enhancing performance for action and perception: multisensory

Transcription

PROGRESS IN BRAIN RESEARCH
VOLUME 192
ENHANCING PERFORMANCE FOR
ACTION AND PERCEPTION: MULTISENSORY
INTEGRATION, NEUROPLASTICITY AND
NEUROPROSTHETICS, PART II
EDITED BY
ANDREA M. GREEN
Département de Physiologie, Université de Montréal
Montréal, Québec, Canada
C. ELAINE CHAPMAN
École de Réadaptation, Département de Physiologie
Université de Montréal, Montréal, Québec, Canada
JOHN F. KALASKA
Département de Physiologie, Université de Montréal
FRANCO LEPORE
Département de Psychologie, Université de Montréal
AMSTERDAM – BOSTON – HEIDELBERG – LONDON – NEW YORK – OXFORD
PARIS – SAN DIEGO – SAN FRANCISCO – SINGAPORE – SYDNEY – TOKYO
A. M. Green, C. E. Chapman, J. F. Kalaska and F. Lepore (Eds.)
Progress in Brain Research, Vol. 192
ISSN: 0079-6123
Copyright Ó 2011 Elsevier B.V. All rights reserved.
CHAPTER 13
Vision restoration after brain and retina damage:
The “residual vision activation theory”
Bernhard A. Sabel*, Petra Henrich-Noack, Anton Fedorov and Carolin Gall
Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
Abstract: Vision loss after retinal or cerebral visual injury (CVI) was long considered to be irreversible.
However, there is considerable potential for vision restoration and recovery even in adulthood. Here, we
propose the “residual vision activation theory” of how visual functions can be reactivated and restored.
CVI is usually not complete, but some structures are typically spared by the damage. They include
(i) areas of partial damage at the visual field border, (ii) “islands” of surviving tissue inside the blind
field, (iii) extrastriate pathways unaffected by the damage, and (iv) downstream, higher-level neuronal
networks. However, residual structures have a triple handicap to be fully functional: (i) fewer neurons,
(ii) lack of sufficient attentional resources because of the dominant intact hemisphere caused by
excitation/inhibition dysbalance, and (iii) disturbance in their temporal processing. Because of this
resulting activation loss, residual structures are unable to contribute much to everyday vision, and their
“non-use” further impairs synaptic strength. However, residual structures can be reactivated by
engaging them in repetitive stimulation by different means: (i) visual experience, (ii) visual training, or
(iii) noninvasive electrical brain current stimulation. These methods lead to strengthening of synaptic
transmission and synchronization of partially damaged structures (within-systems plasticity) and
downstream neuronal networks (network plasticity). Just as in normal perceptual learning, synaptic
plasticity can improve vision and lead to vision restoration. This can be induced at any time after the
lesion, at all ages and in all types of visual field impairments after retinal or brain damage (stroke,
neurotrauma, glaucoma, amblyopia, age-related macular degeneration). If and to what extent vision
restoration can be achieved is a function of the amount of residual tissue and its activation state.
However, sustained improvements require repetitive stimulation which, depending on the method, may
take days (noninvasive brain stimulation) or months (behavioral training). By becoming again engaged
in everyday vision, (re)activation of areas of residual vision outlasts the stimulation period, thus
contributing to lasting vision restoration and improvements in quality of life.
*Corresponding author.
Tel.: þ49-391-672-1800; Fax: þ49-391-672-1803
E-mail: [email protected]
DOI: 10.1016/B978-0-444-53355-5.00013-0
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Keywords: vision; restoration; rehabilitation; plasticity; current stimulation; training.
Introduction
Humans rely on vision more than on any of the
other senses, and more brain tissue is devoted to
visual perception than to all other senses combined
(Felleman and Van Essen, 1991). Thus, when the
brain is damaged, the likelihood to suffer visual
impairments is high and the consequences for
quality of life are grave. Nineteen percent of persons > 70 years have visual impairments (Centers
for Disease Control and Prevention, 2004), and
visual loss is the most feared disease in the elderly
(Aiello, 2008).
There are many possible reasons for
impairments or loss of vision after damage to the
central nervous system. Functional deficits depend
primarily on the location of the damage which may
be in the retina, optic nerve, or higher-level visual
structures of the brain. When the visual radiation
or visual cortex is damaged, homonymous sectors
of the visual field are lost, leading to scotomata or
loss of the entire half of the visual field, a condition
long known as hemianopia (Baumgarten, 1878;
Poppelreuter, 1917). The etiology of visual field
defects may be traumatic, inflammatory, or vascular, and the vision loss can proceed either acutely
(as in stroke or brain trauma) or it can progress
more slowly as in inflammatory degeneration of
the optic nerve or retinal damage (e.g., glaucoma
or age-related macular degeneration (AMD)).
Because of its retinotopic organization and
highly specific cortical organization, the visual
system is generally believed not to recover very
well after injury. The generally accepted notion
is that patients are permanently left with irreparable blindness. However, there is some hope
because vision loss is usually not complete but
partial, having variable degrees of residual visual
functions. A better understanding of how to stimulate the partially damaged visual system to
improve its function is therefore not only scientifically interesting but also clinically relevant.
Despite the lingering pessimism that vision loss
is permanent, searching new ways to help patients
regain at least some of their lost vision is a scientific and clinical responsibility.
It was long suspected that the brain had no
capability of repair after an early spontaneous
recovery phase which typically ends after the first
few weeks of injury. But in recent years, we have
witnessed scientific progress showing many
examples where vision improvements were seen
even well beyond this early recovery phase.
