygdala activation with moderate
cortisol increases. When interpreting the result of a heightened amygdala activity under MRblockade, it should be kept in mind that spironolactone caused an increase in cortisol levels
which was already present at least 25 minutes before stress onset. Therefore, in contrast to
the rapid effect in the stress/MR-available group, this upregulation of amygdala activity in
the control/MR-blocked group cannot easily be attributed to rapid non-genomic effects, but
might have involved genomic GR-effects. Furthermore, when stress-induction was combined
with moderately enhanced cortisol levels due to MR-blockade, we did not find an upregulation of amygdala activity. Interestingly, in the previous in-vitro study the researchers found a
rapid GR-dependent downregulation of amygdala excitability when a second corticosterone
administration was imposed on already heightened cortisol levels (Karst et al, 2010). Similar
u-shaped dose-response curves for glucocorticoids have been suggested earlier (Joëls, 2006)
and appear to be typical for hormones acting via multiple receptor types which co-localize
in some brain structures, which is the case for MR and GR in the amygdala (de Kloet, 2014).
Therefore, our results may be interpreted in terms of a change in the relative balance between MR and GR activation towards the GR, rather than the consequence of MR-blockade
only. In line with this reasoning, it was suggested that the ratio of MR/GR-activation might
be a regulator of cognitive functioning and that imbalance of this system may be related to
psychopathology (DeKloet et al, 2007). To conclude, we interpret our findings as supporting
an MR-dependent, stress-induced shift towards enhanced stimulus-response learning, which
might be mediated by an upregulation of amygdala activity. However, future studies are
needed to decipher the additional effects of rapid increases in GR-activation when the MR is
blocked.
Three recent studies on emotional face processing, fear learning, and probabilistic learning suggested that a stress-induced shift towards systems supporting habit-like behavior
might arise from changes in amygdala connectivity with the hippocampus and the striatum
(Schwabe et al, 2013c; Vogel et al, 2015) or by affecting hippocampal activity (Vogel et al,
accepted for publication). Beyond that, our current findings suggest that also amygdala activity can be affected by MR-activation and might then orchestrate a stress-induced shift by
changing connectivity to the striatum. Interestingly, we did not find such a stress-induced,
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MR-dependent enhancement of amygdala activity in the task directly following stress onset
(Vogel et al, 2015). This might be due to different tasks implemented, but might also suggest
that the stress-induced enhancement of amygdala activity might take about twenty minutes
to arise. The latter interpretation would be in line with in-vitro findings of enhanced amygdala
excitability after corticosterone applications of twenty minutes (Karst et al, 2010).
Another interesting aspect is that the increased amygdala activity related to stress-induced
cortisol increases was most pronounced during the feedback phase of the experiment.
During this phase, participants see the right object location, allowing them to update and
correct their memory. It appears that participants with higher stress-induced cortisol levels
show stronger amygdala activity, especially during the updating of memories, which might
depend on the MR and bias participants towards enhanced stimulus-response learning. Previous experiments, mainly in rodents, focused on the effects of a stress-induced shift between
memory systems at encoding (Packard & Teather, 1998; Packard & Wingard, 2004; Wingard &
Packard, 2008) or retrieval (Elliott & Packard, 2008). Our findings are the first to indicate that
also the updating of already encoded (but not yet consolidated) information might be affected by a stress-induced shift in humans. Together, these findings suggest that multiple
memory stages can be affected by a stress-induced shift between memory systems.
The study at hand comes with several strengths. We used a translational approach in humans by testing a specific hypothesis concerning a stress-induced shift in memory which was
derived from rodent studies. We focused on spatial memory, as it can be supported both by
‘cognitive’ and ‘habit’ memory systems, the underlying neural mechanisms of these systems are
well described, and it can be tested using comparable paradigms in rodents and humans. By
employing a task which allowed the dissociation of ‘cognitive’ and ‘habit’ learning in behavioral
and neural data, we could show that stress affects memory not only quantitatively, but also
qualitatively, by changing the balance of memory systems involved. Finally, we implemented
a full-factorial between-subjects design and combined an established stress induction with
the administration of an MR-antagonist in order to specifically target stress-induced changes
depending on MR-availability in a large sample of healthy volunteers.
However, some limitations should be kept in mind. Concerning our behavioral findings, the
influence of the boundary was stronger for landmark-based objects than for boundary-based
objects in the first trial of test 1 which seems counterintuitive. This was possibly related to
the fact that the first two objects in trial 1 were landmark-based, which might have biased
participants’ responses for the two boundary-based objects. Despite this main affect over all
groups, we were still able to reveal a stress-by-MR-availability interaction on the influence
parameter in this very trial, supporting that stress leads to increased use of the landmark, but
only given MR-availability. Furthermore, as spironolactone affects HPA axis regulation (de Kloet
et al, 2005) it can change cortisol and corticotropin releasing factor concentrations, possibly
contributing to the observed effects. Finally, spironolactone can also affect other receptors,
for example progesterone receptors (Schane & Potts, 1978). Experiments with more specific
80
drugs, possibly in rodents, might help here in further specifying the role of the MR in the
stress-induced shift. While a more specific MR-antagonist, eplerenone, has been described, its
effect on human cognition is still unclear and we chose for spironolactone in order to achieve
comparability with earlier studies in humans. Finally, it should be emphasized that the current
study was carried out in male participants only due to practical limitations to our sample
size in line with many other studies (e.g. Cornelisse et al, 2011; Otte et al, 2007). Whether MRantagonists affect learning strategies similarly in females remains to be investigated.
In line with our finding of a stress-induced shift towards ‘habit’ memory systems, recent
studies in other domains also support a stress-induced shift towards cognitively less demanding systems underlying behavior and learning (Otto et al, 2013; Vogel et al, 2015). Stress leads to
more reflexive behavior (Porcelli & Delgado, 2009; Schwabe et al, 2011), less strategic decisions
being made (Leder et al, 2013), and a dominance of more automated forms of fear learning
(Vogel et al, accepted for publication). Together, these studies suggest a rapid stress-induced
shift in neural processing, resulting in a dominance of less demanding systems across a broad
range of cognitive domains. Our findings support the model proposed earlier (Schwabe et
al, 2010a) that the shift also affects spatial memory formation by inducing a dominance of
habit-like stimulus-response learning. This shift towards the striatum might rescue memory
performance under stress (Schwabe et al, 2010a) but might also lead to strong learning of
salient cues which are hard to unlearn or update, potentially predisposing individuals (at least
males) to develop psychopathology. Importantly, our data suggest that the stress-induced
shift may be prevented by an acute administration of MR-antagonists - and possibly other
manipulations - dampening amygdala hyperactivity.
(NWO) awarded to GF, MJ, MO, and HJK (433-09-251) and CFD (Vidi 452-12-009). CFD is also supported by the European Research Council (ERC-StG RECONTEXT 261177).
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4.5
T
2.0
Figure S4.1. Striatal activation during the feedback phase is modulated by the amount of learning for landmark-based objects. For illustrative purposes, the image is thresholded at p<0.005, uncorrected.
landmark boundary
influence of boundary, trial 1 after LM move
0.6
0.5
0.4
Control/
Stress/
Control/
Stress/
MR-available MR-available MR-blocked MR-blocked
Figure S4.2. Mean influence of the boundary and landmark on the first trial across all objects in test 1, after the
first landmark move (also shown in Figure 4.3 and 4.4). The relative use of boundary or landmark in this trial can
be interpreted as indicator for the strategy used to learn the object locations during study and baseline. Mean
values are depicted, error bars represent 1 SEM, MR mineralocorticoid receptor. ** p<0.01, * p<0.05.
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Chapter 5
A stress-induced shift from trace
to delay conditioning depends on
the mineralocorticoid receptor
Theworkinthischapterisbeingpublishedas:S.Vogel,F.Klumpers,
M.C.W.Kroes,K.T.Oplaat,H.J.Krugers,M.S.Oitzl,M.Joëls,G.Fernández
Biological Psychiatry,accepted for publication.
Chapter 5
Abstract
Fear learning in stressful situations is highly adaptive for survival by steering behavior in subsequent situations, but fear learning can become disproportionate in vulnerable individuals.
Despite this significance, the mechanism by which stress modulates fear learning is poorly
understood. Memory theories state that stress can cause a shift away from cognitively more
demanding processing depending on the hippocampus towards more reflexive processing supported by amygdala and striatum. This shift may be mediated by activation of the
mineralocorticoid receptor (MR) for cortisol. Thus, we investigated how stress shifts processes
underlying cognitively demanding versus less demanding fear learning using a combined
trace and delay fear conditioning paradigm. In a pharmacological fMRI study, we tested 101
healthy men probing the effects of stress (socially evaluated cold pressor vs. control) and
MR-availability (400mg spironolactone vs. placebo) in a randomized, placebo-controlled,
full-factorial between-subjects design. Effective stress induction and successful conditioning
were confirmed by subjective, physiological, and somatic data. In line with a stress-induced
shift, stress enhanced later recall of delay compared to trace conditioning in the MR-available
groups as indexed by skin conductance responses. During learning, this was accompanied by
a stress-induced reduction of learning-related hippocampal activity for trace conditioning. Importantly, the stress-induced shift in fear and neural processing was absent in the MR-blocked
groups. Our results are in line with a stress-induced shift in fear learning, mediated by the MR,
resulting in a dominance of cognitively less demanding amygdala-based fear learning, which
might be particularly prominent in individuals with high MR sensitivity.
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A stress-induced shift from trace to delay conditioning depends on the mineralocorticoid receptor
Introduction
Fear learning in stressful situations is adaptive for survival by guiding subsequent behavior, but
is also a critical initiating factor for stress-related mental disorders (Mineka & Zinbarg, 2006).
The neurobiology of fear learning has been studied extensively, implementing different fear
conditioning paradigms. Most studies focused on delay conditioning where a neutral stimulus co-occurs with an aversive unconditioned stimulus (US) and over time comes to elicit a
fear response in itself (conditioned stimulus, CS+). The neural basis of delay conditioning is
well understood (LeDoux et al, 1990): The basolateral amygdala receives simultaneous sensory inputs from CS and US and stores this association, whereas the centro-medial amygdala
mediates autonomic and behavioral changes (LeDoux et al, 1990; Nader et al, 2001). In trace
conditioning a short interval is inserted between CS and US, changing the learning process
and brain areas involved by preventing reflexive learning (Clark & Squire, 1998). While less
studied, trace conditioning is thought to be more cognitively demanding, require higherlevel cognitive processes like declarative memory (Clark & Squire, 1998; Weike et al, 2007),
and therefore to be perhaps relevant for fear learning in more complex real-life situations. The
hippocampus seems to be necessary for trace conditioning, and the prefrontal cortex (PFC)
appears to be involved in representing the temporal CS-US relationship (Clark & Squire, 1998;
Knight et al, 2004; Kronforst-Collins & Disterhoft, 1998).
In real-life, traumatic fear learning is often embedded in stressful life events. Despite the
potential clinical significance, the interaction between stress and fear learning is not well
understood. Stress in general appears to quickly induce a reallocation of neural resources
causing a shift away from cognitively demanding to less demanding processing, for example
by impairing hippocampus-dependent but enhancing amygdala-dependent processing
(Cousijn et al, 2012; Pruessner et al, 2008; Schwabe et al, 2013c; van Marle et al, 2009). These
neural changes might then differentially affect different types of fear learning (Hermans et
al, 2014b; Packard & Teather, 1998; Schwabe & Wolf, 2013). Initial evidence suggests that
norepinephrine (Hermans et al, 2011) and cortisol (Schwabe et al, 2013c; Vogel et al, 2015)
are critical in this stress-induced shift, the latter via the mineralocorticoid receptor (MR). The
involvement of the MR was first discovered in the spatial memory domain where stress led
to a shift from hippocampus-dependent to striatum-dependent learning (Schwabe et al,
2010a; Schwabe et al, 2013c). This receptor was formerly thought to have only a limited role in
the stress response given its high affinity leading to almost full occupation even at baseline.
However, the discovery of a low-affinity membrane-bound version acting via non-genomic
pathways supported its importance in fast stress responses (Joëls et al, 2008) and stress effects
on memory formation (Cornelisse et al, 2011). Furthermore, the MR is localized in brain regions
important for fear learning (Klok et al, 2011a; Patel et al, 2000) and involved in rodent fear
conditioning (Zhou et al, 2010; Zhou et al, 2011). However, in humans the role of the MR in
stress-induced changes in different fear learning systems has not yet been studied.
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Chapter 5
Here we set out to understand the role of the MR in the stress-related shift towards cognitively less demanding fear learning. This challenge required manipulating MR-availability,
inducing a state of stress, and administering a fear learning task that distinguishes between
cognitively demanding and less demanding learning types while measuring neural correlates.
We employed a randomized, placebo-controlled, full-factorial design (between-subjects
factors stress and MR-blockade) in healthy men undergoing a combined delay and trace
conditioning paradigm while measuring brain activity using functional magnetic resonance imaging (fMRI). We hypothesized that, under MR-availability, stress leads to a shift in
fear learning such that delay conditioning comes to dominate over the more demanding
hippocampus-dependent trace conditioning.
Methods
The study was approved by the local ethical committee (NL37819.091.11) and registered in
the Dutch (3595) and European trial registry (2011-003493-85).
Participants
Healthy right-handed male volunteers (N=101) with normal weight (18.5 ≤ body mass index
≤ 30) were included after being checked for the following exclusion criteria: psychiatric, neurological, cardiovascular or endocrine disease, irregular sleep/wake rhythm, non-admissibility
to the MRI scanner, smoking (>5 cigarettes weekly), alcohol consumption (>21 beverages
weekly), use of recreational drugs (>weekly), psychotropic medication, and hepatic, cardiovascular, or renal impairments. Athletes were excluded because of a possible positive doping
test result after spironolactone intake. All participants had normal or corrected-to-normal
vision and normal hearing. They had to refrain from any medication other than paracetamol
for acute pain and recreational drugs for 72h, alcohol for 24h, and coffee for 3h before testing.
All participants provided written informed consent and were financially compensated.
General procedure
Participants were randomly assigned to one of four groups (control/MR-available, stress/
MR-available, control/MR-blocked, stress/MR-blocked). While the factor MR-availability was
manipulated in a double-blind fashion, the factor stress was not.
Drug administration and adaptation phase day 1
Testing took place in the afternoon to ensure stable endogenous levels of cortisol. After assessment of baseline cortisol, subjective mood, and vital signs (blood pressure, heart rate), participants were orally administered four capsules containing either 100mg of the MR-antagonist
spironolactone each (tablets; Teva Pharmachemie, Haarlem, the Netherlands) or placebo. This
dosage is in accordance with other studies (Cornelisse et al, 2011; Rimmele et al, 2013). A delay
86
of 80 minutes (min) followed ensuring adaptation to the laboratory environment and drug
absorption. Participants rested and cortisol and vital signs were measured every 30 min.
Experimental phase day 1
Participants performed the fear conditioning paradigm in the MRI scanner immediately after
the last part of either stress induction or non-stressful control procedure (described below).
Two other tasks were performed before the last stress induction targeting emotional face
processing (Vogel et al, 2015) and spatial memory (Vogel et al., in prep.). After an anatomical
scan, participants were debriefed about the stress induction procedure followed by a general
assessment of well-being.
Recall phase day 2
Participants returned the next day (on average 24h 32min later, SD: 105min) for a recall session
in a mock scanner, a reconstruction of an MRI scanner highly similar in appearance and sound.
Stress induction
We adapted the socially evaluated cold pressor task (SECPT, Schwabe et al, 2008b) to an MRI
scanner compatible version (Schwabe et al, 2012b). Participants were in a supine position
on the scanner bench and immersed their right foot into ice water (0-2°C), up to and including the ankle and held it there as long as possible (the task stopped after 3 min). During
foot immersion, participants looked into a camera while being closely observed by two
non-supportive experimenters in white laboratory coats. To ensure sustained stress, a socially
evaluated difficult mental arithmetic test was administered just before fear conditioning in
the stress group (counting aloud backwards from 2059 in steps of 17). For the control group,
warm water was used (35-37°C), no camera was used, the arithmetic test was simple (counting forwards in steps of 10), and the experimenter friendly and casually dressed.
Stress measurements
Negative mood, salivary cortisol levels, and vital signs were assessed repeatedly throughout
the experiment (Figure 5.1). For negative mood, the Positive and Negative Affect Scale
(Watson et al, 1988) was administered either on paper or presented on the screen in the
MRI scanner, programmed in Presentation® (Neurobehavioral Systems, Inc.). Sum scores for
negative mood were calculated per time point. Vital signs were measured using an automatic
blood pressure monitor with arm cuff (Intellisense®, OMRON, the Netherlands) outside the
MRI scanner. Heart rate during scanning was acquired using the heart rate device of the MRI
scanner, peak-scored using in-house software and averaged over time bins of 1 min to cover
the task. To measure cortisol levels, saliva samples were taken using Salivettes® (Sarstedt,
Germany). For each sample, participants were asked to chew the cotton swab gently for 1
min. The samples were stored at -24°C until they were analyzed by the Dresden LabService
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0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
S1
-25 0
S2
5
15
Dual delay and trace fear conditioning task
45
70
100
time
85 100
time
15
Heart rate (bpm)
10
5
0
-5
-10
Negative mood
6
S1
S2
-25 0
52
67 73
4
2
0
-2
S1
-25 0
S2
5
100 time
45
Stress/MR-available
Control/MR-blocked
Stress/MR-blocked
Figure 5.1. Cortisol levels (top), heart rate (middle), and negative mood (bottom) over the course of the experiment. Participants were randomly assigned to one of four groups: control/MR-available (gray dotted lines),
stress/MR-available (black dotted), control/MR-blocked (grey solid), stress/MR-blocked (black solid). After pill
ingestion and habituation to the laboratory environment, participants were brought to the MRI room and underwent either the socially evaluated cold pressor (S1) and difficult mental arithmetic task (S2) or non-stressful
control procedures. Afterwards, all participants were fear conditioned (see text). Stress-related increases in
negative mood, cortisol, and heart rate showed successful stress induction in both drug groups. Time is indicated in minutes after stress induction, all measurements are baseline corrected to the last measurement
during habituation (-25 minutes). Mean values are depicted, error bars represent 1 SEM. MR mineralocorticoid
receptor, bpm beats per minute.