Vision recovery as discussed in our review, also
termed vision restoration, is limited to visual
dysfunctions caused by damage of the central nervous system, that is, retina, optic nerve, and different brain regions. We do not discuss restoration of
anterior eye problems (cornea or lense). Vision
restoration also does not assume a “complete
return” to normal function and it may manifest
itself mainly in partial and sometime also in total
recovery, depending on the individual case. The
term “vision restoration” should not be
misunderstood as implying “complete” restoration
of function at all times because the extent of restoration is always rather variable and usually not
complete. The term “restoration” should also not
be misused to raise unfounded hope in patients
with visual loss. It rather emphasizes the residual
potential of the damage system to improve its function, in whatever extent, form or shape.
Several issues cannot be discussed in detail in
this review such as the role of plasticity in normal
learning. Particularly, the study of normal “perceptual learning” was elegantly studied by other laboratories (see below) and their work should be
consulted. The present review also does not focus
on how patients who suffer from visual field defects
may be able to “compensate” for their visual field
loss by scanning the visual world more vigorously
with eye movements, thus attempting to increase
their “field of view.” It is possible that this compensation actually reduces the chance for restoration
as the subjects learn to focus their attention more
on the remaining, intact capacities.
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The aim of the present review is to first summarize the current literature on vision restoration
and on this basis formulate a new theory of the
underlying mechanisms. Although visual system
plasticity has been discussed at numerous
occasions, we still lack a coherent theory of vision
restoration after damage to the adult brain and this
should be rectified. The residual vision activation
theory was therefore conceived to provide a heuristic framework to sort and interpret the different
observations and approaches, helping to guide us
in a new and exciting research direction that provides hope for the many partially blind patients.
“Neuroengineering”-like approaches that aim
at restoring vision can be only mentioned here
in passing: besides efforts to limit the lesion
effects through neuroprotection, there are various
attempts to replace the damaged tissue itself or to
provide some alternative tissue that augments the
damaged tissue or supports its biological regeneration. These include (i) artificial retinal or brain
implants, (ii) retinal and cortical tissue
transplants, (iii) nerve regeneration, and (iv) stem
cell implantation. Because these approaches are
mostly experimental at this time, they are not
discussed in further detail here.
Whereas the neuroengineering approach aims
at replacing or augmenting the lost tissue itself—
as if trying to fill the hole of a donut—the
“neuroplasticity approach” of residual vision aims
at altering the surviving brain tissue itself. It is by
far the clinically more relevant topic and has
received a lot of attention from different groups.
Neuroplasticity studies focus on the residual (surviving) brain structures both at the site of the lesion
(local) and in the brain network as a whole (global).
While visual system plasticity is a well-described
phenomenon in the developing, normal brain at an
age well before the critical period, it is now consensus that the visual system plasticity is possible in
older age; it is observed in perceptual learning in
adults and elderly but also after different types of
brain lesions, both in animals and in man.
The present review summarizes the evidence of
post-lesion plasticity of the partially damaged adult
visual system and its clinical impact and then
formulates the residual vision activation theory.
This theory was conceived to create a unified view
of current empirical evidence of visual system repair
and to explain mechanisms of vision restoration
after lesions of the central visual pathway, including
retina, optic nerve, postchiasmatic tracts, and
radiations, striate (V1) but also extrastriate cortex.
Plasticity of the visual system
Plasticity has been observed at many different
levels of the visual system both in the normal
and in the lesioned brain. In fact, plasticity is a
rather normal, dynamic property that takes place
in normal perceptual learning.
Perceptual learning
Perceptual learning is a change in performance
following training or practice which is typically
investigated in visually healthy subjects (Fahle
and Poggio, 2002; Li et al., 2004). Perceptual
learning may improve different visual abilities
such as detection of thresholds, gratings, hyperacuity, motion, or texture (Fahle, 2002, 2005;
Fiorentini and Berardi, 1980; Gilbert et al., 2001;
Polat and Sagi, 1994). The improvements are usually specific and they do not transfer easily
between different stimuli or stimulus locations in
the visual field. This specificity is attributed to
response modifications of neuron assemblies at
the earliest visual processing stages such as V1
(Fahle, 2005; Fahle and Skrandies, 1994; Hirsch
and Gilbert, 1991). Perceptual learning may
involve training attention to discriminate distinctive stimulus features (Gibson, 1969), increase
alertness (Wolford et al., 1988), and establish
stimulus–response
associations
(correlated
activities) in sensory system of the brain.
Practice is also able to increase the range of the
lateral interactions sixfold in collinearity tasks
(Polat et al., 2004; Polat and Sagi, 1994) which
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appears to increase the efficacy of the collinear
interactions between neighboring neurons. This,
in turn, may improve the connectivity of remote
neurons via local interactions which are also
thought to be involved in receptive field (RF)
plasticity after retinal lesions (for discussion see
below).
Plasticity after retinal lesions
Plasticity after acute retinal damage
When the retina is damaged, visual impairments
can recover spontaneously and there is considerable RF reorganization in upstream areas
(Dreher et al., 2001; Eysel, 1997; Eysel and
Grüsser, 1978; Eysel et al., 1999; Gilbert and
Wiesel, 1992; Kaas et al., 1990). RF reorganization is a well-studied field showing how the brain
reacts to injury by numerous neurophysiological
changes on the molecular, cellular, and network
level (see also Huxlin, 2008). Retinal lesions are
often used in experimental animal models to
study recovery and RF plasticity.
Rather limited RF plasticity occurs in the lateral
geniculate nucleus (LGN) of the thalamus after
retina damage (Eysel and Grüsser, 1978), whereas
in the visual cortex, up to 98% of the deafferented
neurons developed new RFs within 3 months after
retinal lesion in cats (Chino et al., 1995). Cortical
reorganization is typically reflected in a displacement of the RF position and RF enlargement.