88
(Germany).Afterthawing,thesampleswerecentrifugedandanalyzedusingacommercially
availablechemiluminescenceimmunoassaywithhighsensitivity(IBLInc.).Thecortisollevels
werenotnormallydistributedandnormalizedusinglog-transformation.
Combined delay and trace fear conditioning procedure
To assess both delay and trace conditioning in one task, we intermixed a CS+ that coterminatedwiththeUS(CS+delay,Figure5.2),anotherCS+thatwasfollowedbytheUSafteran
intervalof3s(CS+trace),andaCS-thatwasneverreinforced(Cornelisseet al,2014).ThreegreyscaledpicturesofneutralmalefacesservedasCS(Lundqvistet al,1998;Oosterhof&Todorov,
2008),theassignmenttoCS-typewascounterbalancedacrossgroups.Duringhabituation,
allCSwerepresentedtwice(4s)capturingtheorientingresponse,followedbyagrayscreen
(CSinterval,3s),andanintertrialintervalshowingafixationcross(11,12,or13s).Foracquisition,
participantswereinstructedtofindoutwhethertherewasarelationshipbetweenfacesand
shocks. Each CS was presented 26 times and both CS+ were reinforced with a shock (see
cue
CS+delay
CS+trace
0s
cue
interval
4s
interval
Skin conductance response at
learning, CS+ > CS-
cue
CS+delay
CS+trace
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.00
0.00
Stress
ITI
7s
11-13s
interval
CS+delay CS+trace
0.20
Control
ITI
MR-available
n.s.
5
MR-blocked
Figure 5.2. Top:Schematicoverviewofdelayandtraceconditioning.Bottom:Skinconductanceresponses
(SCR)revealedsuccessfulacquisitionofdelayandtraceconditioning.Left:SCRdataforthecueperiodshowed
successfuldistinctionbetweenCS-types.SCRwasgreaterforCS+delaythanforCS+traceandCS-,andstronger
forCS+tracethanforCS-.Right:SCRdataforthetraceintervalalsodifferedbetweenCS-types,withstronger
responsestoCS+trace-intervalandCS+delay-intervalthantoCS-interval.Althoughthestress/MR-availablegroupshowed
numericallygreaterresponsestotheCS+delay-intervalthanthecontrolgroup,theCS-type-by-stressinteractionfor
theCSintervalreachedonlytrendlevelsignificance.Itisimportanttonotethatthegroupsdidnotdifferintheir
responsetoCS-orCS-interval.Errorbarsdepict1SEM.CSconditionedstimulus,ITIintertrialinterval,MRmineralocorticoidreceptor,***p<0.001.
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Chapter 5
below) in 50% of the trials. A short break was inserted after half of the trials to obtain a cortisol
sample. On day 2, participants were again habituated and received the same instruction. All
CS were presented six times during recall, always ensued by CSinterval and fixation cross, but
without reinforcement. Trial timing was similar throughout all experimental phases; trial order
was pseudo-random with no more than two repetitions of the same cue.
Skin conductance measurements
On both days, SCR was measured using Ag/AgCl electrodes on the distal phalanges of the
left index and middle finger, which were cleaned before with warm water. The signal was
amplified using the MRI compatible BrainAmp ExG (BrainProducts GmbH) within the MRI environment and recorded using BrainVision Recorder software with a sampling rate of 5000Hz.
Unconditioned stimulus (US)
The shock was administered to the distal phalanges of index and middle finger of the dominant hand (200ms biphasic pulse with a pulse width of 250μs at 150Hz) with current intensity
set per participant before the experiment using a standardized shock workup. Participants
were presented with five sample shocks, starting at a low intensity and adjusting levels in
order to achieve an intensity that was rated as ‘quite annoying’, a rating of 4 on a scale from
1 (‘not annoying at all’) to 5 (‘annoying and painful’). After the final shock, the intensity was
adjusted once more to reach the intensity that would be used throughout the experiment
(Klumpers et al, 2010).
All behavioral and physiological analyses were performed in SPSS 19 (Armonk, NY: IBM Corp.).
Univariate or repeated measures ANOVA (rmANOVAs) were implemented to analyze behavioral and physiological data including the within-subject factors time and CS-type (for SCR),
and the between-subject factors stress and MR-availability. The alpha level was set to 0.05
for all analyses (two-tailed), Greenhouse-Geisser correction was applied when necessary. As
participants naive to MRI scanning can show a stress response to the scanning procedure
itself (Muehlhan et al, 2011) and our experimental groups differed incidentally in their percentage of naive participants [58% stress/MR-blocked, 50% stress/MR-available, 62% control/
MR-blocked, 25% control/MR-available], we included scanner naivety as covariate of no interest in all of our analyses, including the fMRI analyses. This approach was supported by the fact
that naive participants had both higher heart rate (p<0.001) and cortisol levels (p<0.05) than
non-naive participants.
Scores for negative mood, cortisol, heart rate, blood pressure during the experimental
phase were baseline corrected to the last measurement of the adaptation phase (-25min). For
SCR, we first removed MRI artifacts using the MRI Artifact Correction implemented in BrainVision Analyzer (version 1.05) with a sliding average of 15 volumes. Data were low-pass filtered
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at 5Hz and downsampled to 100Hz in MATLAB (The MathWorks, Inc., United States), using
FieldTrip (Oostenveld et al, 2011). As delay and trace conditioning lead to different timing patterns of conditioned responses (Balsam, 1984), we calculated SCRs for cue and CSinterval separately. We subtracted the average during 1s before cue onset from the peak during 3-5s after
onset and subtracted the average during 1s before CSinterval onset from the peak during 3-5s
after CSinterval onset. Of note, our baseline correction for the CSinterval gives us a clearer measure
for the CSinterval response, but is more conservative in that it might result in a smaller response
if a strong cue signal is subtracted. Using 1s before cue onset as baseline as implemented
in other studies led to similar or even stronger results. Negative responses were scored as
zero and all SCRs were square root transformed to account for skewness. We only analyzed
non-reinforced CS+ trials to avoid confounding effects of the US on SCR. Mean scores were
calculated for acquisition and early and late recall to capture the fast dissipating conditioned
response at delayed recall. To investigate a stress-induced shift in learning systems, we also
directly compared CS+trace and CS+delay, subtracting the CS- from both CS+ and analyzing the
resulting composite scores. As we were primarily interested in effects on differential delay and
trace conditioning, we focus on main effects and interactions involving the factor CS-type.
Magnetic resonance imaging and analysis
MRI measurements were acquired using a 1.5tesla Avanto Scanner (Siemens, Germany)
equipped with a 32-channel head coil. Blood-oxygenation-level-dependent (BOLD) T2*weighted multi-echo GRAPPA sequence (Poser et al, 2006) images were obtained with the following parameters: Repetition Time (TR)=2.14s, Echo Times (TEs)=9/21/33/44/56ms, 34 transversal slices, ascending acquisition, distance factor 17%, effective voxel size=3.3x3.3x3.0mm,
Field of View (FOV)=212mm. We used this multi-echo sequence for its improved BOLD
sensitivity and reduced artifacts, especially in ventral regions such as the amygdala (Poser
et al, 2006). We also acquired a high resolution T1-weighted anatomical image (TR=2.73s,
TE=2.95ms, 176 sagittal slices, FOV=256mm, voxel size=1.0x1.0x1.0mm).
All functional MRI data were analyzed using SPM8 (Wellcome Trust Centre for Neuroimaging, London, UK). For spatial realignment of the fMRI data, head motion was estimated on
the first echo using least-squares estimation and movement correction was applied to the
five echoes acquired for each excitation using six rigid-body transformation parameters. The
echo images per volume were then combined into a single volume using an optimized echo
weighting procedure (Poser et al, 2006) and coregistered to the structural image. The T1-image
was segmented into cerebral spinal fluid, white matter, and gray matter and used to normalize
functional and structural scans to MNI space with the unified segmentation procedure. Finally,
the images were spatially smoothed using an 8mm FWHM Gaussian kernel.
In line with earlier studies we focused on transient, learning-related activity (Büchel et al,
1999; Büchel et al, 1998; Marschner et al, 2008) which is supposed to decrease as soon as the
associations are learned (Büchel et al, 1999; Büchel et al, 1998; Marschner et al, 2008; Tabbert
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et al, 2005) and the US is reliably predicted (Boll et al, 2013; Li et al, 2011; McNally et al, 2011).
We expected learning-related activity in the amygdala for delay conditioning, and in the hippocampus for trace conditioning. For completeness, we also analyzed sustained activity related
to the expression of fear, which varies little over the course of the task and is usually found in the
anterior insula, anterior cingulate cortex (ACC), dorsomedial PFC (dmPFC), midbrain (Marschner
et al, 2008), and sometimes in the amygdala (Sehlmeyer et al, 2009). The first-level models contained the following predictors: for habituation regressors representing CS (4s) and CSinterval (3s),
for acquisition regressors modeling CStransient (CS+delay,transient, CS+trace,transient, CS-transient), CSinterval,transient
(CS+delay-interval,transient, CS+ trace-interval,transient, CS-interval,transient), and six equivalent regressors for
sustained activity (CSsustained, CSinterval,sustained). The transient predictors were constructed by multiplying each sustained regressor with a linear decaying function (Marschner et al, 2008). We
added regressors for instructions, shocks (0.2s), six realignment parameters, and a constant.
As the administration of shocks can lead to large and fast signal fluctuations (Klumpers et al,
2010), we included a regressor with the mean signal intensity per volume.
Comparable to our behavioral analysis, we first tested for brain regions differentiating
between the three CS-types during CS and CSinterval. We then tested a possible stress-induced
shift, directly comparing delay and trace conditioning. To identify brain regions showing
stronger learning-related activation to the CS+delay than to the CS+trace, we computed a contrast subtracting CS+trace,transient from CS+delay,transient. Analogous contrasts were computed for the
CSinterval and sustained activity. For explorative whole-brain analyses, the significance threshold
was set to pFWE<0.05 (cluster-level). For regions included in our a-priori hypotheses (bilateral
amygdala, hippocampus, insula, dmPFC), we implemented small volume correction (SVC),
using an initial threshold of p<0.005, uncorrected, followed by FWE-correction (p<0.05) for
multiple comparisons within ROIs. The amygdala mask was obtained similar to another study
(Schiller et al, 2013) based on the overlap of the contrast US>baseline at pFWE<0.05 and an
anatomical mask (Automated Anatomical Labeling (AAL) atlas, Tzourio-Mazoyer et al, 2002,
in Wake Forest University PickAtlas version 2.4). The amygdala reacts strongly to electrical
shocks (Klumpers et al, 2010), and by using a functional amygdala mask independent of our
task effects, we hoped to enhance sensitivity. Anatomical masks for hippocampus, insula, and
dmPFC were taken from the AAL atlas (for dmPFC, we combined supplementary motor area
and median cingulate).
Excluded participants and handling of missing data
Three participants had to be excluded from all analyses due to panic attacks during scanning or non-compliance with study instructions (participation in another drug study). Due to
excessive movement during scanning, five participants could not be used for fMRI analyses.
Technical failure resulted in another four participants which had to be excluded from fMRI
analysis and SCR analyses on day 1 and three participants on day 2. One participant (control/
MR-blocked) was removed from the cortisol analysis on day 1 because of faulty measure92
ments (last cortisol sample at 100min after stress and cortisol increase from 70 to 100 min
exceeded mean+3SD of the group). Another participant (stress/MR-available) had to be
removed from the analysis on negative mood on day 2 as his negative mood (but not cortisol)
score exceeded the mean+3SD due to a major life event that day. Remaining missing cortisol
data (seven non-adjacent measurements in six participants) were interpolated by averaging
across two neighboring measurements. For negative mood, missing items (six items in five
participants) were replaced by the individual mean score per time point.
Results
The experimental groups did not differ significantly in age, body mass index, or trait anxiety
(Table 3.1). The stress group immersed their foot in water less long than the control group
(F(1,93)=20.123, df=1, p<0.001), but importantly, there was no influence of MR-availability (no
main effect or interaction).
Stress measures adaptation phase
Decreases throughout the adaptation phase in negative mood, cortisol levels, heart rate,
and blood pressure indicated successful adaptation to the laboratory environment (all main
effects of time p<0.001). MR-blockade led to higher cortisol levels 25 min before stress onset
(time-by-MR-availability interaction (F(1.7,155.4)=13.333, p<0.001; t96=3.126, p=0.002) in line with
a regulatory role of the MR on HPA axis activity (de Kloet et al, 2005). Importantly, within medication groups there was no significant difference between stress and control in any measure
prior to stress induction (all p>0.1).
Stress measures experiment phase
Stress-related increases in negative mood, cortisol, and heart rate showed successful stress
induction in both medication groups (Figure 5.1).
Cortisol
Significant time-by-stress interactions in each drug group indicated stress-related increases
in cortisol over time (MR-available: F(2.5,114.4)=3.018, p=0.041; MR-blocked: F(2.3,105.6)=6.933,
p=0.001, over both drug groups: time-by-stress F(2.5,229.5)=8.927, p<0.001, main effect of stress
F(1,92)=13.004, p=0.001). This was supported by significant elevations of cortisol in the stress
compared to the control group during the task (from 15 min until 70 min, all p≤0.001) and, at
trend level, after the task (100 min, p=0.054). Furthermore, MR-blockade lead to heightened
cortisol levels (time-by-MR-availability: F(2.5,229.5)=6.217, p=0.001, main effect of MR-availability:
F(1,92)=15.013, p<0.001), but did not interact with the factor stress (p>0.4). The MR-available
groups had lower cortisol levels than the MR-blocked groups (time points 5 to 100 min after
stress, all p<0.05). No other significant group differences were found.
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Heart rate and blood pressure
As expected, stress induction increased heart rate over time (time-by-stress interaction
F(6.8,569.7)=3.096, p=0.004), with higher heart rate in the stress groups throughout early fear
acquisition (all p<0.05), except for minute 10, 14, and 15 (0.05<p<0.09), and at 100 min post
stress (p=0.046). Heart rate was not affected by MR-availability. Both systolic and diastolic
blood pressure were unaffected by stress and MR-availability.
Negative Mood
Stress induction led to a significant increase of negative mood over time in the stress groups
(time-by-stress-interaction F(2.4,218.4)=9.812, p<0.001, Figure 1). This was supported by more
negative mood in the stress groups as compared to the control groups at 5 min post-stress at
trend (p=0.096), and even stronger just prior to the conditioning task (45 min, p<0.001). We
further found a tendency for a time-by-stress-by-MR-availability interaction (F(2.4,218.4)=2.692,
p=0.060) with significant stress-related increases in negative mood only in the MR-available
group (F(2.2,93.5)=10.269, p<0.001), but not in the MR-blocked group. While statistically weak, this
finding is in line with a role for the MR in appraisal of novel situations (ter Horst et al, 2012b).
To conclude, stress induction was successful leaving stress levels elevated during fear
conditioning.
Successful acquisition of delay and trace conditioning
SCR data for the cue revealed successful differentiation between CS-types (F(1.8,163.6)=30.531,
p<0.001, Figure 5.2), i.e. greater SCR to CS+delay than to CS+trace and CS- (both p<0.001), and
stronger SCR to CS+trace than to CS- (p=0.003). Also during the CSinterval we found successful
differentiation of CS-types (F(1.5,135.9)=20.972, p<0.001) with stronger responses to CS+trace-interval
and CS+delay-interval than to CS-interval (both p<0.001), but no differences between CS+trace-interval
and CS+delay-interval. The lack of a difference between the CS+-intervals likely reflects the slow
nature of SCR and a response to the omission of an expected shock after the unreinforced
CS+delay (Dunsmoor & LaBar, 2012). Thus, participants successfully acquired both trace and
delay conditioned fear responses.
Subsequently, we tested whether stress affected fear expression on day 1 using the
composite score to directly contrast CS+delay and CS+trace. Importantly, the groups did not
differ in response to CS- or CS-interval (all p>0.1). Potentially supporting our hypothesis of a
stress-induced shift, we found a CS-type-by-stress interaction during CSinterval presentation at
trend-level (F(1,89)=3.737, p=0.056). However, no post-hoc test reached significance and no
influence of MR-availability was found.