The shift of RFs following retinal lesions has been
reported both in cat's area 17 and 18 (Kaas et al.,
1990; Young et al., 2002). The properties of these
RFs are normal, except for elevation of contrast
threshold (Chino et al., 1995) and changes in temporal characteristics of response (Darian-Smith
and Gilbert, 1995; Heinen and Skavenski, 1991;
Waleszczyk et al., 2003). Lesions of both retinal
and cortical areas are typically accompanied by
reduced GABAergic inhibition and increased glutamatergic excitation, leading to an increased spontaneous activity and excitability change of visual
activity in the region of cortical scotoma (cortical
representation of retinal lesion; Giannikopoulos
and Eysel, 2006) or in regions surrounding the cortical lesion (penumbra) (Dohle et al., 2009; Eysel
et al., 1999; Imbrosci et al., 2010).
Recovery of visual responses in the silenced
area of the visual cortex is suggested to be mediated by anatomical (Darian-Smith and Gilbert,
1994) and functional changes of intrinsic cortical
horizontal connection (Calford et al., 2003; Das
and Gilbert, 1995; Palagina et al., 2009; Young
et al., 2007). Keck et al. (2008) recently observed
in adult mice with small retinal lesions a complete
reorganization of dendritic spines in the
deafferented cortex within 2 months. The rate at
which postsynaptic connections perished and
were reestablished was three times higher than
in normal brain. Smirnakis et al., (2005) have
questioned the existence of cortical reorganization as they could not see topographic changes
in the BOLD response of adult macaques 7.5
months after retinal lesion, but the BOLD
response was considered to be insensitive to
changes in RFs by most investigators in the field
(Calford et al., 2005). The body of literature on
RF reorganization in the visual system is too large
to be reviewed here and numerous publications
by the groups of Eysel and Gilbert (see above),
Chino et al. (1995), Calford et al. (2000), and
others can be readily found (Huxlin, 2008).
Plasticity in AMD
Age related macular degeneration (AMD) is a
progressive, degenerative disorder that often
causes a large scotoma in the central visual field
which leads to fixation and reading problems.
Such patients also show signs of perceptual plasticity as seen in the “filling-in” phenomenon
(Cohen et al., 2003; McManus et al., 2008;
Mendola et al., 2006; Zur and Ullman, 2003).
Another sign of perceptual plasticity in AMD
patients is that the patients spontaneously adopt
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a new preferred retinal location (PRL) to
achieve eccentric fixation (note: “preferred
locus” does not imply that it is the most optimal
locus). In such cases, patients compensate their
foveal damage by using intact (or partly intact)
regions at the edge of the damage to better be
able to fixate objects (Greenstein et al., 2008;
Schuchard and Fletcher, 1994). Eccentric fixation can also be learned which may improve
reading (Nilsson et al., 1998, 2003; Watson
et al., 2006), and, in cases where an unfavorable
PRL has spontaneously developed, behavioral
training can be used to shift the PRL to a more
optimal location (Nilsson et al., 2003; Radvay
et al., 2007). This is needed because an
untrained PRL is sometimes located in an undesirable area such as on the left side of the scotoma, that is, in a position that is not optimal
for reading. When patients are trained to relearn
a new PRL above or below the scotoma, they
may experience substantially increased reading
speed (Nilsson et al., 1998, 2003), though it is
still unclear, what the best position for such a
PRL might be.
Imaging studies showed that the visual cortex
of AMD patients shows signs of cortical reorganization in areas of the cortex that topographically
match the fovea (Baker et al., 2005, 2008). Cortical regions that previously processed only central
(foveal) visual information could now be
activated by peripheral stimuli and this reorganization is associated with development of eccentric
vision (Schumacher et al., 2008). This type of
reorganization apparently only occurs when the
functional loss at the fovea was complete, that is,
without tissue sparing. However, only small
patient numbers were studied so far, requiring
confirmation in larger clinical trials, which are
currently underway in the UK (G. Rubin, personal communication). Yet, the spontaneous
development of PRLs and the ability to retrain
their location are signs of how the visual system
uses plasticity mechanisms to adapt to the loss.
Here, intact tissue takes over the role of the
damaged regions.
Plasticity in glaucoma
Glaucoma is the leading cause of visual field loss
in all age groups (Ramrattan et al., 2001). It is a
slowly developing retinal disease where elevated
intraocular pressure leads to retinal ganglion cell
(RGC) death. In contrast to AMD, field defects
in glaucoma typically emerge from the periphery
of the visual field. This may be one of the reasons
why visual field impairments remain undetected
by the patient for a long time until nerve cell loss
has already progressed significantly with serious
field impairments. Another reason for the late
detection may be that the brain adapts to the slow
loss by plastic changes: it compensates the retinal
cell loss by some yet unknown mechanism (such
as filling-in), keeping it subclinical or below conscious perception.
While the progression of glaucoma-induced
visual field loss can mostly be arrested by proper
medication, the scotoma, once detected, is considered to be permanent, with no chance to improve.
However, some RGCs survive within the damaged
retinal regions (Pavlidis et al., 2003) and perhaps
by the process of RF plasticity, the functionally
deafferented visual cortex undergoes sensitivity
changes. Gudlin et al. (2008) have carried out a
pilot study with five patients that suffered stable
primary open-angle glaucoma and trained them
with near-threshold repetitive light stimuli (vision
restoration training, VRT). They observed
improvements in the perception of light stimuli in
perimetries of the central visual field in four
patients. This observation was later confirmed by
a randomized, placebo-controlled clinical trial with
30 glaucoma patients with stable visual field loss at
baseline who were randomized to one of two
groups: either “VRT”, or “stimulus discrimination
training” for 3 months (Gudlin, 2008). VRT significantly increased the detection performance in different perimetric tasks which confirms that even
visual loss after retinal damage can be improved
by training the visual field border.