Stress-induced shift on recall of delay and trace conditioning
Importantly, on day 2, we did not find any significant group difference in cortisol, negative mood, heart rate, or blood pressure (all p>0.05), supporting full drug wash-out and an
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recall, CS+delay > CS+trace
absenceofresidualstresseffects.Therefore,possiblegroupdifferencesduringrecallcanbe
readilyinterpretedasstressorMR-availabilityeffectsonlearningandconsolidation.
SCRdataduringcuepresentationatrecallwereinfluencedbybothMR-availabilityand,at
trendlevel,stress(CS-type-by-MR-availabilityinteraction:F(1.9,169.6)=3.352,p=0.039;CS-type-bystress-by-MR-availabilityinteraction:F(1.9,169.6)=2.827,p=0.063),whilewefoundnosignificant
differentiationofCS-typesoverall.ThegroupsdidagainnotdifferinresponsetotheCS-or
CS-interval (all p>0.1).To further elucidate the group differences, we analyzed the composite
scores(CS+minusCS-),confirmingthatthesedifferenceswerepresentinthedifferentialresponsetoCS+delayversusCS+trace(CS-type-by-MR-availabilityinteraction:F(1,87)=5.087,p=0.027;
CS-type-by-stress-by-MR-availability interaction: F(1,87)=5.144, p=0.026). When directly contrastingbothCS+,wefoundstrongerearlyrecall(firstthreetrials)oftheCS+delayascompared
to the CS+trace after stress in the MR-available groups (p=0.020), but no such difference in
theMR-blockedgroups(Figure5.3).Additionally,wefoundstrongerearlyandlaterecallof
theCS+delayascomparedtotheCS+traceinthestress/MR-availablegroupthanthestress/MR-
0.30
cue
early recall late recall
0.30
0.20
0.20
0.10
0.10
0.00
0.00
-0.10
-0.10
Control
Stress
interval
early recall late recall
#
#
MR-available
5
MR-blocked
Figure 5.3. Skinconductanceresponses(SCR)duringrecallonday2.DataareplottedasCS+delayoverCS+trace
withSCRtotheCS-andCS-intervalbeingsubtractedfrombothCS+andCS+interval,respectively.Thegroupsdid
notdifferinresponsetotheCS-orCS-interval.Itisimportanttonotethatindividualvariancewashighleading
toratherweakdifferentialrecall(FigureS5.1).Left:SCRduringcuepresentationwereaffectedbythefactors
MR-availabilityand,attrend,stress.Analyzingthecompositescoresconfirmedtheseeffects.Whendirectly
comparingCS+delayandCS+trace,wefoundstrongerSCRduringearlyrecalltotheCS+delayascomparedtothe
CS+traceafterstressintheMR-availablegroups,butnosuchdifferenceintheMR-blockedgroups.Furthermore,
SCRduringrecalltotheCS+delayascomparedtotheCS+tracewerestrongerintheinthestress/MR-available
groupthanthestress/MR-blockedgroup.Right:SCRduringintervalpresentationshowednodifferentiation
betweenCS-typesinthegeneralANOVA.However,thecompositescoresrevealedatrend-levelinfluenceof
bothstressandMR-availability,thusshowingasimilar,althoughweaker,patternastheanalysisonthecueperiod.ForanillustrationoftheresponsesrelativetoCS-seeFigureS5.1.Errorbarsdepict1SEM,CSconditioned
stimulus,MRmineralocorticoidreceptor,*p<0.05,#p<0.10.
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blocked group (early: p=0.012, late: p=0.017). This pattern of results supports our hypothesis
of a stress-induced, MR-dependent shift towards a dominance of reflexive delay conditioning.
The analysis on the SCR composite scores during CSinterval at recall (CS+interval minus CS-interval)
discerned a CS-type-by-time interaction and, at trend level, CS-type-by-time-by-stress and
CS-type-by-stress-by-MR-availability interactions in the same direction as for the cue (CStype-by-time-by-stress interaction: F(1,87)=3.624, p=0.060, CS-type-by-stress-by-MR-availability
interaction: F(1,87)=3.217, p=0.076). These results support again, though being statistically
weaker, an MR-dependent stress-induced shift towards better recall of delay than trace conditioning (Figure 5.3).
Neural mechanisms underlying learning
Brain regions showing transient activation to the CS+delay
After successful fear learning was confirmed at the physiological level, we investigated brain
regions involved in learning by modeling a response decaying over time. We observed a
transient bilateral amygdala response to the CS+delay (left: pSVC=0.024, k=37, Tmax=3.24; right:
pSVC=0.016, k=40, Tmax=3.58, Figure 5.4). However, we did not find the hypothesized differential
transient amygdala activity when testing for regions differentiating CS-types. This might be
explained by the fact that our stimuli were faces, which intrinsically activate the amygdala (as
opposed to geometric shapes, e.g. Marschner et al, 2008), possibly making it more difficult to
find differences in transient responses to the CS-types.
Brain regions showing transient activation to the CS+trace-interval
We found that activity in bilateral medial temporal clusters overlapping with the hippocampus
differed between CSinterval-types (four clusters; left: pSVC=0.001, k=89, F=14.68 and pSVC=0.063,
k=27, F=9.10; right: pSVC<0.001, k=95, F=19.41 and pSVC=0.005, k=65, F=12.26). When testing
specifically for regions showing a stronger transient response for CS+trace-interval than for CS+delayinterval, we again found extended hippocampal clusters (right: pFWE<0.05, left: pSVC<0.008, k=75,
Tmax=4.41, Figure 5.4).
Together, our findings confirm a transient role for the hippocampus during the trace
interval in trace conditioning (Büchel et al, 1999), and support evidence that the amygdala is
involved in encoding the cue for delay conditioning (Marschner et al, 2008).
Stress-by-MR-availability effects on fear learning related brain activity
We extracted data from the bilateral amygdala reactions to the CS+delay,transient (at p<0.005,
uncorrected), but the ANOVA on the parameter estimates for CS+delay,transient revealed no
effect of stress or MR-availability (Figure 5.4). However, a similar analysis on the bilateral
medial temporal cluster responding to the CS+trace-interval (at pFWE<0.05) revealed a stress-byMR-availability interaction (F(1,83)=4.573, p=0.035, Figure 5.4). Interestingly, stress decreased the
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Transient activation CS+delay
Transient activation interval+trace
3.5
amygdala
T
2.8
y=-17
Parameter estimates transient
activation CS+delay (a.u.)
No group differences
1.5
1.0
0.5
0.0
MR-available MR-blocked
n.s.
hippocampus
T
2.8
Stress-by-MR-availability interaction
Control
Stress
Parameter estimates transient
activation interv al+trace (a.u.)
y=-2
7.5
MR-available MR-blocked
1.5
1.0
Control
Stress
0.5
0.0
Figure 5.4. Top left:TransientactivationtotheCS+delayinthebilateralamygdala.Forillustrativepurposes,this
imageisthresholdedatp<0.005,uncorrected.Top right:Transientactivationtothetraceintervalwasfound
onlyinthebilateralhippocampus(pFWE<0.05).SimilarmedialtemporallobeactivationswerefoundwhentestingforregionsdifferentiatingthethreeCS+interval-types,orforregionsshowingastrongertransientresponse
toCS+trace-intervalthanCS+delay-interval.Bottom:ParameterestimatesforthecontrastCS+delay,transient(left)andCS+traceinterval,transient(right).Parameterestimateswereextractedfromtheclustershownontop.Wefoundastress-byMR-availabilityinteractionontheparameterestimatesforCS+trace-interval,transientbutnottheCS+delay,transient.Stress
decreasedhippocampallearning-relatedactivitytothetraceinterval,butonlyiftheMRwasavailable.This
effect was prevented in the MR-blocked groups, MR mineralocorticoid receptor. Error bars depict 1 SEM,
*p<0.05.
transienthippocampalresponseintheMR-availablegroupsindicativeoflesslearning-related
activity(p=0.031),butnotintheMR-blockedgroups.InlinewiththeSCRresultsatrecall,this
suggestsanMR-dependentstress-inducedimpairmentoftraceconditioningresultingina
relativedominanceofreflexiveformsoffearlearning.
Neural mechanisms underlying expression of fear
Inlinewithpreviousstudies,wefoundsustainedactivityinasetofbrainregionsoverlapping
a network consistently activated during the expression of conditioned fear, the so-called
saliencenetwork(pFWE<0.05,Figure5.5)includingtheamygdala(pSVC=0.021,k=16,Tmax=3.50)
fordelayconditioning,andthehippocampus(pFWE<0.05)fortraceconditioning.Thisactivity
wasnotsignificantlyaffectedbystressorMR-availability.
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Sustained activation during cue
Sustained activation during interval
CS+delay > CS+trace
CS+trace > CS+delay
dmPFC
dmPFC
10
13
ant. insula
ant. insula
T
3
T
3
Figure 5.5. Brain regions showing stronger activity to the CS+delay than the CS+trace during presentation of the
cue (left). We show activation of the dorsal medial prefrontal cortex (dmPFC), midbrain, anterior insula (ant.
insula), which are regions of the salience network, and the left amygdala. The right panel displays brain regions
showing stronger activation to the trace interval than the interval after the CS+delay. Again, we see activation of
the dmPFC, the midbrain, and anterior insula, but also the left hippocampus.
Discussion
We present results supporting a dominance of cognitively less demanding fear learning under
stress, depending on cortisol interacting with the MR. In line with our hypothesis, stress led to
a dominance of delay conditioning over trace conditioning in SCR at recall, paralleled by an
MR-dependent, stress-induced impairment of hippocampal fear learning during acquisition.
Previous studies investigating stress effects on fear learning have led to equivocal results
possibly due to differences in design, stress induction method, time interval between cortisol
increase and fear conditioning, and outcome measures (for a summary see Cornelisse et al,
2014). Nevertheless, as of yet no study investigated the effect of stress induction just prior to
a combined delay and trace paradigm including a later recall test, by which we could reveal a
stress-induced dominance shift in fear learning systems.
Stress-induced changes are brought about by different waves of neuromodulators (Joëls
& Baram, 2009). Initially, norepinephrine leads to activation of a neural salience network and
enhanced vigilance (Hermans et al, 2011). Activation of the HPA axis, conversely, results in
slower action of cortisol at glucocorticoid receptors (GR) and MRs. Both receptors mediate
rapid, non-genomic and slow, genomic effects (Joëls et al, 2012). It is assumed that rapid MRmediated effects are permissive, facilitating adaptive behavior in stressful situations, whereas
slow, mostly GR-mediated effects reinstall homoeostasis (de Kloet et al, 2008b; Groeneweg
et al, 2011). We extend findings that the MR, presumably via non-genomic pathways, is implicated in stress-induced shifts between multiple memory systems (Schwabe et al, 2010a;
Schwabe et al, 2013c; Vogel et al, 2015) by showing its crucial role in inducing a shift in fear
learning. However, genomic effects might have contributed in later stages of acquisition or
consolidation. Both GR and MR have been implicated in fear learning in rodents (Zhou et al,
2010; Zhou et al, 2011), but these studies did not include a comparison between stressful and
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non-stressful conditions, therefore allowing no conclusion about rapid or genomic effects.
Studies manipulating the timing between stress and task (Cornelisse et al, 2014) will help for a
better mechanistic understanding of stress effects on memory formation.
While there are reports on impaired hippocampal functioning (Cousijn et al, 2012; Pruessner et al, 2008; Schwabe et al, 2013c) and memory retrieval under stress (Roozendaal et al,
2006b; Schwabe et al, 2012b), stress often enhances encoding of declarative (item) memory.
In apparent contrast, we find impaired hippocampus-dependent fear learning under stress.
Fear conditioning differs substantially from standard declarative memory tasks in that the
same few stimuli are repeated, resulting in recurring encoding-retrieval cycles. This suggests
that when such encoding-retrieval cycles occur under stress, hippocampal impairment of the
retrieval component may disrupt stabilization of more complex associations. Interestingly,
this resembles the impaired contextualization of emotional memories associated with posttraumatic stress disorder (Acheson et al, 2012), and those induced by heightened cortisol
levels in rodents (Kaouane et al, 2012) and healthy adults (van Ast et al, 2013b).
Our findings support the hypothesis that trace conditioning poses additional demands
(e.g. working memory) and thus engages brain regions beyond those needed for delay conditioning (Büchel et al, 1999; Gilmartin et al, 2014; Knight et al, 2004; Raybuck & Lattal, 2014).
Although it is unclear how these regions interact while encoding temporal CS-US relationships, trace conditioning seems to require additional resources to encode the more complex
stimulus contingencies. The simpler stimulus-shock associations and concurrent sensory
inputs in delay conditioning, in contrast, can be encoded by the amygdala even without the
hippocampus (Bechara et al, 1995). Testing stress-effects on cognitively more demanding
forms of fear learning might therefore provide additional insight as they rely on different brain
structures and may be relevant for complex real-life situations.
Despite the strengths of this study such as the large sample size, its full-factorial design, a
task combining delay and trace conditioning, and a pharmacological manipulation enabling
us to investigate effects of stress depending on MR-availability, some limitations should be
considered.
Overall recall on day 2 in SCR was rather weak, possibly due to strong inter-individual differences and the complexity of the task. Nevertheless, we were able to observe meaningful
group differences related to stress and MR-availability. As spironolactone affects HPA axis
regulation (de Kloet et al, 2005), it might affect cortisol and corticotropin releasing factor levels. More cortisol will then be available for GR binding, thus shifting the balance between MR
and GR activation. While most rapid effects so far have been ascribed to the MR (Groeneweg
et al, 2011; Joëls et al, 2012), GR-activation might have played a role, too, especially in later
trials. Furthermore, spironolactone can also affect other receptors, for example progesterone
receptors (Schane & Potts, 1978).
It is also important to acknowledge that we only investigated male participants. A recent
study (Schwabe et al, 2013c) demonstrated the stress-induced shift in both sexes, suggesting
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that our finding might hold for females, as well. However, other studies found sex differences
in stress effects when investigating fear memory formation (Merz et al, 2013b). Furthermore,
the prevalence of anxiety disorders is higher in women (Kessler et al, 2005), suggesting sexspecific effects in stress and anxiety. Finally, stress effects might vary across the menstrual
cycle (Duchesne et al, 2012; Ossewaarde et al, 2010). Therefore, while our choice for testing
only males was deliberate given practical constrains to our sample size, a follow-up study
deciphering the mechanism of a stress-induced shift in females is needed to assure the generalizability of our findings.
Finally, we would like to emphasize a difference between the task we implemented and the
paradigms employed by earlier studies investigating a stress-induced shift between systems
supporting different types of spatial memory (Packard & Wingard, 2004; Schwabe et al, 2007;
Schwabe et al, 2010a; Wingard & Packard, 2008). In earlier studies, a test trial was used to identify which memory system dominated behavior, and it was assumed that these systems act
competitively. However, in our recall task, participants could demonstrate good performance
in both delay and trace conditioning, i.e. there was not necessarily competition between the
two systems. Therefore, we cannot readily conclude that the stress-induced increase in delay
conditioning is directly linked to a decrease in trace conditioning. Nonetheless, we observed
a relative shift in the activity balance between the two fear learning systems supporting a
comparative increase in cognitively less demanding fear learning. More research is needed to
gain a deeper understanding of the precise interactions of different fear memory systems in
humans.
Recent studies in other cognitive domains support that under or directly after stress individuals shift towards cognitively less demanding systems underlying behavior. Under stress
more reflexive responses (Porcelli & Delgado, 2009; Schwabe et al, 2011) and less strategic
decisions are made (Leder et al, 2013). Also in reinforcement learning stress reduces complex,
model-based contributions to behavior (Otto et al, 2013). Together, these studies suggest that
stress leads to a rapid shift in neural processing, resulting in a dominance of less demanding
systems in a broad range of cognitive domains.
This stress-induced shift might prove relevant for psychiatric disorders involving welllearned maladaptive behaviors or cognitive rigidity. For example, anxiety or stress can lead to
relapse in drug addiction (Herman & Polivy, 1975; Weiss et al, 2001), and it is conceivable that
this might hold for obsessive-compulsive disorder, too. The shift might also have implications
for post-traumatic stress disorder, which is assumed to result from excessive fear learning
under stress and is characterized by impaired hippocampal functioning (Acheson et al, 2012).
Being able to place memories in the right context and to retrieve those memories which
are applicable in a specific context is an important hippocampal function which is impaired
in PTSD and schizophrenia (Maren et al, 2013). As of yet, no studies have been conducted
to specifically target a stress-induced cognitive shift in patient populations. However, if our
findings hold in patient samples, we suggest that the shift is dependent on MR-activation
100
and might therefore be prevented by short-term administration of MR-antagonists. Related
to this, recent studies associated genetic variants in the gene encoding the MR with interindividual differences in risk for psychopathology (Bogdan et al, 2012; DeRijk et al, 2011; Klok
et al, 2011b; Vogel et al, 2014). Our finding could therefore have mechanistic implications for
our understanding of the impact of stress on daily life and mental well-being, which might be
particularly prominent in individuals with high MR sensitivity.
(NWO) awarded to GF, MJ, MO, and HJK (433-09-251). Supplementary information will be available
via http://www.biologicalpsychiatryjournal.com/.