In summary, there are signs of considerable
plasticity even in cases of retinal lesions which
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can happen spontaneously (as shown in the
PRLs) or are induced by perceptual training.
While it is possible that there is plasticity on the
retinal level as well or even in the damaged nerve
itself, it appears that the functionally relevant
change requires central visual pathway plasticity
at the level of the lateral geniculate, the visual
cortex or higher cortical networks.
Plasticity after optic nerve lesions
Prechiasmatic (optic nerve) lesions typically have
a traumatic or inflammatory origin with concentric narrowing of the visual field after compression of the outer portions of the optic nerve
fibers.
Animal models
Optic nerve damage has been a popular model to
study neuroprotection, regeneration, and functional restoration after acute complete or incomplete injury (e.g., Benowitz and Yin, 2007, 2008;
Heiduschka and Thanos, 2000; Lorber et al.,
2008). Here, we will focus our discussion on restoration of function after partial optic nerve crush
(ONC), which was studied for many years in our
laboratory. ONC can be induced in adult rats by
means of cross-action forceps which produce
definable, reproducible lesions (Duvdevani
et al., 1990). Though the rat has a relatively simple visual system compared to higher mammals,
its contrast sensitivity function is roughly comparable to that of cats, monkeys, and humans
(Keller et al., 2000). After ONC, only a definable,
small number of cells survive after the injury, thus
providing a small remnant of residual fibers (similar to Lashley's work, e.g., Lashley, 1939). If this
spares just a small, minimum number of neurons
and axons, spontaneous recovery of some visual
functions can take place such as brightness or pattern discrimination, or orientation toward small
moving targets (Duvdevani et al., 1990; Sautter
and Sabel, 1993; Sautter et al., 1991; Schmitt and
Sabel, 1996a,b).
Because the slope of recovery is typically about
2–3 weeks, ONC recovery can be correlated well
with cellular and molecular changes that accompany recovery (Sabel et al., 1995; Sautter and
Sabel, 1993; Sautter et al., 1991). Within the first
1–2 weeks, the number of RGCs is reduced by
as much as 70–90% as a result of retrograde
degeneration (Sabel et al., 1995, 1997; Sautter
and Sabel, 1993). After that time, only about
10–30% of the RGCs survive and remain
connected with their principle target, the superior
colliculus (Prilloff et al., 2007; Rousseau and
Sabel, 2001; Rousseau et al., 1999; Sabel et al.,
1997; Sautter and Sabel, 1993). Although recovery is usually incomplete, the extent of recovery
is remarkable: performance in visual tasks immediately after the damage is only 10–30% (which
corresponds to the cell number at that time) but
vision improves to about 80–90% within 2–3
weeks (Rousseau and Sabel, 2001; Sabel et al.,
1997). The surviving, residual cells show morphological and functional signs of plasticity: their cell
body size moderately increases (Rousseau and
Sabel, 2001; Rousseau et al., 1999) and their calcium activity levels rise in a delayed and moderate manner (Fig. 1), unlike the fast calcium
influx that precedes cell death (Prilloff et al.,
2007). We believe that these cellular changes contribute to vision restoration because (i) the time
course of the delayed cellular and metabolic
changes is similar to the time course of functional
recovery (Prilloff et al., 2007), and (ii) the hyperactivation of residual neurons leads to hyperresponsiveness to visual stimulation (Prilloff
et al., 2007). These correlations in time of behavioral and neurobiological changes are an example
of “within-systems” plasticity (see below).
Recovery of optic neuritis in humans
Spontaneous restoration (recovery) of vision after
optic nerve lesions is also seen in humans and
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Recovery from optic nerve damage
(a)
Visual function
80
Percentage of control
70
60
Metabolic activity (2DG)
50
40
30
No. of neurons
20
10
0
0
2
4
6
8
10
12 14 16 18
Days after crush
20
22
26
28
30
Calcium activation in residual neurons
(b)
900
Bg
*
800
*
700
RGC type I
RGC type II
RGC type III
Dying RGCs
*
600
FI (% Bg)
24
500
*
*
400
*
300
*
200
100
0
−2
Pre crush
2
4
6
10
15
Days after crush
Fig. 1. Recovery from partial optic nerve damage. The partial optic nerve crush in the rat serves as a model to study recovery from partial
visual system damage. (a) Time course of behavioral and anatomical change after optic nerve crush. Despite an ongoing loss of retinal
ganglion cells (RGCs), there is recovery of vision and metabolic (2DG) activity. The surviving cells seem to be able to compensate the
loss rather well. (b) RGCs that manage to survive (RGC type II and III) show increased calcium activation and greater responsivity to
visual stimulation starting at day 10-postlesion which is the time that significant recovery has taken place. These hyperfunctional cells
may contribute to recovery of vision (see Prilloff et al., 2007); RGCs with massive calcium influx die within 6 days.
typically happens within the first weeks and
months after damage. Recovery can happen even
if conduction speed remains impaired as evident
in longer latencies of the visual evoked potentials
(VEPs; Levin et al., 2006; Russ et al., 2002;
Werring et al., 2000). This suggests that
mechanisms of recovery are probably not
restricted to the optic nerve (within-systems
plasticity) but may also involve associated
structures along the visual pathway (network
plasticity). Korsholm et al. (2007) measured the
effects of visual stimulation with functional imaging in 19 patients recovering from acute optic
neuritis (ON) and found activations in the LGN
of the thalamus and in the visual cortex in both
the acute condition and after 3 and 6 months
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post-lesion. In the acute phase, the LGN and
visual cortex activation were significantly
reduced. The difference in activation of the intact
and the damaged eye, however, became smaller
(recovered) over time and was no longer significant at 6 months. This could be explained by an
increased activation of the retina of the damaged
eye and also an activation reduction of the retina
in the intact eye.
Patients with ON undergo cortical and subcortical neuroplasticity as revealed by functional
magnetic resonance imaging (fMRI; Korsholm
et al., 2007, 2008). While adaptive cortical reorganization in higher visual areas was not directly
observed in the Korsholm studies, extrastriate
activations may happen, which is a sign of an
adaptive reorganization of cerebral activity after
acute ON (Toosy et al., 2002, 2005). Toosy et al.
(2002) observed activations in the right insula/
claustrum, lateral parts of the temporal–parietal
cortex and in thalamus. Thus, not only the primary structure of the visual system damage is
involved in the post-lesion plasticity response
but also secondary (and probably tertiary)
structures.
In summary, activity patterns along the entire
axis of the visual system may change during spontaneous vision restoration (recovery), particularly
in extrastriate areas, and these may very well be
associated with performance improvements
(Henriksson et al., 2007; Toosy et al., 2002,
2005). It has not been resolved to which extent
these activation changes are necessary or sufficient for the recovery process, if they are adaptive
or maladaptive, and which mechanisms and
structures are involved. This needs further study.
Plasticity after post-chiasmatic lesions
In contrast to lesions of the retina and optic
nerve, damage in upstream brain regions (such
as primary visual cortex) may leave different
alternative pathways intact, depending on the
lesion location. Especially in the early literature
the question of interest was this: Which structures
are necessary for vision and how well can animals
recover when visual structures are damaged?
Spontaneous recovery of vision in animals
Lashley (1939) was, to the best of our knowledge,
the first to report recovery of vision in rats. He
found that with only small remnants of surviving
tissue, amounting to as few as 700 cells in the
LGN of the thalamus (which is about one-fiftieth
of the normal number), visual discrimination ability was maintained. After cortical lesions, rats, just
like other species (such as hamsters, hedgehogs,
tree shrews, cats, and monkeys), were initially
impaired in their ability to solve visual problems,
but over time some visual functions recovered.
To determine which brain areas are involved in
recovery from visual cortex damage, Baumann
and Spear (1977) first allowed the animals to
recover from a visual cortex lesion and then
removed additional areas of the brain. Loss of
the lateral portions of the suprasylvian gyrus left
the animals unable to recover, suggesting that this
area played a special role in recovery. Also
Fischman and Meikle (1965) suspected that other
brain areas (pretectum or the suprasylvian gyrus)
might be critical for recovery in such combined
lesion cases.
Recovery of vision has also been studied in
cats. Wiesel and Hubel (1965), for example, noted
some limited recovery in kittens with early visual
deprivation induced by eye sutures, even if the
deprived eye was reopened at a time well beyond
the “critical period.” Also in adult cats, recovery
of brightness discrimination was described after
bilateral cortex or superior colliculus removal or
simultaneous removal of both structures (in which
case additional training was required, see below)
(Urbaitis and Meikle, 1968). Only when all alternative pathways of the visual system were damaged simultaneously (total network lesion),
recovery was no longer possible (Fischman and
Meikle, 1965).
207
Since these early observations, many studies
have been published on either electrophysiological measures of cortical reorganization or behavioral measures of vision restoration and
reorganization following cortical deafferentation
or silencing by retinal lesions. This includes studies of (i) RF reorganization after retinal (Chino
et al., 1995; Gilbert and Wiesel, 1992) or cortical
lesions (Eysel, 1997) which depends in its extent
on visual experience (Milleret and Buser, 1984);
(ii) recovery from monocular deprivation during
or after the early critical period when the competing, intact eye is occluded or removed (He and
Loop, 1991; Maire-Lepoivre et al., 1988; Mitchell
et al., 1984; Smith and Holdefer, 1985; Spear
and Ganz, 1975; van Hoff-van Duin, 1976); (iii)
restoration of visual functions after additional
brain lesions which lift inhibition by competing
fibers to the deafferented zone (Di Stefano and
Gargini, 1995; Wallace et al., 1989) or after loss
of the intact, fixating eye in amblyopia (Tieman
and Hirsch, 1983); and (iv) complete or incomplete spontaneous recovery of vision after lesions
of the cortex (Baumann and Spear, 1977; De
Weerd et al., 1993, 1994; Fabre-Thorpe et al.,
1994; Wallace et al., 1989) or optic tract
(Jacobson et al., 1977, 1979).
In the macaque monkey, the primary visual
cortex and visual association areas occupy about
50% of the total cortical mantle (Van Essen and
Maunsell, 1980). Monkey studies on restoration
of vision are more rare and they typically employ
only very few animals. While specific lesions
within visual pathways usually lead to stable
deficits, there have been a few reports showing
recovery of some visual functions in monkeys
indicating that an initial loss of vision must not
always be permanent.
Zee et al. (1983), for example, created bilateral
occipital lobectomies in monkeys, rendering the
animals incapable of smooth pursuit eye movements 1 month postsurgery. In the subsequent
months, however, the function recovered to normal levels. Also Mohler and Wurtz (1977) noted
recovery in a visual detection paradigm within 3
weeks following either cortical or tectal injury,
but no recovery was seen when both lesions were
combined. Also, lesions in area MT produced
pursuit eye-movement deficits from which the
monkeys recovered within the relatively short
period of about 1 week (Dürsteler et al., 1987;
Newsome et al., 1985). This was attributed to
the relatively small size lesion.
Surprisingly, unilateral lesions produce sometimes more permanent deficits from which the
animals do not recover. Segraves et al. (1987)
offer the following explanation for this apparent
paradox: “First, the monkey with unilateral striate
lesions presumably relies upon the intact striate
cortex for input to the smooth pursuit system.