5
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Chapter 5
Forcompleteness,FigureS5.1illustratesSCRforbothCS+delayandCS+traceforearlyrecallonday
2forbothcue(left)andCSinterval(right).Forthisfigure,wefocusedonearlyrecall,asthestressby-MR-availabilityeffectsweremostpronouncedhere.Thiswastobeexpectedasextinction
occursduringlongerrecallwithoutreinforcement,leavinglittleroomforgroupdifferences.
early recall, CS+ > CS-
cue
CS+delay
CS+trace
interval
CS+delay CS+trace
0.20
0.20
0.10
0.10
0.00
0.00
-0.10
-0.10
Control
Stress
MR-available
MR-blocked
Figure S5.1. Skinconductanceresponses(SCR)atearlyrecallonday2,forbothCS+delay>CS-andCS+trace>CS-
duringcue(left)andCSinterval(right)presentation.ThegroupsdidnotdifferinresponsetotheCS-orCS-interval
(allp>0.1).Itisimportanttonote,thatindividualvariancewashighandtheoverallrecallwasratherweak.Error
barsdepict1SEM,CSconditionedstimulus,MRmineralocorticoidreceptor.
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General Discussion
General Discussion
Stress is an important aspect of everyday life and we have a complex stress response in
place to overcome the demands posed by stressful events. Although the action of the stress
systems is usually adaptive, stress has been implicated in a variety of psychiatric and somatic
diseases. In this thesis, we set out to gain a better understanding of one specific part of the
stress system, namely the mineralocorticoid receptor (MR) for glucocorticoids.
Firstly, we set up a genetic study to investigate an association between the NR3C2 gene
coding for the MR and a risk factor for stress-related mental disorders. Secondly, we used
a pharmacological approach to test the hypothesis of a stress-induced shift away from
controlled, goal-directed processing towards more automated and habitual processing,
depending on MR-availability, in three cognitive domains. In the experiments summarized
in this thesis, we present work corroborating the suggestion that genetic differences in MRfunctioning may be related to psychopathology or resilience. Furthermore, we support an
involvement of the MR in mediating a stress-induced shift towards more automated systems
underlying learning and behavior.
This general discussion commences with a summary of the aims, main findings, and
conclusions of each study presented in the previous chapters. Thereafter, limitations and
remaining open questions will be discussed. Finally, I will consider possible implications of our
work for clinic and society.
Summary of findings
Genetic variants of the MR
In chapter 2, we investigated innate variations in MR functionality, i.e. common variants in
the gene NR3C2 coding for the MR. We could show that inter-individual differences in the
susceptibility for stress-related mental disorders relate to variations in NR3C2. Common variations in this gene were associated with a stronger bias remember negative words, especially
if these words described self-relevant concepts (such as ‘pessimistic’). This enhanced memory
for negative information is called ‘negative memory bias’ and assumed to be a risk and maintenance factor for depression (Chan et al, 2007; Leppanen, 2006; Mathews & MacLeod, 2005;
van Oostrom et al, 2013). The association between NR3C2 variants and negative memory bias
was stronger in participants with a lifetime history of adverse events, who are in general at a
higher risk to develop psychiatric disorders (Beck, 2008). In contrast to other studies focusing
on single variants in a given gene and thereby necessarily ignoring other loci in this gene, we
used a newer statistical approach to test all measured (i.e. genotyped) variation in NR3C2 for
an association with negative memory bias in a large number of participants. This set-up allowed for an unbiased identification of gene loci associated with enhanced negative memory
bias. We then further explored one single nucleotide polymorphism (SNP) which was known
to be functional and significantly associated with negative memory bias in our sample, again
in interaction with lifetime adversity. Although we should be cautious not to over-interpret
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results from a single genetic association study (Li & Meyre, 2013), our finding was partially
replicated and extended in participants who had previously suffered from depression (Vrijsen
et al, 2015). This was an important addition to our initial finding as we had investigated a
sample of healthy volunteers consisting mainly of university students who are a selected,
rather homogeneous, and high-functioning population (Henrich et al, 2010).
Several other studies also investigated genetic variants (polymorphisms and haplotypes,
i.e. the combination of polymorphisms which are inherited together) in the genes coding for
MR and GR. These studies found associations between genetic variants and the risk for stressrelated mental disorders or phenotypes that can be linked to these disorders. Concerning
the MR, individuals carrying genetic variants that are assumed to result in a loss of function
showed higher depression rates and stronger hopelessness (Klok et al, 2011b), neuroticism
(DeRijk et al, 2011), HPA axis responsiveness (van Leeuwen et al, 2011), and amygdala reactivity
to salient stimuli (Bogdan et al, 2012). Just like our experiment, these human studies were
cross-sectional, observational in nature, and relied on questionnaire data to assess stressful life
events (when life adversity was included). These cross-sectional, subjective data make it hard
to distinguish causation from correlation, which is an important limitation to bear in mind.
Nonetheless, a multitude of studies now suggest a link between genetic variation of the MR
on the one hand and cognition and well-being on the other hand.
With the behavioral genetics setup implemented in chapter 2 we can only speculate about
the underlying neural mechanism of how the MR genotype might affect cognitive functions.
One should keep in mind that the different parts of a gene are often inherited together.
Therefore, we cannot be certain whether the functional variation we found to be associated
with an enhanced risk for stress-related mental disorders (rs5534) was a causal variation or
merely served as a marker for related variants which might have exerted causal effects. rs5534
is a nonsynonymous variation, i.e. it does not change the sequence of amino acids which
make up the MR. However, rs5534 is present in an upstream basepairing site for a microRNA
(Nossent et al, 2011). The authors of this in-vitro study reported that the A allele of rs5534
was associated with increased micro-RNA-induced repression of MR expression (personal
communication, November 4, 2013). The expected net-effect would be less MR expression
or stronger downregulation in A-carriers, who also have stronger negative memory bias and
are therefore assumed to be at risk for depression (van Oostrom et al, 2013). On a neural level,
negative memory bias is associated with enlarged amygdalae and smaller hippocampi in
healthy volunteers (Gerritsen et al, 2011). Both of these structures are involved in emotional
memory and stress-related mental disorders. The authors interpreted their findings such that
individuals with higher negative memory bias might be more susceptible to stress, which
might in turn increase amygdala volume (Hölzel et al, 2010) and thereby further increase the
risk to develop psychopathology.
Several studies described above also support the model that the MR has protective functions, at least in women (but see Hlavacova & Jezova, 2008; Wang et al, 1995; Wang et al,
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General Discussion
2008). According to this model, more MR expression or better function is linked to a lower
risk for psychopathology. Conversely, the less MR is expressed or the lower its functionality,
the higher the risk may be for psychiatric disorders. This model was supported by the few
post-mortem studies investigating MR-expression in patients. In line with the findings from
genetic variants resulting in loss-of-function, they found decreased MR-expression in the
hippocampus and prefrontal cortex of depressed patients as compared to healthy controls
(Klok et al, 2011a; Lopez et al, 1998; Medina et al, 2013; Qi et al, 2013; but see Pandey et al,
2013). Furthermore, pharmacological MR-activation was shown to result in faster responding
to antidepressant treatment in patients with major depressive disorder, however, the neural
mechanism underlying this treatment efficiency enhancement is still unclear (Otte et al, 2010).
Interestingly, a decrease in hippocampal and amygdala MR-expression was also found after
chronic unpredictable stress and early maternal separation in rats (Cotella et al, 2014; Lopez et
al, 1998), which are assumed to be animal models predisposing individuals to depressive-like
behavior. In line with the idea of the MR being protective, long-term treatment of rats with
different types of antidepressant drugs led to upregulation of hippocampal MRs (but also
GRs, Brady et al, 1991; Brady et al, 1992). Finally, activation of MRs was also shown to be able to
prevent hippocampal cell death caused by GR activation (Crochemore et al, 2005).
All in all, these findings imply the MR in resiliency with higher expression levels being
protective and lower expression levels potentially affecting neuronal integrity and mental
health (ter Heegde et al, 2015). However, it should be noted that not all findings are in line
with this model. For example, Wu and colleagues found that MR-blockade rather than MRactivation was able to reduce glucocorticoid induced depressive-like behavior in mice (Wu
et al, 2013). Nonetheless, the majority of current evidence summarized above suggests that
increased MR-expression is associated to better health outcomes. In line with this idea, we
could show that a supposedly stronger downregulation of MR-expression is related
to enhanced risk for depression in a large sample of healthy individuals. Future studies using a combination of genetics and neuroimaging will be an important addition to the
rodent literature in order to deepen our understanding of the link between genes, receptor
expression, brain function, and risk for disease in humans. One interesting example for such a
study was conducted by Bogdan and colleagues (2012) who could show that an MR polymorphism leading to a mild loss-in-function is associated to enhanced amygdala activity to salient
stimuli which might in turn be a risk factor for stress-related mental disorders (Godlewska et
al, 2012).
Acute activation of the MR: the shift theory
Complementing the genetic approach in chapter 2, we used a pharmacological set-up to
investigate the role of the human MR in acutely stressful situations. Until recently, research
on the effects of stress-induced cortisol increases was mainly focused on the other receptor
type, the GR. Only within the last few years more research has been conducted aiming at
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unraveling the role of the MR in the stress response. The three projects described in chapter
3, 4, and 5 were set up to test for a stress-induced shift towards habitual processing, which
was hypothesized to depend on MR-availability (Schwabe et al, 2010a). Given earlier findings
involving the MR in emotional processing, spatial memory, and fear memory, we focused on
investigating a stress-induced shift affecting these three cognitive domains.
In chapter 3, we investigated how stress and MR-availability affect amygdala sub-region
connectivity directly after stress onset. Studies in rats had previously suggested that the
stress-induced shift towards habitual processing is driven by the amygdala (Packard & Teather,
1998; Packard & Wingard, 2004; Wingard & Packard, 2008). A later behavioral study in mice
indicated that this stress-induced shift might be mediated by cortisol interacting with the
MR (Schwabe et al, 2010a). Complementing these findings in rodents, we found that stress
induction led to an immediate increase in connectivity between the amygdala and the dorsal
striatum. While the amygdala is a crucial structure in emotion processing (LeDoux, 2000), the
dorsal striatum is known to support habit learning and automated behavior (Yin et al, 2004).
We could show this shift in a task probing the processing of salient stimuli (emotional faces),
thereby demonstrating that the shift might be general and not limited to memory tasks.
Importantly, we could refine earlier reports by showing that the enhanced connectivity with
the striatum was confined to the centro-medial amygdala. Previous rodent studies performed
by Packard and colleagues, in contrast, suggested a causal role of the basolateral amygdala in
the stress-induced shift in (spatial) memory (Packard & Teather, 1998; Packard & Wingard, 2004;
Wingard & Packard, 2008).
We carefully interpreted our finding of enhanced amygdala-striatal connectivity as a stressinduced shift of neural resources towards systems supporting habitual, automated behavior
and memory. In line with our hypothesis, the stress-induced shift depended on MR-availability
and was absent in the MR-blocked groups. Corroborating our hypothesis of a stress-induced
shift mediated by MR-activation, the strength of amygdala-striatal connectivity was associated
with the stress-induced cortisol increase in the MR-available group but not the MR-blocked
group. Our results thus support and extend earlier rodent data arguing for a stress-induced
shift towards neural systems supporting habitual processing. This shift appears to be mediated
by MR-activation leading to a change in functional coupling between the amygdala and the
dorsal striatum. It remains to be seen whether the different findings concerning the involved
amygdala sub-regions are due to different task domains, species, techniques, or other factors.
Nonetheless, a role for the amygdala in the stress-induced shift can be inferred from both
rodent and human data.
To summarize, chapter 3 supported our hypothesis that the MR mediates an amygdaladependent, stress-induced shift towards the striatum, supposedly favoring well-learned
habit-like responses over more controlled and flexible behavior. In the following two projects,
we tested whether this stress-induced shift depending on MR-availability would also affect
tasks explicitly targeting memory formation, as suggested by earlier studies conducted in
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General Discussion
rodents. Furthermore, the study presented in chapter 3 did not yield any behavioral effects
of stress and MR-blockade which we expected to be more pronounced when using more
difficult tasks in order to avoid ceiling effects.
In chapter 4, we investigated the stress-induced shift in spatial learning and its possible dependence on MR-availability. The spatial memory domain was at the focus of the
early rodent studies reporting a stress-induced shift between memory systems (e.g. Packard
& Teather, 1998). They demonstrated that animals were biased towards striatum-dependent
stimulus-response learning upon stress or amygdala activation. On the contrary, place learning based on the hippocampus was impaired in these animals. Later behavioral studies in
humans supported a stress-induced shift between spatial memory systems (Schwabe et al,
2007). However, the underlying neural mechanism remained unclear. As outlined above,
evidence pointed at a role of the MR in shifting the contributions of spatial memory systems
in mice (Schwabe et al, 2010a), and the amygdala appeared to have an orchestrating function.
We therefore hypothesized a crucial role of the MR in a stress-induced shift towards enhanced
stimulus-response learning in humans, orchestrated by the amygdala.
In line with earlier findings, we could dissociate two systems supporting spatial memory using fMRI (Doeller et al, 2008). While stimulus-response learning towards the landmark involved
the dorsal striatum, place learning towards the boundary activated the hippocampus. In line
with our hypothesis, we found that stress led to a relative enhancement of stimulus-response
learning in behavior. We could show that participants with stronger cortisol increases after
stress showed an enhancement of amygdala activity, in line with a model of the amygdala
orchestrating a stress-induced shift towards habit memory. Just as for chapter 3, we found
that stress enhanced amygdala-striatal connectivity and that the stress-induced shift could
be prevented by an acute administration of spironolactone. Together, our findings supported
again a role for the human MR in a stress-induced shift towards habit memory. This shift also
affects spatial memory systems and appears to be orchestrated by the amygdala, which in
turn modulates memories encoded in other brain regions.
Finally, in the last study (chapter 5), we investigated whether stress and MR-availability
affect how we acquire fear memories. Given that exaggerated fear learning during stressful
life events is thought to be of crucial importance for the development of stress-related mental
disorders (Mineka & Zinbarg, 2006), a better understanding of the mechanisms underlying this
association might lead to new insights on how stress-related mental disorders arise. Nevertheless, there are surprisingly few studies testing the effects of stress on the acquisition of fear
memories in humans (for exceptions, see for example Merz et al, 2013a; Merz et al, 2010; Merz
et al, 2013b). Furthermore, if stress effects on fear learning were investigated, usually rather
simple forms of fear learning were tested. The hypothesis of a stress-induced shift, however,
concerns the contribution of different systems to behavior and memory formation. To test
the shift hypothesis in fear learning, we used a paradigm which allowed us to distinguish
between more cognitively demanding fear learning supported by the hippocampus and less
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demanding, more automated fear learning supported by the amygdala. We achieved this aim
by combining delay conditioning, a simpler form of cue-shock association learning based on
the amygdala, with trace conditioning, a more complex form of association learning which
necessitates the hippocampus (see also Cornelisse et al, 2014). By implementing both types of
fear learning, we also hoped to better model the more complex fear learning occurring under
stress in real-life situations.
In line with the model of a stress-induced shift towards more automated forms of learning,
we could show that stress led to a relative dominance of more automated fear memories
twenty-four hours after learning relative to cognitively more demanding fear memories. This
was paralleled by a reduction of learning-related activity in the hippocampus under stress,
while learning-related activity in the amygdala was not affected. As for the stress-effects on
emotional processing and spatial memory formation, we only found these stress-induced
changes in the groups with available MRs. Blocking the MR with spironolactone prevented
this shift towards more automated learning, arguing again for a crucial role of this receptor in
mediating a stress-induced shift which affects emotional processing, spatial, and fear learning.
Our finding can be related to previous reports on impaired hippocampal functioning in
patients with PTSD (Acheson et al, 2012), schizophrenia (Maren et al, 2013), or after cortisol
administration in healthy participants (van Ast et al, 2013a). Considering these findings in
patients, it seems important to study both cognitively demanding and less demanding types
of fear learning and the way they are affected by stress. Combining different types of fear
learning allowed us to show that fear learning does not merely get better or worse under
stress. Instead, specific systems underlying cognitively more demanding fear learning are
impaired, leading to a dominance of less demanding forms of fear learning. This might in
turn explain the enhanced responding to salient cues and the impaired contextualization of
memories observed in PTSD patients.
Together, the results presented in the pharmacological studies support the existence
of a stress-induced shift from flexible, controlled processing towards more automated
and habit-like systems underlying human behavior and cognition (Figure 6.1). In line
with our hypothesis, this shift appears to be mediated by cortisol interacting with the MR for
all three cognitive domains investigated. Moreover, together with other experiments (Packard
et al, 1989; Packard & McGaugh, 1992; Packard & Teather, 1998; Schwabe et al, 2013c; Wingard
& Packard, 2008), our results support the idea that this shift is orchestrated by the amygdala.
Cortisol increases activity of the amygdala and its crosstalk with the striatum while impairing
hippocampal contributions to behavior.
So far, I have summarized and discussed the main findings outlined in the empirical
chapters. In the next part, I will integrate the findings of this thesis into the literature on glucocorticoids and their actions mediated by MR and GR.
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General Discussion
Stress
via MR-activation
‘Cognitive’, controlled systems
‘Habit-like’, automated systems
- Cognitively more demanding
- supported by hippocampus and
prefrontal cortex
- Cognitively less demanding
- supported by amygdala and striatum
Affecting:
processing of emotional stimuli (chapter 3)
spatial memory (chapter 4)
fear memory (chapter 5)
Figure 6.1. In summary, our findings in chapter 3, 4, and 5 support a stress-induced shift from ‘cognitive’, controlled processing towards ‘habit-like’, automated systems underlying human behavior and cognition. This shift
appears to be mediated by cortisol interacting with the mineralocorticoid receptor (MR).
Integration of findings
In the chapters described in this thesis, we investigated the effects of genetic variation, acute
activation, and blockade of the mineralocorticoid receptor for cortisol. Up until now, the exact
MR-mediated neural effects, which ultimately lead to the observed memory bias and stressinduced shift affecting brain activity, connectivity, and behavior, remain largely unknown.
Regions in the rodent brain where the MR was shown to be expressed and can therefore
affect neural functioning are the hippocampus, amygdala, prefrontal cortex, lateral spetum
(DeKloet & Reul, 1987), and striatum (Marit Arp, unpublished observation). In humans, only
few post-mortem studies investigated MR-expression but the pattern seems to be similar to
that seen in rodents, i.e. expression at least in the hippocampus, amygdala, and prefrontal
cortex (Klok et al, 2011a; Lopez et al, 1998; Medina et al, 2013; Seckl et al, 1991; Watzka et al,
2000a; Watzka et al, 2000b). Interestingly, we found MR-related effects in overlapping brain
regions. We could show that genetic variants in the MR are associated to a trait linked to
increased amygdala volume and decreased hippocampal volume. Acute stimulation of the
MR in humans appears to enhance amygdala activity and connectivity to the striatum, while
decreasing activity in the hippocampus (however, in rodents, an increase of hippocampal activity was found, Karst et al, 2005). This suggests that the consequences of MR-activation differ
between brain regions. These differential effects on structure and function in different brain
regions should be investigated in more detail for a better understanding of stress-induced
changes in cognition.
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When trying to understand the consequences of MR and GR activation it is important
to also consider recent findings that both receptors can mediate rapid, presumably nongenomic, and slow genomic effects. Rapid effects, mainly mediated by the MR, are assumed
to be permissive for stress effects (Groeneweg et al, 2011; Schwabe et al, 2012b). These
non-genomic MR-mediated effects can interact with norepinephrine to rapidly enhance
excitability of the amygdala and the hippocampus (Karst et al, 2005; Karst et al, 2010). How
these rapid MR-mediated neuronal changes translate to the large-scale differences in neural
activity and connectivity we found using fMRI in humans remains unclear for now and should
be further investigated. Slow, mainly GR-mediated effects appear to reverse the effects of
rapid MR-activation in order to reattain baseline levels and suppress the encoding of new
information. It is assumed that the fast, non-genomic effects develop within minutes, whereas
the slow, genomic effects need about an hour to develop. Considering this time scale, the
stress-induced shift described in this thesis is likely initiated by rapid MR-mediated effects
via non-genomic pathways. For the stress-induced effects observed using the fear memory
task (chapter 5) which started 45 minutes after stress induction, genomic effects might have
contributed at later stages of the task. However, given that we found the stress-induced
shift also in the earlier tasks, we can conclude that the stress-induced shift towards habitual
control over behavior appears to be initiated by rapid corticosteroid action mediated by the
MR. Although we did not study the slow effects of cortisol on cognition in the present set
of experiments, it can be assumed that the shift would be reversed after approximately one
hour, possibly mediated by GR-activation (Hermans et al, 2014b). Studies further investigating
the precise effects of MR and GR activation will help to unravel the detailed time course of
glucocorticoid action in the human brain.
It remains to be investigated how genetic differences in MR-functioning affect the stressinduced shift. We showed that variants presumably resulting in less MR-expression are associated to enhanced negative memory bias, which is in turn related to enlarged amygdalae and
smaller hippocampi (Gerritsen et al, 2011). Such changes in brain structure could predispose
vulnerable individuals to enhanced responding of the amygdala, stronger stress-related shifts,
or a trait-like bias towards relying more on reflexive, automated systems as shown for individuals suffering from chronic stress (Schwabe et al, 2008a). While we did not study how genetic
variation affects the stress-induced shift, such research might explain the inter-individual differences in responses to stress and the strength of the stress-induced shift. Furthermore, they
might relate to psychopathology by enhancing learning and recall of maladaptive behaviors
to salient cues in vulnerable individuals.
Open questions, limitations, and outlook
In the next part, I will consider limitations of the projects described in this thesis, remaining
open questions, and possible future research avenues.
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General Discussion
In chapter 2, we used a genetic approach to investigate the effects of differences in MRfunctioning. This approach is assumed to yield stable links between inter-individual differences in the genetic code and specific behavior or brain phenotypes, which can in turn be
associated with diseases. However, it was recently discovered that there is an additional layer
between the genetic code and how it is read out into proteins. This new field of research
raising high hopes involves epigenetics and complements classic genetic association studies.
Epigenetic changes concern long-lasting alterations in the transcriptional characteristics of
cells (i.e. the proteins produced) without changing the genetic code itself. These changes
can enhance or repress the readout of genes and thereby affect the functioning of all cells,
but leave the genetic code (i.e. what we investigated in chapter 2) unchanged. Very recently,
it was found that acute stress can change epigenetics (Hunter et al, 2014). Strikingly, some
epigenetic stress effects can even be passed on to the next generations (Gapp et al, 2014a;
Gapp et al, 2014b). Gapp and colleagues found that stress during pregnancy affects hippocampal MR-expression, metabolism, and behavior of at least two following generations, while
leaving the genetic code and GR-functioning unchanged. Therefore, not only can traumatic
life stress affect MR-functioning of the individual, but these effects can be passed on to future
generations. Concerning chapter 2, it is important to notice that we have not investigated
any epigenetic changes. Studying epigenetics in addition to differences in the genetic code
might reveal a more comprehensive view on how disorders arise. While at least some epigenetic signatures appear to be tissue-specific, recent studies indicated a link between trauma,
epigenetics, and brain function in humans (Mehta et al, 2013; Nikolova et al, 2014). More
research into the causes, effects, and possible reversibility of epigenetic changes after stress
might therefore help in designing new treatment or prevention strategies.
Concerning the pharmacological studies described in this thesis, it is important to
acknowledge that we only investigated male participants. A recent study by Schwabe and
colleagues (Schwabe et al, 2013c) demonstrated the stress-induced shift in both male and
female participants, suggesting that our findings may hold for females, too. However, other
studies found sex differences in stress (hormone) effects when investigating face processing
(Schwabe et al, 2013a), fear memory (Merz et al, 2013b), and spatial memory (Guenzel et al,
2014). Moreover, stress effects might vary across the menstrual cycle (Duchesne et al, 2012;
Ossewaarde et al, 2010) and the prevalence of stress-related mental disorders is higher in
women (Kessler et al, 2005), suggesting sex-specific effects in stress, depression, and anxiety.
Finally, also the human studies on the effects of MR-haplotypes and the rodent studies on MRknockout and overexpression models found that males and females are differentially affected
by variations of MR-expression (e.g. Klok et al, 2011b; ter Horst et al, 2012a; ter Horst et al, 2014;
van Leeuwen et al, 2010). A follow-up study deciphering the mechanism of a stress-induced
shift in females over different cycle phases is therefore needed to assure the generalizability
of our findings.
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Another factor one has to keep in mind when interpreting studies with an acute stress
intervention in humans is that, given ethical considerations, the stress induced is rather mild
compared to real-life trauma. Also the stress levels induced in rodent experiments are likely
stronger than those experimentally induced in humans, if only because in rodents participation is inherently unpredictable. The effects of real-life traumatic stress might differ compared
to the effects of an acute mild stress induction in the laboratory (Deppermann et al, 2014).
Also the consequences of chronic or prenatal stress might deviate from the effects of a short
acute stress induction. It was found that participants suffering from chronic stress or having
experienced prenatal stress are prone to rely more on automated memory systems (Schwabe
et al, 2012a; Schwabe et al, 2008a). These studies suggest that chronic stress might lead to a
general, trait-like shift towards more automated and habitual systems. While the role of the
MR in these long-term changes is still unclear, we could show that stable variations in MRfunctioning can induce a bias towards enhanced emotional memories. Unraveling the precise
neural mechanisms and the role of the MR in chronic, prenatal, and traumatic stress will be
an important next step given that these are important predictors of stress-related mental
disorders (Hill et al, 2012; Joëls et al, 2012; Krugers et al, 2010; Marin et al, 2011).
A complicating factor when trying to understand acute stress effects specifically on
memory concerns the different stages of memory. Stress can lead to very different outcomes
depending on whether encoding, consolidation, retrieval, or reconsolidation of memory
is affected. Stress is assumed to rapidly enhance memory encoding and consolidation by
inducing a so-called ‘memory formation mode’ (Schwabe et al, 2012b). Rapid non-genomic
effects of corticosteroids are assumed to interact with norepinephrine to strengthen the
encoding of events occurring in the stressful situation. Later on, the consolidation of these
memories acquired under stress is enhanced by slow, genomic corticosteroid effects which
induce a ‘memory storage mode’. During this time window (i.e. at some time after stress
onset) the encoding of new information is impaired in order to avoid interference with the
memory trace encoded during the earlier stressful encounter (Schwabe et al, 2012b). Stress
immediately before memory recall usually impairs retrieval of information (de Quervain et
al, 1998; Merz et al, 2014). The effect of acute stress before reconsolidation has not yet been
sufficiently investigated in humans (Raio & Phelps, 2015), but evidence from highly anxious
individuals suggests that arousal might protect fear memories from being disrupted after
reconsolidation (Soeter & Kindt, 2013). To summarize, depending on which stage a memory
is in and the precise timing of stress induction with respect to these stages, stress can have
either enhancing or impairing effects on memory. In our pharmacological studies stress was
induced shortly before memory acquisition. We demonstrated that stress did not enhance
all memories encoded (and consolidated), but actually impaired memories relying on the
hippocampus while sparing or even enhancing memories depending on the amygdala and
the striatum. However, we did not test whether the stress-induced shift we observed would
also change the balance of memory systems during retrieval or reconsolidation. Such a bias
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General Discussion
towards habitual memory systems following amygdala activation just before recall was found
in rats (Elliott & Packard, 2008) and might also be present in humans. Furthermore, also with
our genetic study, we cannot decipher which memory stage was affected by genetic variants
in the MR, ultimately leading to an enhanced negative memory bias. To test the effects of
stress and MR-variants on different stages of memory will be an interesting and important
follow-up on the findings described in this thesis.
Not only the specific timing of stress relative to a memory’s current stage can have differential effects on glucocorticoid action. Also the time of day might change glucocorticoid
effects. Glucocorticoid levels vary naturally over the course of the day, displaying both a circadian (Bartter et al, 1962) and an ultradian rhythm in healthy individuals (Lightman et al, 2008;
Sarabdjitsingh et al, 2010; Sarabdjitsingh et al, 2012). The circadian rhythm leads to heightened
glucocorticoid release at awakening and then slowly decreasing levels throughout the day
with rather stable and relatively low concentrations in the late active phase. The stability of
cortisol levels in this phase (the afternoon in humans) is the reason why most human studies
with a stress induction, including ours, are conducted in that time window. While enhancing
the internal validity of these studies and the comparability between studies, this focus on a
specific time of day might limit the generalizability of the results.
Underlying the circadian rhythm, the ultradian rhythm is apparent in hourly pulses of cortisol secretion with variable amplitude, leading to rising and falling cortisol levels alternating
rapidly. Importantly, these fast alterations were shown to affect stress responsiveness in rats,
with the rising phase leading to stronger stress reactions than the falling phase (Sarabdjitsingh
et al, 2010; Windle et al, 1998). So far, the effects of ultradian rhythms in corticosteroid release
on stress reactivity are not well understood in humans. Future studies are necessary to unravel
the possible effects of circadian and ultradian rhythms on stress-induced changes in human
behavior and brain function.
As outlined above, this thesis had a specific focus on the effects of glucocorticoids. It
should be noted, however, that it is highly unlikely that memory bias or the stress-induced
shift are solely affected by MR functioning. The stress response is much more complex and
many other messengers as well as their interactions play a role in stress-induced effects. Other
neuromodulators released under stress include among others norepinephrine, dopamine,
serotonin, CRH, ACTH, endorphins, enkephalins, vasopressin, and other neuropeptides. The
release of yet other neuromodulators and hormones such as testosterone, growth hormone,
or insulin is decreased under stress. All these modulators, whether increased or decreased
under stress, might in turn change behavior and brain function.
A study arguing against the fact that cortisol can induce a stress-induced shift in isolation
was conducted by Schwabe and colleagues (2012c). They found a shift towards habitual
processing only when administering both hydrocortisone and yohimbine together, the latter
being a drug to enhance norepinephrine activity. The shift was not found when administering
either drug alone. Other findings even suggest that norepinephrine in itself can induce a
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Chapter 6
shift towards striatal learning (Packard & Wingard, 2004) and enhanced salience processing
(Hermans et al, 2011). Also in the studies using acute stress induction in this thesis (chapter 3,
4, and 5), the norepinephrinergic system was likely more activated in the stress groups than
in the control groups as indicated by heightened heart rate. Therefore, norepinephrine may
well have contributed to the observed stress effects. More studies are needed to mechanistically pinpoint the interactions of cortisol, norepinehrine and other neuromodulators, and
their respective effects on the relative balance of memory systems. To conclude, we should
keep in mind that the stress response is very complex with many interacting systems being
affected. While we focused on a specific part of the stress system, i.e. the MR as a receptor
for corticosteroids, several other messengers contribute to stress-induced changes and their
interactions are still largely unclear. Still, this specific focus allowed us to investigate in detail
the effects of both stable and acute variations in MR-functioning and dissect stress-induced
changes mediated by this receptor.
So far, we have largely ignored the important functions of the MR outside of the brain
and as a receptor for other steroid hormones. As receptor for aldosterone, the MR regulates
the salt and water balance of the body, which is crucial for survival. Interestingly, aldosterone
might also bind to the MR in the brain, but only in those brain regions where the enzyme
11-beta hydroxysteroid dehydrogenase type 2 (11β-HSD2) is expressed. This might be the
case for the nucleus of the solitary tract, but possibly also the central amygdala and paraventricular nucleus (Murck et al, 2014). In these brain regions, the MR might also be occupied by
aldosterone, for which the effect on cognition is still being investigated (Kubzansky & Adler,
2010). Also progesterone has been mainly ignored in this thesis, for which some argue that it
is an antagonist which might have a higher affinity than aldosterone at the MR (Rupprecht et
al, 1993; but see Arriza et al, 1987; Myles & Funder, 1996). Furthermore, there appears to be a
link between MR-overactivity and a heightened risk for diabetes and inflammatory processes
(Murck et al, 2014), suggesting a role for the MR in metabolic and immune functions. Considering these findings involving the MR in other functions beyond cognition, one may have
to be careful when thinking about manipulating MR-functioning as a possible treatment for
stress-related mental disorders.
Finally, as yet we have not fully acknowledged the possible contributions of the second
receptor type for glucocorticoids, the GR, to stress-induced changes in brain and behavior.
As outlined before, slow GR-mediated effects are in recent work assumed to bring the stress
response to halt and reinstall homeostasis. In the studies described in chapter 3, 4, and 5, we
made use of MR-blockade to investigate processes depending on MR-availability. However,
MR-blockade induced by spironolactone results in more cortisol being available for binding
to GRs, which might in turn activate GR-dependent pathways. On top of that, MR-blockade
enhances baseline cortisol levels, thus further increasing the likelihood of GR activation in
the participants who received spironolactone. Our results concerning a stress-induced shift
being blocked by spironolactone must therefore not be interpreted as a sole MR effect, but
116
General Discussion
rather as a consequence of shifting the MR/GR activation balance towards the GR (Cornelisse
et al, 2011). A similar shift in receptor balance towards high GR but low MR-activation has
been related to early life adversity and psychiatric disorders, therefore making it an interesting
condition to further investigate as a possible model for depression (de Kloet et al, 2005). Future
studies which manipulate the MR/GR activation balance are desirable to further investigate
the specific effects mediated by either receptor, but also the interactive effects driven by the
relative activation balance.
To summarize, we support a role for the human MR both in the susceptibility for stressrelated mental disorders and a stress-induced shift towards more automated systems
underlying behavior and cognition. This shift appears to be driven by MR-activation, although
other messengers released under stress, e. g. norepinephrine, and the other cortisol receptor,
the GR, might contribute as well. MR-activation appears to induce - in addition to its slow
gene-mediated actions - rapid, non-genomic changes leading to enhanced amygdala activity and connectivity with the striatum, but impaired hippocampal activity. It remains to be
investigated how this shift is to be generalized to females and how MR-dependent effects are
changed by factors such as circadian and ultradian rhythms in cortisol release or the different
stages of memories. In the last chapter, I will describe possible implications of our findings for
the clinic and general society.
Possible implications for clinic and society
Stress is a phenomenon which we encounter frequently in every-day life. Healthy individuals
will activate a complex stress-response in these challenging situations, allowing them to readily adapt and deal with increased demands. After stressful encounters, the stress-response
will be efficiently downregulated to reattain homeostasis. However, in some individuals and
under specific circumstances, the stress response can become maladaptive. Considering the
societal costs related to stress-related mental disorders, a better understanding of stress and
the stress-response is of great importance in order to better treat or even prevent health
problems related to stress (Collins et al, 2011).