However, a monkey with a bilateral striate lesion
is left with only subcortical and residual extrastriate visual mechanisms, and so may use them
more fully. The effect is analogous to the tendency of monkeys with unilateral rhizotomies to
avoid use of the deafferented limb until the intact
one is mechanically restrained” (p. 3056). This is
related to the “Sprague effect” as discussed
below.
Spontaneous recovery of vision in humans
Traditionally, geniculostriate lesions were considered to result in complete and permanent visual
loss in the topographically related area of the
visual field (Holmes, 1918; Poppelreuter, 1917)
though the maintenance of movement perception
was noticed by some clinical investigators
(Riddoch, 1917). Lesions of the occipital cortex
typically cause contralateral visual field defects termed hemianopia or quadrantanopia, depending on
their size. Teuber et al. (1960, 1975) was among
the first to systematically observe recovery of
vision in patients. He extensively studied soldiers
with gunshot wounds of the brain acquired during
the Korea war and found better recovery in younger patients. Further, the spontaneous shrinkage of
the resultant scotoma depended on the age at
lesion (Teuber, 1975).
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In humans with brain injury, recovery of visual
functions is the rule and not the exception. But
patients with visual field defects typically have a
poor prognosis if they do not spontaneously recover
early on. The speed of recovery depends on the
lesion characteristics: whereas in cases with partial
defects maximal recovery is achieved within the first
48 h (Pambakian and Kennard, 1997; Pambakian
et al., 2005), recovery from complete hemianopia
occurs usually within the first few weeks. About half
of the patients show partial recovery and less than
10% of patients recover their full field of vision back
(Zhang et al., 2006). There are only very rare cases
of spontaneous vision recovery beyond this time
point (Poggel et al., 2001).
Nelles et al. (2002, 2007) studied patients with
ischemic lesions of the visual cortex using functional imaging. While in a control group, they
observed the maximum activity in hemifield stimulation in the contralateral visual cortex and bilaterally in the extrastriate cortex, the patients
showed increased ipsilateral activation in the
extrastriate cortex when stimulating the
hemianopic hemifield. Although many studies
are available on spontaneous and traininginduced visual field recovery, cortical reorganization processes after acquired visual cortex lesions
are rarely examined (Dilks et al., 2007).
Schoenfeld et al. (2002) described a young
hemianopic patient (age 22 years), who spontaneously recovered some motion and color perception
at 1 month post-lesion. Functional neuroimaging
showed activation of areas V4/8, V5, and V2/3 with
no activation in his damaged V1. Magnetoencephalographic recordings revealed more posterior activation areas V2/3 followed by activation
of the MTþ and V4/8 complex. Other functional
imaging studies have also shown that stimulating
fields of residual (or recovered) vision leads to
activation of extrastriate cortical regions which
was interpreted as a sign of reorganization
(Rausch et al., 2000). But other studies could not
confirm this (Barbur et al., 1993; Zeki and Ffytche,
1998). Clearly, this is an area requiring further
experimentation.
In this context, it is noteworthy that patients,
especially those with blindness early in life, show
a massive reorganization of the brain involving
multimodal activation of nonvisual senses. Here,
the brain recruits visual cortex for other
functions, for example, processing of tactile input
in reading Braille (Sadato, 2005). This type of
“transmodal plasticity” is important for the
patient to compensate their vision loss and
remain able to orient and navigate in space.
Reorganization in congenital and early blindness
Congenital and early blindness are different in
that they originate early in life, when the brain
has a considerable developmental plasticity
potential. Here, the “visual” cortex of the blind
processes somatosensory and auditory information, which suggests a rewiring of neural
associations sending nonvisual sensory information to the visual cortex (Noppeney, 2007; Ptito
and Kupers, 2005; Ptito et al., 2008). Consequently, “intermodal plasticity” appears to outpace within-systems plasticity in the early blind.
Park et al. (2007) studied the neural reorganization in the visual cortex in early blind patients
with diffusion tensor functional imaging. Mainly
in the primary visual pathway, reduced anisotropy
and increased diffusion were found compared to
emmetropic subjects. Changes in regional diffusion were observed not only in the visual pathway
but also in nonvisual areas such as the U-fibers of
the parietal lobe, the striatum, the pulvinar, and
the inferior and superior longitudinal fasciculus.
These changes are adjustments to the early loss
of visual system structures. These adjustments,
in turn, may represent hyperfunctions of other
sensory systems (especially hearing and somatosensory functions) which the blind need for orientation in space (Ptito et al., 2008).
Another study that highlights the potential of
residual capacities of congenitally blind people
was presented by Gothe et al. (2002) who applied
transcranial magnetic stimulation (TMS) to excite
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the visual cortex of patients with congenital blindness. Patients reported phosphenes in different
retinotopic positions even inside areas of perimetric blindness, and these phosphenes could
be provoked by TMS more easily in patients with
some residual vision.
Recently, a remarkable case of plasticity in congenital blindness was reported by Ostrovsky et al.
(2006). An adult subject from India, S.R.D., who
was blind from birth on until age 12 at which time
she underwent surgery for removal of dense congenital cataracts on both eyes, still had acuity
impairments at age 32, but, surprisingly, S.R.D.'s
acuity was proficient on mid- and high-level visual
tasks. The authors concluded that the human brain
retains an impressive capacity for visual learning
well into late childhood which was observed also
in three other subjects in a later study (Ostrovsky
et al., 2009).
Residual vision
Clinically relevant plasticity occurs not primarily
in regions of “absolute blindness” but in “areas
of residual vision” (ARVs). They are located at
the visual field borders and in islands of residual
vision in regions of presumed “total” blindness.