In chapter 2, we demonstrated that genetic changes leading to differences in MR-functioning can be linked to a risk factor for depression in healthy individuals. Even though the
variance explained by single polymorphisms or genes is usually rather small, the approach
of gene-by-environment studies might lead to a better prediction of who might fall ill upon
encountering stressful events and who will not. At last, this might be a step forward on the
way to personalized health care that does not only consider the disease category (e.g. major
depressive disorder), but includes the genetic and psychosocial make-up, and life history of an
individual when evaluating treatment options. This could lead to improved therapy outcomes
and might even help in preventing disease in those who are at risk. Relating to this, it is important to realize that the amount of MR-expression in a given neuron is not set in stone but
117
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Chapter 6
highly dynamic. For example, acute stress can lead to increased hippocampal MR-expression
for 24 hours which is assumed to restrain the HPA axis from overshooting (Gesing et al, 2001).
Also long-term physical exercise has been shown to induce changes in MR expression (Chang
et al, 2008). A better understanding of these inducible changes in MR expression, also in humans, is certainly needed. Given the findings summarized above, enhancing MR expression
chronically might be a promising novel target in the therapy of depression.
In the remaining three empirical chapters, we investigated a stress-induced shift from
controlled processing towards more automated, habitual behavior. We demonstrated this
shift in three different cognitive domains, indicating that it might be a general phenomenon
affecting a broad range of human behavior. In line with this idea of a general stress-induced
shift, recent studies reported similar stress (hormone)-induced shifts towards habitual systems underlying behavior in goal-directed learning (Schwabe et al, 2012c), reinforcement
learning (Otto et al, 2013), classification learning (Schwabe et al, 2013c), and decision making
(Leder et al, 2013; Porcelli & Delgado, 2009). Despite this proposed universality of the stressinduced shift, the specific neural changes under stress might differ according to the cognitive
domain investigated. For example, while the stress effects we found were clustered on the
amygdala, striatum, and hippocampus, other studies using different paradigms suggested a
stress-induced downregulation of the executive control network (Hermans et al, 2011) and
the orbitofrontal cortex (Schwabe et al, 2012c).
The stress-induced shift might prove relevant for any disorder involving well-learned
maladaptive behaviors or cognitive rigidity. For example, anxiety or stress can lead to relapse
in drug addiction (Herman & Polivy, 1975; Weiss et al, 2001), and also patients with obsessivecompulsive disorders might be prone to stress-induced relapse. As of yet no studies were
conducted to specifically target a stress-induced cognitive shift in patient populations. However, if our findings hold in patients as well, we suggest that the shift might be prevented by
short-term administration of MR-antagonists, by preventing stress-induced cortisol increases,
or by inhibiting stress-induced hyperactivity in the amygdala. Moreover, the stress-induced
shift is not only relevant for psychiatry but might be relevant for society in general. It was
shown that stress leads to more reflexive behavior (Porcelli & Delgado, 2009; Schwabe et al,
2011; Vinkers et al, 2013) and less strategic decisions (Leder et al, 2013). This might have implications for any occupation which involves complex decisions being made under high levels
of stress. Our and other findings suggest that under stress, behavior will be driven by habitual,
automatic responses instead of being guided by a complex decision-making process which
takes long-term consequences into account. Awareness of the stress-induced shift might help
to prevent errors made under stress by making limitations in flexible thinking more explicit
and by practicing important behaviors so that they become automated.
118
General Discussion
Concluding remarks
William James supposedly said that ‘The greatest weapon against stress is our ability to choose
one thought over another’ (quoted in Hyland, 2014, p. 27). However, the stress-induced shift
towards automated habit-like behavior and memories might impair exactly this flexibility to
choose one thought over the other. Our findings suggest that this effect might be mediated
by the MR and stronger for participants with higher MR-sensitivity. This thesis might therefore
help in better understanding what happens to humans under stress and why there is a striking variability in individual responding to stress and the risk for psychopathology.
6
119
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Iedereen weet wat ‘stress’ is, bijvoorbeeld in verband met een groot tentamen, sollicitatiegesprek of auto-ongeluk. Typische symptomen bij mensen onder stress zijn een verhoogde
hartslag en bloeddruk, overmatig zweten en versnelde ademhaling. Dit komt door de activatie van zogenaamde stresssystemen in ons lichaam, die leiden tot de productie en afgifte
van onder ander het stresshormoon cortisol, maar ook adrenaline en noradrenaline. Deze
stoffen beïnvloeden onder stress niet alleen onze transpiratie of het cardiovasculaire systeem,
maar ook onze cognitieve vaardigheden: onze gedachten en ons vermogen om dingen te
onthouden. In dit proefschrift hebben we dus onderzocht hoe stress ons denken beïnvloedt,
waarbij we ons vooral op de rol van het hormoon cortisol geconcentreerd hebben. Cortisol
kan aan twee receptoren in de hersenen binden om zijn effecten op onze cognitie uit te
oefenen. Over de rol van één van beide receptoren, de mineralocorticoidreceptor (MR), bij
stress-gerelateerde cognitieve veranderingen is bij de mens nog weinig bekend. Daarom is
het doel van dit proefschrift om meer te leren over de rol van de MR in de cognitieve functies
van mensen. Eerst onderzochten we de cognitieve gevolgen van kleine genetische varianten van de MR bij gezonde jonge mensen (hoofdstuk 2) onderzocht. Ten tweede hebben
we bestudeerd hoe een acute activatie van de MR door middel van een stress-ervaring de
cognitieve vaardigheden bij mensen beïnvloedt (hoofdstukken 3-5). In de volgende alinea’s
worden deze studies in het kort beschreven en de resultaten samengevat.
Een reden waarom ‘stress’ zo vaak bestudeerd wordt is dat stress in veel psychische
stoornissen een belangrijke rol lijkt te spelen. Stressvolle levensgebeurtenissen - zoals het
verlies van je baan, een echtscheiding, ongevallen of een slachtoffer van geweld zijn - kunnen depressieve episodes en angststoornissen veroorzaken en in stand houden. Echter, dit
geldt niet voor iedereen in gelijke mate, er zijn sterke individuele verschillen in of en hoe snel
mensen na stress ziek worden. Een mogelijke reden voor deze verschillen tussen individuen
is hun genetische aanleg, waardoor sommigen misschien kwetsbaarder zijn dan andere. In
dit verband werd reeds vastgesteld dat genetische variaties die tot een gering functieverlies
van de MR leiden geassocieerd zijn met een hoger risico van depressieve symptomen bij
oudere mensen. In hoofdstuk 2 hebben we dus onderzocht of we ook een associatie kunnen
vinden tussen genetische varianten van de MR bij gezonde jonge mensen met cognitieve
veranderingen, die op een verhoogd risico van depressie wijzen. Onze resultaten tonen aan
dat genetische varianten van MR gepaard gaan met de neiging om negatieve gebeurtenissen beter te herinneren - een verschijnsel dat ook optreedt bij patiënten met een depressie.
Deze associatie was versterkt bij proefpersonen met meer stressvolle levensgebeurtenissen,
die over het algemeen een verhoogd risico van psychische problemen hebben. Hieruit kan
geconcludeerd worden dat bepaalde genetische varianten van MR geassocieerd zijn met een
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verhoogd risico op depressieve stoornissen, wat bovendien versterkt wordt door de ervaring
van stressvolle levensgebeurtenissen. Vergelijkbare ‘gen-omgeving interacties’ zijn ook gerapporteerd voor andere genen die geassocieerd zijn met depressie en blijken prototypisch
voor de ontwikkeling van stressgerelateerde psychische stoornissen te zijn. Naast het feit
dat dergelijke studies op lange termijn misschien een voorspelling mogelijk maken van wie
gevoeliger is voor psychische problemen onder stress, konden we ook laten zien dat variaties
van het MR inderdaad geassocieerd zijn met cognitieve veranderingen, die de ontwikkeling
van een depressie kunnen begunstigen.
Vervolgens hebben we onderzocht hoe een acute activatie van de MR door een stressvolle
ervaring cognitieve vaardigheden van mensen beïnvloed. Uit een recente studie bij knaagdieren bleek dat de MR onder stress tot een cognitieve verschuiving (‘shift’) van veeleisende,
cognitief intensieve processen naar meer geautomatiseerde en efficiënte cognitieve processen leidt. Hierbij wordt aangenomen dat deze shift adaptief is omdat cognitieve bronnen
bespaard worden, snelle reacties mogelijk gemaakt worden en de cognitieve prestatie
bewaard blijft. Deze hypothese van een stress-gerelateerde shift hebben we vervolgens in
detail bij mensen onderzocht. In de eerste stap hebben we de effecten bestudeerd op de
detectie en verwerking van emotionele stimuli, namelijk foto’s van gezichten met emotionele
gezichtsuitdrukkingen (hoofdstuk 3). Stress werd bij gezonde mannelijke vrijwilligers geïnduceerd om de afgifte van cortisol te verhogen en aldus de MR te activeren. Aan de helft van de
proefpersonen werd voordien de drug spironolacton toegediend, die de MR blokkeert, om
te onderzoeken welke stress-gerelateerde veranderingen afhankelijk van de MR zijn. Er was
voor vergelijkingsdoeleinden ook nog een groep van proefpersonen die geen stress hebben
ervaren, maar ook spironolacton of een placebo toegediend kregen. Hoewel we op gedragsniveau geen stress-gerelateerde verschillen in emotionele verwerking konden vinden, waren
we in staat om met behulp van functionele magnetische resonantie imaging (fMRI) te laten
zien dat stress - via MR activatie - leidt tot een verandering in de functionele connectiviteit in
de hersenen. Dit betekent dat de mate van connectiviteit, hoeveel de activiteit van verschillende hersengebieden met elkaar gecorreleerd is, beïnvloed werd door stress. In dit geval
vonden we een toename aan connectiviteit tussen het outputgebied van de amygdala en
het dorsale striatum. De amygdala speelt een belangrijke rol bij het herkennen van stress en
de inleiding van de stress respons van het lichaam, terwijl het dorsale striatum de basis is van
meer geautomatiseerd gedrag. We konden dus de hypothese van het knaagdieronderzoek
ondersteunen – stress (door activatie van de MR) leidt tot een verschuiving naar hersengebieden die meer geautomatiseerd gedrag steunen, wat cognitieve resourcen kann besparen en
dus ook snelle en efficiënte reacties in een stressvolle situatie mogelijk maakt.
In de volgende stap (hoofdstuk 4), hebben we onderzocht hoe acute (stress-gerelateerde)
activatie van de MR het ruimtelijke leren en de onderliggende hersenprocessen beïnvloed.
Aangezien de bovengenoemde studie in knaagdieren ook een ruimtelijk geheugentaak
gebruikte, was het doel hier een exacte replicatie van de bevindingen in de mens te vol138
brengen, en bovendien de onderliggende neurale mechanismen te onderzoeken. Nadat
de vrijwilligers de emotionele beelden hadden verwerkt (hoofdstuk 3), deden ze een taak
in de MRI scanner die vergelijkbaar was met een computerspel. Ze leerden de posities van
verschillende objecten in een virtuele omgeving. Om deze posities te leren kan men een
cognitief veeleisende strategie gebruiken, die leidt tot een mentale kaart van de omgeving
die door de hippocampus wordt opgeslagen. Maar men kan zich ook oriënteren met behulp
van opvallende markeringen in de omgeving, zogenaamde landmarks, om de objectposities
te leren. Deze strategie is automatischer en minder veeleisend, maar ook minder flexibel als
de omgeving verandert. In de hersenen is deze strategie gebaseerd op het dorsale striatum.
In overeenstemming met een door stress veroorzaakte shift naar meer geautomatiseerde
strategieën konden we laten zien dat stress ervoor zorgt dat mensen meer gebruik maken van
landmarksom te navigeren, dan van het aanleren van een mentale kaart. Daarnaast toonden
wij aan dat - met name bij personen met een sterke cortisolreactie na stress - de activiteit van
de amygdala en de connectiviteit met het dorsale striatum door stress verhoogd werd. Samenvattend kunnen we weer de hypothese ondersteunen dat stress via activatie van de MR
leidt tot een shift naar automatisch gedrag. In de hersenen lijkt dit georkestreerd te worden
door de amygdala, waarvan de activiteit en connectiviteit met de dorsale striatum beïnvloed
word door de MR.
In het laatste hoofdstuk onderzochten we de vraag of stressgerelateerde MR activatie het
leren van angst beïnvloed (hoofdstuk 5). Hoewel angst conditionering benadrukt wordt als
een belangrijke oorzaak van het ontstaan van stress-gerelateerde psychische stoornissen, zijn
er verbazend weinig studies over de effecten van stress op angst conditionering. Ook voor
het leren van angst kan men eenvoudigere en meer complexe strategien onderscheiden.
Terwijl de eerste gebaseerd is op de amygdala, is de hippocampus nodig voor complexere
stimulus-angst associaties. Nadat de proefpersonen de taak voor ruimtelijk geheugen hadden
voltooid, was een laatste opdracht gepresenteerd, waar ze leerden dat bepaalde beelden een
onaangename (maar niet pijnlijke) elektrische prikkel voorspelden. De taak bevatte eenvoudigere en meer complexe stimulus-shock associaties. De gestresste proefpersonen vertoonden
de volgende dag een sterkere angstreactie voor de eenvoudige stimulus-shock associatie dan
voor de meer complexe associatie. Dat steunt weer de de hypothese van een door stress veroorzaakte shift naar eenvoudigere strategieën in gedrag en geheugen. Zoals in de hoofdstukken 3 en 4 werd dit veroorzaakt door de MR en kon worden voorkomen door spironolacton
toe te dienen. In de hersenen vonden we dat stress – gemediëerd door de MR – de activiteit
van de hippocampus tijdens het leren van de complexe associates vermindert. Deze studie
kan dus ook weer beschouwd worden als steun voor een stressgerelateerde shift naar meer
geautomatiseerde en efficiënte cognitieve processen. Naast de rol van de amygdala tonen we
hier ook aan dat stress invloed kan hebben op de activatie van de hippocampus bij het leren
van complexere stimulus-shock associaties.
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Samenvattend kunnen we stellen dat de MR wel een belangrijke rol speelt in het leren en
gedrag van mensen, zowel onder stress maar ook bij rust. Stabiele, genetische varianten van
de MR lijken geassocieerd te zijn met een veranderd risico op stressgerelateerde psychische
stoornissen. Wellicht verklaren de genetische varianten een deel van de grote spreiding tussen
individuen en hun manier met stress om te gaan. Acute veranderingen in MR activatie onder
stress lijken echter een shift van meer geavanceerde cognitieve en gedragsstrategien naar
meer automatisch gedrag te veroorzaken, vergelijkbaar met een ‘automatische piloot’. Wij
denken dat deze shift in de stresssituatie naar meer geautomatiseerde gedrag van voordeel is,
omdat het snelle actie mogelijk maakt en het onthouden van belangrijke stimuli bevorderd.
Echter, op lange termijn of bij chronische stress kan de shift mogelijk leiden tot ongewenste
gevolgen en potentieel het risico van psychische stoornissen verhogen.
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Ein jeder von uns weiß wie es ist, gestresst zu sein, zum Beispiel vor wichtigen Prüfungen, beim
Bewerbungsgespräch oder bei einem Autounfall. Typische Symptome, die bei Menschen unter Stress auftreten, sind ein Anstieg von Puls und Blutdruck, vermehrtes Schwitzen und eine
schnellere Atmung. Dem zugrunde liegt die Aktivierung von sogenannten Stress-Systemen,
die dazu führen, dass unter Anderem das Stresshormon Kortisol, aber auch Adrenalin und Noradrenalin verstärkt ausgeschüttet werden. Diese Botenstoffe beeinflussen unter Stress nicht
nur unsere Schweißbildung oder das Herz-Kreislauf-System, sondern auch unsere kognitiven
Fähigkeiten, also unsere Gedanken und unsere Fähigkeit, uns Dinge einzuprägen. In dieser
Dissertation haben wir untersucht, wie Stress unser Denken beeinflusst, wobei wir uns auf die
Rolle des Hormons Kortisol konzentriert haben. Kortisol kann an zwei Rezeptoren im Gehirn
binden, um seine Effekte auf unsere Kognition auszuüben. Zur Rolle von einem der beiden
Rezeptoren, dem Mineralocorticoidrezeptor (MR), bei Stress-bedingten kognitiven Veränderungen beim Menschen ist bisher nur wenig bekannt. Daher war das Ziel dieser Arbeit, mehr
über die Rolle des MRs bei kognitiven Funktionen des Menschen zu lernen. Dazu haben wir
als erstes untersucht, welche kognitiven Konsequenzen kleine genetische Variationen des MR
beim gesunden Menschen haben (Kapitel 2). Danach haben wir geprüft, wie sich eine akute
Aktivierung des MR durch eine Stresserfahrung auf kognitive Fähigkeiten beim Menschen
auswirkt (Kapitel 3 bis 5). In den folgenden Absätzen werden diese Studien kurz beschrieben
und ihre Ergebnisse zusammengefasst.
Ein Grund warum das Phänomen Stress so häufig untersucht wird, ist dass Stress bei vielen
psychischen Störungen eine wichtige Rolle zu spielen scheint. So können stressvolle Lebensereignisse (z.B. Jobverlust, Scheidung, Unfälle oder Opfer von Gewalt zu werden) depressive
Episoden oder Angsterkrankungen auslösen und aufrechterhalten. Allerdings gilt das nicht
für jeden gleichermaßen, sondern es gibt starke individuelle Unterschiede ob und wie schnell
Menschen nach Stress erkranken. Ein möglicher Grund für diese Unterschiede zwischen Individuen ist ihre genetische Ausstattung, wodurch manche Menschen womöglich anfälliger
für Stress sind als andere. In diesem Zusammenhang wurde bereits herausgefunden, dass
genetische Variationen, die zu einem leichten Funktionsverlust des MRs führen, mit einem höheren Risiko für depressive Symptome im Alter einhergehen. In Kapitel 2 haben wir daraufhin
untersucht, ob man auch bei gesunden jungen Menschen Zusammenhänge zwischen genetischen Variationen des MR mit kognitiven Veränderungen finden kann, welche wiederum auf
ein erhöhtes Depressionsrisiko hindeuten. Wir konnten zeigen, dass genetische Varianten des
MRs mit der Neigung einhergehen, sich negative Ereignisse besonders gut zu merken – ein
Phänomen, welches auch bei Patienten mit Depression auftritt. Dieser Zusammenhang war
besonders stark, wenn die Probanden mehr stressvolle Lebensereignisse erlebt hatten, was
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allgemein das Risiko für psychische Probleme erhöht. Daraus lässt sich schlussfolgern, dass
bestimmte genetische Variationen des MR mit einem erhöhten Risiko für depressive Erkrankungen einhergehen, was durch das Erleben von stressvollen Ereignissen noch verstärkt wird.
Ähnliche Gen-Umwelt-Interaktionen wurden auch für andere genetische Marker für Depressionen gefunden und scheinen prototypisch für die Entstehung Stress-bedingter psychischer
Störungen zu sein. Abgesehen davon, dass solche Studien auf lange Sicht vielleicht eine
Vorhersage ermöglichen, wer anfälliger für psychische Probleme unter Stress ist, konnten wir
damit auch zeigen, dass Variationen des MR mit kognitiven Veränderungen einhergehen, die
möglicherweise die Entstehung von Depressionen begünstigen.
Als nächstes haben wir untersucht, wie sich eine akute Aktivierung des MRs durch eine
stressvolle Erfahrung auf kognitive Fähigkeiten des Menschen auswirkt. Eine aktuelle Studie
in Nagetieren konnte zeigen, dass der MR unter Stress zu einem kognitiven Wechsel weg
von anspruchsvolleren, ressourcenintensiven Prozessen hin zu mehr automatisierten und
ressourcenschonenden kognitiven Prozessen führt. Dabei wird angenommen, dass dieser
Wechsel adaptiv ist, da er Ressourcen in stressvollen Situationen spart, effizientes Handeln
ermöglicht und die kognitive Leistungsfähigkeit aufrechterhält. Diese Hypothese eines stressbedingten Wechsels haben wir daraufhin im Menschen näher untersucht. Im ersten Schritt
haben wir uns die Effekte auf das Erkennen und Verarbeiten emotionaler Reize, d.h. Bilder von
Gesichtern, die emotionale Gesichtsausdrücke zeigen, angeschaut (Kapitel 3). Dazu wurde
bei gesunden männlichen Probanden Stress induziert, um die Ausschüttung von Kortisol zu
erhöhen und damit den MR zu aktivieren. Der Hälfte der Probanden wurde vorher das Medikament Spironolacton verabreicht, welches den MR blockiert, um untersuchen zu können
welche Stress-bezogenen Veränderungen auf den MR zurückzuführen sind. Schließlich gab
es zu Vergleichszwecken noch eine weitere Gruppe von Probanden, bei denen kein Stress induziert wurde und die ebenfalls entweder Spironolacton oder ein Placebo erhielten. Obwohl
es wir auf Verhaltensebene keine stressbezogenen Unterschiede im Verarbeiten emotionaler
Bilder finden konnten, konnten wir mittels funktioneller Magnetresonanztomographie (fMRT)
zeigen, dass Stress – mit Hilfe des MR – zu einer Veränderung der funktionalen Konnektivität
im Gehirn führt. Das heißt, dass das Ausmaß, wie sehr die Aktivität verschiedener Hirnregionen miteinander korreliert, durch Stress beeinflusst wurde. In unserem Fall bezog sich das
speziell auf einen Anstieg der Korrelation zwischen der Output-Region der Amygdala und
dem dorsalen Striatum. Die Amygdala spielt eine wichtige Rolle beim Erkennen von Stress
und der Initiierung der Stressantwort des Körpers, wohingegen das dorsale Striatum eher ressourcenschonendem, automatisiertem Verhalten zu Grunde liegt. Demzufolge konnten wir
die These der Nagetierstudie unterstützen, dass Stress – durch eine Aktivierung des MR – zu
einem Wechsel hin zu automatisiertem Verhalten führt, was womöglich kognitive Ressourcen
spart und damit in einer stressvollen Situation schnelles und effizientes Reagieren ermöglicht.
Entsprechend fanden wir eine stärkere Korrelation zwischen Gehirnregionen, welche diese
Funktionen vermitteln.
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Im nächsten Schritt (Kapitel 4) haben wir untersucht, wie sich eine akute (Stressbedingte)
Aktivierung des MRs auf das räumliche Lernen und die zugrundeliegenden Hirnprozesse
auswirkt. Da die zuvor erwähnte Studie in Nagetieren ebenso räumliche Gedächtnisaufgaben
verwendete, strebten wir hier eine exakte Replikation der Befunde an und wollten zusätzlich
die zugrundeliegenden neuronalen Mechanismen untersuchen. Nachdem den Probanden
die emotionalen Bilder gezeigt wurden (Kapitel 3) wurde ihnen im MRT-Scanner eine Aufgabe präsentiert, die mit einem Computerspiel vergleichbar war. Sie sollten in einer virtuellen
Umgebung lernen, wo bestimmte Objekte zu finden sind. Um deren Positionen zu lernen,
kann man sowohl kognitiv anspruchsvollere Strategien verwenden, die zum Aufbau einer
mentalen Karte führen, die vom Hippocampus gespeichert wird. Man kann sich aber auch an
bestimmten auffälligen Markierungen in der Umwelt, sogenannten Landmarken, orientieren
und die Objektpositionen relativ zu den Landmarken lernen. Diese Strategie ist automatischer
und weniger anspruchsvoll, dafür aber auch weniger flexibel, wenn sich die Umgebung ändert. Im Gehirn beruht sie auf dem dorsalen Striatum. Im Einklang mit einem stressinduzierten
Wechsel hin zu automatisierteren Verhaltensweisen konnten wir zeigen, dass Stress dazu
führt, dass Menschen vermehrt Landmarken zum Navigieren nutzen anstatt sich eine mentale Karte aufzubauen. Außerdem konnten wir zeigen, dass – besonders bei den Probanden
mit einer starken Kortisolausschüttung nach Stress – die Aktivität der Amygdala anstieg sowie
wiederum auch ihre Konnektivität mit dem dorsalen Striatum. Zusammenfassend lässt sich
sagen, dass wir die Hypothese, dass Stress über eine Aktivierung des MR zu einem Wechsel hin
zu automatischeren Verhalten führt, unterstützen können. Im Gehirn scheint dies durch die
Amygdala orchestriert zu sein, deren Aktivität und Konnektivität mit dem dorsalen Striatum
durch den MR beeinflusst wird.
Im letzten Kapitel untersuchten wir die Frage, ob eine stressbedingte MR-Aktivierung auch
das Furchtlernen beeinflusst (Kapitel 5). Obwohl Furchtlernen unter Stress als eine wichtige
Ursache für das Entstehen stressbedingter psychischer Störungen gilt, gibt es erstaunlich wenige Erkenntnisse darüber, wie Stress das Furchtlernen beeinflusst. Auch beim Furchtlernen
kann man einfacheres, weniger anspruchsvolles Furchtlernen von komplexerem Furchtlernen
unterscheiden. Während ersteres auf der Amygdala basiert, ist für letzteres der Hippocampus
notwendig. Nachdem die Probanden die Aufgabe zum räumlichen Gedächtnis absolviert
hatten, wurde ihnen noch eine letzte Aufgabe präsentiert, bei der sie lernten, dass bestimmte
Bilder einen unangenehmen (aber nicht schmerzhaften) Elektroschock vorhersagten. Die
Aufgabe war dabei so angelegt, dass sie sowohl einfachere als auch etwas komplexere ReizSchock-Assoziationen enthielt. Es zeigte sich, dass die gestressten Probanden am nächsten
Tag eine stärkere Furchtreaktion bei der einfacheren Reiz-Schock-Assoziation zeigten im Vergleich zu der komplexeren Assoziation, was wiederum die Hypothese eines stressinduzierten
Wechsels hin zu einfacheren Strategien unterstützt. Ebenso wie in Kapitel 3 und 4 war dies
durch den MR vermittelt und konnte durch die Gabe von Spironolacton verhindert werden.
Auf neuronaler Ebene fanden wir, dass Stress – vermittelt durch den MR – die Aktivität des
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Hippocampus beim Lernen der komplexeren Zusammenhänge reduziert. Auch diese Studie
kann demnach als Unterstützung für den Stress-induzierten Wechsel hin zu einfacheren Lernund Verhaltensformen betrachtet werden. Zusätzlich zu einer Rolle der Amygdala konnten
wir zudem zeigen, dass Stress auch die Aktivierung des Hippocampus beim Lernen von ReizSchock-Assoziationen beeinträchtigen kann.
Zusammenfassend lässt sich sagen, dass der MR, über dessen Einfluss auf Lernen und
Verhalten von Menschen bisher wenig bekannt war, in diesen Funktionen eine wichtige
Rolle spielt. Stabile, genetische Variationen des MR scheinen mit einem veränderten Risiko
für stressbezogene Störungen zusammenzuhängen. Damit können sie vielleicht einen Teil
der großen Varianz zwischen Individuen und ihrer Art mit Stress umzugehen erklären. Akute
Veränderungen der MR-Aktivierung unter Stress hingegen scheinen einen Wechsel von
kognitiv anspruchsvollerem Denken und Handeln zu eher automatischen Verhaltensweisen
zu verursachen, vergleichbar mit einem „Autopiloten“. Wir glauben, dass dieser Wechsel hin zu
automatisiertem Verhalten und Denken in der Stresssituation durchaus von Vorteil ist, da er
schnelles Handeln und das einprägen wichtiger Reize ermöglicht. Auf lange Sicht hingegen,
oder unter chronischen Belastungen, kann er möglicherweise zu ungewünschten Folgen
führen und potentiell das Risiko für psychische Störungen erhöhen.
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neuroscience series
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Grol, M.J. (2008). Parieto-frontal circuitry in visuomotor control. Utrecht University, Utrecht, the Netherlands.
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23. Vergeer, M. (2010). Perceptual visibility and appearance: Effects of color and form. Radboud University Nijmegen,
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28. Dado – Van Beek, H.E.A. (2010). The regulation of cerebral perfusion in patients with Alzheimer’s disease. Radboud
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52. Ossewaarde, L. (2011). The mood cycle: Hormonal influences on the female brain. Radboud University Nijmegen,
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108. Visser, E. (2012). Leaves and forests: Low level sound processing and methods for the large-scale analysis of white
matter structure in autism. Radboud University Nijmegen, Nijmegen, The Netherlands.
109. Van Tooren-Hoogenboom, N. (2012). Neuronal communication in the synchronized brain. Investigating the functional role of visually-induced gamma band activity: Lessons from MEG. Radboud University Nijmegen, Nijmegen,
The Netherlands.
110. Henckens, M.J.A.G. (2012). Imaging the stressed brain. Elucidating the time- and region-specific effects of stress
hormones on brain function: A translational approach. Radboud University Nijmegen, Nijmegen, The Netherlands.
111. Van Kesteren, M.T.R. (2012). Schemas in the brain: Influences of prior knowledge on learning, memory, and education.
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112. Brenders, P. (2012). Cross-language interactions in beginning second language learners. Radboud University Nijmegen, Nijmegen, The Netherlands.
113. Ter Horst, A.C. (2012). Modulating motor imagery. Contextual, spatial and kinaesthetic influences. Radboud University Nijmegen, Nijmegen, The Netherlands.
114. Tesink, C.M.J.Y. (2013). Neurobiological insights into language comprehension in autism: Context matters. Radboud
115. Böckler, A. (2013). Looking at the world together. How others’ attentional relations to jointly attended scenes shape
cognitive processing. Radboud University Nijmegen, Nijmegen, The Netherlands.
116. Van Dongen, E.V. (2013). Sleeping to Remember. On the neural and behavioral mechanisms of sleep-dependent
memory consolidation. Radboud University Nijmegen, Nijmegen, The Netherlands.
117. Volman, I. (2013). The neural and endocrine regulation of emotional actions. Radboud University Nijmegen, Nijmegen, The Netherlands.
118. Buchholz, V. (2013). Oscillatory activity in tactile remapping. Radboud University Nijmegen, Nijmegen, The Netherlands.
119. Van Deurzen, P.A.M. (2013). Information processing and depressive symptoms in healthy adolescents. Radboud
120. Whitmarsh, S. (2013). Nonreactivity and metacognition in mindfulness. Radboud University Nijmegen, Nijmegen,
The Netherlands.
121. Vesper, C. (2013). Acting together: Mechanisms of intentional coordination. Radboud University Nijmegen, Nijmegen, The Netherlands.
122. Lagro, J. (2013). Cardiovascular and cerebrovascular physiological measurements in clinical practice and prognostics
in geriatric patients. Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
123. Eskenazi, T.T. (2013). You, us & them: From motor simulation to ascribed shared intentionality in social perception.
124. Ondobaka, S. (2013). On the conceptual and perceptual processing of own and others’ behavior. Radboud University
Nijmegen, Nijmegen, The Netherlands.
125. Overvelde, J.A.A.M. (2013). Which practice makes perfect? Experimental studies on the acquisition of movement sequences to identify the best learning condition in good and poor writers. Radboud University Nijmegen, Nijmegen,
The Netherlands.
126. Kalisvaart, J.P. (2013). Visual ambiguity in perception and action. Radboud University Nijmegen Medical Centre,
127. Kroes, M. (2013). Altering memories for emotional experiences. Radboud University Nijmegen, Nijmegen, The
Netherlands.
128. Duijnhouwer, J. (2013). Studies on the rotation problem in self-motion perception. Radboud University Nijmegen,
129. Nijhuis, E.H.J (2013). Macroscopic networks in the human brain: Mapping connectivity in healthy and damaged
brains. University of Twente, Enschede, The Netherlands
130. Braakman, M. H. (2013). Posttraumatic stress disorder with secondary psychotic features. A diagnostic validity study
among refugees in the Netherlands. Radboud University Nijmegen, Nijmegen, The Netherlands.
131. Zedlitz, A.M.E.E. (2013). Brittle brain power. Post-stroke fatigue, explorations into assessment and treatment. Radboud
132. Schoon, Y. (2013). From a gait and falls clinic visit towards self-management of falls in frail elderly. Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
133. Jansen, D. (2013). The role of nutrition in Alzheimer’s disease - A study in transgenic mouse models for Alzheimer’s
disease and vascular disorders. Radboud University Nijmegen, Nijmegen, The Netherlands.
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134. Kos, M. (2013). On the waves of language - Electrophysiological reflections on semantic and syntactic processing.
135. Severens, M. (2013). Towards clinical BCI applications: Assistive technology and gait rehabilitation. Radboud University Nijmegen, Nijmegen, Sint Maartenskliniek, Nijmegen, The Netherlands.
136. Bergmann, H. (2014). Two is not always better than one: On the functional and neural (in)dependence of working
memory and long-term memory. Radboud University Nijmegen, Nijmegen, The Netherlands.
137. Wronka, E. (2013). Searching for the biological basis of human mental abilitites. The relationship between attention
and intelligence studied with P3. Radboud University Nijmegen, Nijmegen, The Netherlands.
138. Lüttjohann, A.K. (2013). The role of the cortico-thalamo-cortical system in absence epilepsy. Radboud University
139. Brazil, I.A. (2013). Change doesn’t come easy: Dynamics of adaptive behavior in psychopathy. Radboud University
140. Zerbi, V. (2013). Impact of nutrition on brain structure and function. A magnetic resonance imaging approach in
Alzheimer mouse models. Radboud University Nijmegen, Nijmegen, The Netherlands.
141. Delnooz, C.C.S. (2014). Unravelling primary focal dystonia. A treatment update and new pathophysiological insights.
Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
142. Bultena, S.S. (2013). Bilingual processing of cognates and language switches in sentence context. Radboud University Nijmegen, Nijmegen, The Netherlands.
143. Janssen, G. (2014). Diagnostic assessment of psychiatric patients: A contextual perspective on executive functioning.
144. Piai, V. Magalhães (2014). Choosing our words: Lexical competition and the involvement of attention in spoken word
production. Radboud University Nijmegen, Nijmegen, The Netherlands.
145. Van Ede, F. (2014). Preparing for perception. On the attentional modulation, perceptual relevance and physiology of
oscillatory neural activity. Radboud University Nijmegen, Nijmegen, The Netherlands.
146. Brandmeyer, A. (2014). Auditory perceptual learning via decoded EEG neurofeedback: a novel paradigm. Radboud
147. Radke, S. (2014). Acting social: Neuroendocrine and clinical modulations of approach and decision behavior. Radboud University Nijmegen, Nijmegen, The Netherlands.