Residual vision at the visual field border
The difficulty to appreciate the existence of residual vision at all has, besides conceptual issues, a
technical origin. Current perimetric methods were
not designed to measure ARVs, visual cognition,
or subjective vision. They were designed to measure vision loss that results from eye diseases
(such as glaucoma). Thus, standard perimetry
methods are not very detailed (low resolution)
and have other limitations when applied to the
studies of the more subtle phenomenon of visual
system plasticity: they simply ignore the weaker
residual visual functions. This may be one
source of some controversy over the
interpretation of visual field expansion results
(Sabel and Trauzettel-Klosinski, 2005). Just paying attention to intact regions and damaged areas
(absolute defects) is insufficient. Rather, ARVs
that are known also as “relative defects” are key
in our understanding of visual system recovery
(Kasten et al., 1998a; Sabel, 1999; Sabel and
Kasten, 2000; Sabel et al., 2004; Widdig et al.,
2003; Zihl and von Cramon, 1979).
Thus, a more precise diagnostic with higher resolution needs to complement the existing static
supraliminal- and threshold-perimetry. Also,
visual information in daily life is normally
processed binocularly. For these reasons in our
laboratory, we usually evaluate visual fields with
both eyes open using a specifically developed
computer-based method termed “high-resolution
perimetry” (HRP; Kasten et al., 1998a,b). This
method presents suprathreshold light stimuli
repeatedly in random sequences (Kasten et al.,
1998a). As Fig. 2 shows, binocular HRP reveals
areas of inconsistent detections (gray areas),
intact areas (white), with reliable stimulus detection, and areas of absolute blindness (black
areas). The brightness of the light stimuli is of
decisive relevance for the characterization of the
intact, residual (partially damaged), and absolute
damaged areas. When using brighter (high-contrast) light stimuli, the intact visual field area
appears larger than when darker (low-contrast)
light stimuli are used. Typically, there is not a
sharp border between the damaged and intact
visual field but rather a more smooth “transition
zone” (relative defects) which varies considerably
between patients in size or shape (Kasten et al.,
1998a). These “fuzzy” transition zones are particular prominent in patients with prechiasmatic
(optic nerve) lesions as they have a rather inhomogeneous, scattered visual field defect (Fig. 2).
We have proposed that these transition zones
are the functional representation of partially damaged brain areas and termed them “ARVs”
(Fig. 3). They are the regions where plasticity
mostly occurs. Here, neurons survived the damage similar to what we found in our rat
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Areas of residual vision
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Fig. 2. Areas of residual vision (ARVs). (a) To assess the visual fields with high-resolution computer-based perimetry (HRP),
suprathreshold stimuli are presented at random from which simple detection charts (here: 3) can be created. Intact visual field sector
is shown in white; black represents regions of absolute blindness. When superimposed, the new chart (right) reveals gray areas
where response accuracy is inconsistent. They are known as ARVs or relative defects. Gray regions are interpreted by us as
representing partial damage where only some cells remain connected with their target structure. Thus, partial structure leads to
partial function (b, c). The disconnected cells will degenerate retrogradely due to lack of trophic support. (b) Different brain regions
(square) can suffer different severities of deafferentation, shown in different shades of gray in the visual field map. The extent of
deafferentation has a functional correlate: the greater the loss, the lower the functional accuracy, ranging from intact (white)
through shades of gray (i.e., ARV) to black (blind). (c) The concept of stimulation-induced synchronization after partial nervous
system damage. While neurons of the intact regions fire in a synchronized manner to drive normal vision (here they jointly fire
action potentials in perfect temporal coordination), areas of partial damage are nonsynchronized, with poor firing synchrony. In
blind (black) regions, no neuronal firing is elicited due to complete loss of neurons. After external stimulation which is induced by
training or during electric current stimulation, the partially damaged regions are forced to fire jointly in temporal coordination. It is
hypothesized that repeated stimulation induces a synaptic plasticity of the partially damaged structures (shown here) and
downstream areas (not shown here), stabilizing their synchronous firing beyond the treatment period (aftereffects). This improved
or stabilized synchronization is one of the proposed neurophysiological mechanism of vision restoration.
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(a)
Plasticity in striate and extrastriate regions
P4
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Fig. 3. Vision restoration pathways: striate and extrastriate. (a) This highly simplified diagram shows the primary and secondary visual
structures in the human brain. In the normal brain, the main retinofugal pathway is the retino-geniculo-striate pathway (striate route)
which comprises the majority (>90%) of retinofugal fibers. It supplies striate cortex and higher cortical regions with neuronal
information for normal perception. Only a small proportion of retinofugal fibers (probably <10%) travel an alternative (extrastriate)
route, that is, via the direct geniculo-V5 connection or the retino-tectal/pulvinar pathway. When a lesion damages the primary visual
pathway (e.g., lesion in V1 as shown in black here), plasticity can occur in different places (P1–P4) along the neuronal pathway so that
information traveling to higher cortical regions are facilitated: (i) via intact tissue in V1, (ii) via partially damaged, residual regions in
V1 (the gray zones surrounding the lesion), or (iii) via the extrastriate pathways. These alternative pathways mediate different kinds of
residual visions, depending on the lesion size and location. Vision restoration may be mediated by any one of these pathways or a
combination of them. (b) Examples of visual fields of two patients showing vision restoration (the x- and y-axes display degrees of
visual angle). For explanation of the charts, see legend to Fig. 2. The upper visual field charts are taken from a stroke patient suffering
from hemianopia, the lower charts from a patient suffering from optic nerve damage (Gall et al., 2010b). The stroke patient was
treated with behavioral vision training for 6 months and the optic nerve patient with alternating current stimulation (ACS) for 10 days.