148. Simanova, I. (2014). In search of conceptual representations in the brain: towards mind-reading. Radboud University
149. Kok, P. (2014). On the role of expectation in visual perception: A top-down view of early visual cortex. Radboud
150. Van Geldorp, B. (2014). The long and the short of memory: Neuropsychological studies on the interaction of working
memory and long-term memory formation. Radboud University Nijmegen, Nijmegen, The Netherlands.
151. Meyer, M. (2014). The developing brain in action - Individual and joint action processing. Radboud University Nijmegen, Nijmegen, The Netherlands.
152. Wester, A. (2014). Assessment of everyday memory in patients with alcohol-related cognitive disorders using the
Rivermead Behavioural Memory Test. Radboud University Nijmegen, Nijmegen, The Netherlands.
153. Koenraadt, K. (2014). Shedding light on cortical control of movement. Radboud University Nijmegen, Nijmegen;
Sint Maartenskliniek, Nijmegen, The Netherlands.
154. Rutten-Jacobs, L.C.A. (2014). Long-term prognosis after stroke in young adults. Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
155. Herbert, M.K. (2014). Facing uncertain diagnosis: the use of CSF biomarkers for the differential diagnosis of neurodegenerative diseases. Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
156. Llera Arenas, A. (2014). Adapting brain computer interfaces for non-stationary changes. Radboud University Nijmegen, Nijmegen, The Netherlands.
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157. Smulders, K. (2014). Cognitive control of gait and balance in patients with chronic stroke and Parkinson’s disease.
Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
158. Boyacioglu, R. (2014). On the application of ultra-fast fMRI and high resolution multiband fMRI at high static field
strengths. Radboud University Nijmegen, Nijmegen, The Netherlands.
159. Kleinnijenhuis, M. (2014). Imaging fibres in the brain. Radboud University Nijmegen, Nijmegen, The Netherlands.
160. Geuze, J. (2014). Brain Computer Interfaces for Communication: Moving beyond the visual speller. Radboud University Nijmegen, Nijmegen, The Netherlands.
161. Platonov, A. (2014). Mechanisms of binocular motion rivalry. Radboud University Nijmegen, Nijmegen, The Netherlands.
162. Van der Schaaf, M.E. (2014). Dopaminergic modulation of reward and punishment learning. Radboud University
Nijmegen Medical Centre, Nijmegen, The Netherlands.
163. Aerts, M.B. (2014). Improving diagnostic accuracy in parkinsonism. Radboud University Nijmegen Medical Centre,
164. Vlek, R. (2014). From Beat to BCI: A musical paradigm for, and the ethical aspects of Brain-Computer Interfacing.
165. Massoudi, R. (2014). Interaction of task-related and acoustic signals in single neurons of monkey auditory cortex.
Radboud University Nijmegen, Nijmegen, The Netherlands
166. Stolk, A. (2014). On the generation of shared symbols. Radboud University Nijmegen, Nijmegen, The Netherlands.
167. Krause F. (2014). Numbers and magnitude in the brain: A sensorimotor grounding of numerical cognition. Radboud
168. Munneke, M.A.M. (2014). Measuring and modulating the brain with non-invasive stimulation. Radboud University
Nijmegen Medical Centre, Nijmegen, The Netherlands.
169. Von Borries, K. (2014). Carrots & Sticks - a neurobehavioral investigation of affective outcome processing in psychopathy. Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
170. Meel-van den Abeelen, A.S.S. (2014). In control. Methodological and clinical aspects of cerebral autoregulation and
haemodynamics. Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
171. Leoné, F.T.M. (2014). Mapping sensorimotor space: Parieto-frontal contributions to goal-directed movements. Radboud University Nijmegen, Nijmegen, The Netherlands.
172. Van Kessel, M. (2014). Nothing left? How to keep on the right track - Spatial and non-spatial attention processes in
neglect after stroke. Radboud University, Nijmegen, The Netherlands.
173. Vulto-van Silfhout, A. T. (2014). Detailed, standardized and systematic phenotyping for the interpretation of genetic
variation. Radboud University Medical Centre, Nijmegen, The Netherlands.
174. Arnoldussen, D. (2015). Cortical topography of self-motion perception. Radboud University, Nijmegen, The Netherlands.
175. Meyer, M.C. (2015). “Inbetween Modalitiers combined EEG – fMR”. Radboud University, Nijmegen, The Netherlands.
176. Bralten, J. (2015). Genetic factors and the brain in ADHD. Radboud university medical center, Nijmegen, The
Netherlands.
177. Spaak, E. (2015). On the role of alpha oscillations in structuring neural information processing. Radboud University,
178. Van der Doelen, R. (2015). Translational psychiatry; the twists and turns of early life stress and serotonin transporter
gene variation. Radboud university medical center, Nijmegen, The Netherlands.
179. Lewis, C. (2015). The structure and function of intrinsic brain activity. Radboud University, Nijmegen, The Netherlands.
180. Huang, Lili. (2015). The subiculum: a promising new target of deep brain stimulation in temporal lobe epilepsy. Investigation of closed-loop and open-loop high frequency stimulation of the subiculum in seizure and epilepsy models in
rats. Radboud University, Nijmegen, The Netherlands.
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181. Maaijwee, N.A.M.M (2015). Long-term neuropsychological and social consequences after stroke in young adults.
Radboud university medical center, Nijmegen, The Netherlands.
182. Meijer, F.J.A. (2015). Clinical Application of Brain MRI in Parkinsonism: From Basic to Advanced Imaging. Radboud
university medical center, Nijmegen, The Netherlands.
183. Van der Meij, R. (2015). On the identification, characterization and investigation of phase dependent coupling in
neuronal networks. Radboud University, Nijmegen, The Netherlands.
184. Todorovic, A. (2015). Predictive adaptation in early auditory processing. Radboud University, Nijmegen, The Netherlands.
185. Horschig, J.M. (2015). Flexible control and training of posterior alpha-band oscillations. Radboud University, Nijmegen, The Netherlands.
186. Vogel, S. (2015). The runner-up: on the role of the mineralocorticoid receptor in human cognition. Radboud university
medical center, Nijmegen, The Netherlands
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Acknowledgements - Dankwoord - Danksagung
This book would not have been possible without a lot of other people. I want to use the last
few pages to thank you all for your help, guidance, and company. I may have forgotten one or
the other, please feel included in a big warm thank you.
Guillen, Ich danke dir, dass Du in den letzten vier Jahren mein Promoter warst und es mir,
frisch vom Studium, zugetraut hast, dieses Projekt erfolgreich durchzuführen. Ich habe die
vier Jahre in deiner Gruppe und am Donders sehr genossen und enorm viel lernen können.
Danke dir auch, dass du mir den Freiraum gelassen hast, auch Dinge zu tun, die vielleicht nicht
direkt diesem Buch hier geholfen haben, die mir aber unglaublich viel Spaß gemacht haben,
wie z.B. das Teaching oder der Blog. Wenn ich mich zu weit von der Doktorarbeit entfernt
habe, hast du mich immer wieder zurückgeholt und dafür gesorgt, dass wir diese Arbeit
trotz mancher Schwierigkeiten unterwegs erfolgreich abschließen konnten. Ich danke dir für
deine Betreuung, dein engagiertes Mitdenken und dass du mich immer ermutigt hast, in der
Wissenschaft zu bleiben.
Marian, you were my second promoter and although you were physically further away, I
am very happy that I could always rely on your feedback and your helpful and critical thoughts
throughout the four years. I enjoyed the meetings and interaction with you a lot, I learned
quite a bit about the corticosteroid system in rodents and humans. I also want to thank you
for your explicit early career support, such as giving me a talk at the ENP which was a truly
valuable experience. Thank you very much.
Floris, you came with a fresh PhD to Nijmegen to supervise a handful of PhD students
including me. Although I think the beginning was quite hectic for both of us, we managed
to run a complex pharmacological fMRI study together which I‘m very proud of. Thanks for
your support in these years (and your continued support), I learned a lot from you and I‘m very
happy that you were my daily supervisor. Also special thanks for supervising me even after you
had joined a new group, I‘m not sure whether this was the easiest solution for you, but I am very
grateful you supervised me until the end even though your new projects had already started.
My PhD project was set up in a collaborative fashion with researchers from Nijmegen,
Leiden, Amsterdam, and Utrecht. I am truly thankful to all the other people involved, especially
Marit, Melly, Harm, and Lotte. Thank you very much for your input during our meetings and
the support along the way. I am very happy I could get some insight into animal research, it
taught me a lot. Marit, thanks especially for the wonderful talk in Utrecht which brought us all
the money to travel to fancy places! Lotte, you were an enormous help for the genetics part. I
also owe you a big thanks for your efforts to teach me Dutch, scientific writing and frustration
tolerance. Thanks a lot! Mark, you were only involved at the very beginning, but still also my
thanks to you for your help during that time.
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Sabine, you were a huge help for my PhD project - without you it would have been much
more trouble and I‘m not sure whether it would have worked at all. Thanks for all your support
and warmth along the way, the endless screenings of my participants and your help with settling in the Netherlands. I also want to thank all the MD‘s who supported this study even when
I was testing in evenings: Daphne, Monique, Liesbeth, Nils, Dirk, Atsuko - Thank you so much.
I want to the thank all current and former members of the Emotion & Memory (or Memory
& Emotion) group: Atsuko, Boris, Carly, Christina, Daphne, David, Dirk, Eelco, Erno, Frauke,
Isabella, Jenny, Klodiana, Linda, Mariët, Marieke, Marijn, Marlieke, Marloes, Martin, Nils, Noortje,
Ruud, Yu, and all the interns and students, especially those I worked with: Krista, Sanne, Yuen,
Fernanda, and Nuha. Thank you for the great time in the group, for all the lab dinners, pizza
(thanks Linda for the organization) and knutsel-evenings, the retreat, and of course all the
scientific input. I also want to give a special thank you to the students who have been a huge
help with acquiring the data for this thesis. I wish you all the best for your future career.
A very special thanks goes to the wonderful Donders technical staff and the administration:
Arthur, Ayse, Edwards, Hong, Jessica, Marek, Mike, Nicole, Sander, Sandra, Tildie, and Erik. Lucia,
thanks a lot for being so cheerful, for your emergency office decoration package, and the
Sinterklaas poems.
Dear current and past 0.80 members, Daan, Eelke, Frauke, Ili, Liesbeth, Monique, Richard,
and Xu, thanks for all the coffee breaks and chitchat that made work so much nicer, even with
construction noise. It was a great time!
I also want to thank the Donders Wonders team, and especially its members in 2014: Alina,
Janita, Jeanette, Julian, Lieneke, Piet, Richard, Romy & Winke. It‘s been a wonderful opportunity
setting up Donders Wonders with you. I‘m still proud of it and enjoy every post you share.
One of the big advantages of the Donders is its collaborative and translational culture,
enabling collaborations all over the place. Thanks to all the members of other groups who I
was able to work with along the way: Benno‘s group, Judith‘s group, Barbara‘s group, Esther,
Iris, Indira, Janna, and Lieneke. Thanks for the scientific input and the fun we had during
conference visits.
The other big advantage of the Donders is its sociality (my attempt to translate gezelligheid). I may forget someone, but I want to thank the Donders social crowd, the FAD team,
the swimmers, sporters, and Madnes visitors: Anke Marit, Claudia, Corina, Eelco, Eelke, Flora,
Freek, Gesa, Isabella, Jeanette, Jil, Jolien, Jonas, Lieneke, Linda, Lisa, Marlieke, Miriam, Mirjam,
René, Ruben, Ruud, Sean, Tim, Tobias, Verena, Vincent, Winke, and all the others. Thanks for
the fun times! I also want to thank all the non-science people I met during my stay in the
Netherlands, making it a home for four years, especially Renke, my old roommate, thanks for
all the wonderful soul food.
And of course, I want to give special thanks to Jeanette & Winke. I have mentioned you
already two times here and I can easily go on. It‘s been great 2.5 years together, in Antwerpen,
156
on the trampolines, or just at home on the couch. I hope to see you as much as possible - even
if I have to come in a zebra costume.
Weiterhin möchte ich mich bei allen bedanken, die nicht direkt zur Doktorarbeit beigetragen haben, aber den Weg davor begleitet und unterstützt haben, insbesondere Kersten, Elisabeth, Veronika und Jens. Danke für eure Tipps mit Kanada und Nijmegen - ohne euch wäre
ich wohl nie dort gelandet. Schön, dass wir uns immer noch über den Weg laufen! Außerdem
möchte ich mich bei meiner neuen Gruppe für die liebe Aufnahme in Hamburg bedanken:
Christel, Lars, Lisa3, Lukas und Mario – ich freu mich auf die neuen Projekte mit euch.
Natürlich ist die Arbeit nicht alles. Daher danke ich auch meinen (ehemalig) Dresdner
Freunden, die mich über die ganze Zeit von Nahem oder von weiter weg begleitet haben
und mich trotz der vielen Kilometer nicht abgeschrieben haben - Caro, Falko, Felix, Katha, Ines,
Jana, Maria, Mario, Niels und Thomas. Ich bin sehr froh euch alle zu haben und dass ihr mir
immer wieder zeigt wie schön und interessant die Welt außerhalb der Wissenschaft ist. Erst
Recht mit einem Patenkind.
Außerdem gebührt natürlich meiner Familie großen Dank. Euch ist die Arbeit gewidmet,
ihr habt mich all die Jahre unterstützt und mir irgendwann verziehen, dass ich kein Ingenieur
geworden bin. Ich bin froh, dass ihr immer da seid, auch wenn es uns ab und an sonst wohin
verschlägt.
Lieber Malte, der letzte Satz gehört dir, auch wenn ich es hier kurz halte. Ich danke dir für
alles was wir haben, was wir sind und was wir noch werden. Ich freu mich drauf.
A
157
Publication list
Vogel S, Klumpers F, Kroes MCW, Oplaat KT, Krugers HJ, Oitzl MS, Joëls M & Fernández G.
A stress-induced shift from trace to delay conditioning depends on the mineralocorticoid
receptor. Biological Psychiatry, Accepted for publication.
Vrijsen JN, Vogel S, Arias-Vásquez A, Franke B, Fernández G, Becker ES, Speckens A, & van
Oostrom I (2015). Remitted depressed individuals show an interaction between variance in
the mineralocorticoid receptor NR3C2 gene and childhood trauma on negative memory bias.
Psychiatric Genetics, in press.
Vogel S, Klumpers F, Krugers HJ, Fang Z, Oplaat KT, Oitzl MS, Joëls M, & Fernández G (2015).
Blocking the Mineralocorticoid Receptor in Humans Prevents the Stress-Induced Enhancement of Centromedial Amygdala Connectivity with the Dorsal Striatum. Neuropsychopharmacology, 40, 947–956.
Vogel S, Gerritsen L, van Oostrom I, Arias-Vásquez A, Rijpkema M, Joëls M, Franke B, Tendolkar
I, & Fernández G (2014). Linking genetic variants of the mineralocorticoid receptor and negative memory bias: Interaction with prior life adversity. Psychoneuroendocrinology, 40, 181-190.
Engert V, Efanov SI, Duchesne A, Vogel S, Corbo V, & Pruessner JC (2013). Differentiating anticipatory from reactive cortisol responses to psychosocial stress. Psychoneuroendocrinology,
38, 1328-1337.
Engert V, Vogel S, Efanov SI, Duchesne A, Corbo V, Ali N, & Pruessner JC (2011). Investigation
into the cross-correlation of salivary cortisol and alpha-amylase responses to psychological
stress. Psychoneuroendocrinology, 36, 1294-1302.
In preparation
Vogel S, Klumpers F, Navarro Schröder T, Oplaat KT, Krugers HJ, Oitzl MS, Joëls M, Doeller C &
Fernández G. Stress induces a shift towards striatum-dependent stimulus-response learning
via the mineralocorticoid receptor. Submitted.
Vogel S, Fernández G, Joëls M & Schwabe L. Cognitive adaptation under stress: a case1 for the
mineralocorticoid receptor. Submitted.
Kroes MCW, Tona KD, Den Ouden HEM, Vogel S, van Wingen GA, & Fernández G. How noradrenergic blockade during extinction can prevent the return of fear. Submitted.
159
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Curriculum Vitae
Susanne studied psychology at the Dresden
University of Technology in Germany. During
her studies and her internship in a specialized
walk-in clinic for panic disorder at the Charité
Berlin, she became interested in understanding
the neural and endocrinological underpinnings
and consequences of stress and anxiety. For her
diploma thesis (equivalent to a Master’s thesis),
she worked in the group of Prof. Jens Prüssner’s
at the Douglas Mental Health University Institute
in Montreal, Canada. There, she studied the association between trait anxiety and amygdala
volume in healthy young adults and investigated
how different human stress response systems
interact over time. Thereafter, Susanne moved
to the Netherlands to work in the ‘Memory and
Emotion’ group with Prof. Guillén Fernández. Her PhD project was set up in a translational and
collaborative fashion together with the University of Amsterdam and the Brain Center Rudolf
Magnus in Utrecht. In her thesis, she focused on investigating the neural mechanisms underlying stress effects on human memory encoding. Furthermore, she was involved in organizing
scientific meetings such as the Donders Summer School and was a founding member of
‘Donders Wonders’ - a blog communicating neuroscience findings to the Dutch public. After
finishing her PhD, Susanne moved on to the University of Hamburg to work with Prof. Lars
Schwabe on further investigating how stress changes memory processes in humans.
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