Note the marked improvement of visual detection ability after the treatment in both cases. Both black and gray areas showed marked
functional improvements, leading to significant visual field expansions. These cases show that vision restoration is possible.
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experiments after optic nerve lesions (Fig. 1),
though visual information is processed at a suboptimal level. These residual structures are the primary targets for therapeutic intervention
(Kasten et al., 1998a; Sabel, 1999; Sabel and
Kasten, 2000). Both objective tests and subjective
patient reports indicate that the quality of the
visual perception in ARVs is significantly
reduced. ARVs also show increased reaction
times to stimuli of different brightness, and colors
and shapes are insufficiently discriminated
(Kasten et al., 1998b; Sabel and Kasten, 2000).
Subjectively, patients reported them as shadows
or “diffuse” vision.
ARVs, by their nature of being functionally
compromised, are also the largest source of
variability in visual field testing (Poggel et al.,
2010). Zihl et al. (1977) observed daily variations
in visual field size up to 10 . A possible cause of
fluctuations in the visual field size and intraindividual variations of residual vision are
periodic fluctuations in brain activity, in particular
in the alpha-band (8–14 Hz) (Romei et al., 2008a,
b): fluctuations in the alpha-band correlated
highly with the probability of visual stimulus
detection. Therefore, fluctuations may be an
expression of the activation state of the visual
system and this may, in part, explain the inconsistent stimulus detections in these areas of the
visual field. The activation state depends much
on the state of general attention and vigilance
(arousal).
In summary, spontaneous plasticity is a rather
common event, irrespective of where the lesion
is located along the axis of the visual pathway.
Plasticity is not a unique feature of the human
brain but can be found in all species. In fact, animal experiments have helped a lot to delineate
possible mechanisms underlying vision restoration and recovery. But besides partially injured
ARVs of the primary visual pathway, there are
other routes whereby higher cortical regions can
receive visual input after lesions of the visual
cortex.
Blindsight
Since the seminal work by Schneider (1969) and
others (Felleman and Van Essen, 1991), it is
known that the retina has different routes to send
visual information to higher cortical regions.
Lesions limited to the coronal radiation or the
visual cortex do not directly injure the “extrastriate” pathways which therefore survive the
injury (Fig. 3). In a way, damage in one region
of the visual system network leaves other detour
options intact. For many years, the role of these
surviving pathways in mediating residual vision
has been explored. Despite cortical blindness, certain levels of perceptual processing are
maintained inside the blind visual field sector.
Pöppel et al. (1973) were the first to report that
patients were able to make saccades when stimuli
were presented at different eccentricities inside
their blind fields. Later, Weiskrantz et al. (1974)
described a patient with partial occipital ablation
who was able to carry out not only manual and
saccadic localization but also discriminated line
orientations, shapes (X and O), and color by correct guessing. But because the patient was not
aware of a conscious percept, this phenomenon
was termed “blindsight” (Sanders et al., 1974).
Since then, there have been many reports of
blindsight and this field has been discussed extensively (for reviews, see Cowey and Stoerig, 1995;
Stoerig, 2006; Stoerig and Cowey, 1997). It has
since been described that motion perception is
largely maintained in the blind area as some
(but not all) patients with striate lesions are capable of detecting moving stimuli in the scotoma,
being able to indicate the direction of simple or
complex stimuli. This intact perception of visual
motion is probably mediated by a direct geniculate–V5 connections (Azzopardi and Cowey,
2001; Benson et al., 1998).
There was a controversy if blindsight is an exclusive affair of extrastriate, alternative pathways bypassing V1 (Stoerig, 1993; Weiskrantz, 1996) or if
it is rather mediated by intact residual areas of
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the primary visual cortex in islands of residual
vision (Fendrich et al., 1992, 1993; Sabel and
Kasten, 2000; Wessinger et al., 1997) (Fig. 3).
The correct answer is probably that it could
be both. The reason we believe also in a role
of residual fibers of the primary retinogenulate–striate route is that even patients
suffering optic nerve damage show blindsight
inside the blind field (Wüst et al., 2002). This
argues for the existence of partially surviving tissue
inside the blind field which is able to mediate
visual functions without awareness. Today,
blindsight is thought to be sustained by three different mechanisms (Cowey and Stoerig, 1991): (i)
extrageniculate activity via the subcortical pathways, (2) geniculo-extrastriate involvement, or
(3) partial sparing of visual cortex with preservation of cortical processing sufficient for stimuli to
reach a subjective threshold. So the issue is not
which one of these hypotheses is correct but rather
how much each of the different routes contributes
to a particular patients’ response. It seems reasonable to assume that all available retinofugal routes
are involved in vision restoration: the partially
damaged, primary structures as well as the undamaged, non-V1 projections (Fig. 3). Perhaps, it
matters less which exact residual structures contribute to vision restoration but how much activation is produced in areas downstream of V1
(V2–V5 and other higher cortical areas). Whatever
the mechanisms of blindsight are, it is indeed a
very impressive example of how the brain is capable of processing residual functions, being able to
create visual percepts by information rerouting
despite the lack of primary visual cortex.
Activating residual vision
Ever since the studies by Held and Hein (1963)
and Hubel and Wiesel (1970), it is known that
visual stimulation is required for normal visual
system development. Early in life, active visual
experience shapes the structure and function of
the visual system, particularly during the critical
period which is thought to end at the age of
around 7–10 years for binocular visual functions
in humans (Greenwald and Parks, 1999; PrietoDiaz, 2000; von Noorden, 1981). But also in the
adult brain, visual experience shapes neuronal
function, as it is known from studies of perceptual
learning. Even after damage in the adult visual
system, recovery of function happens and this is
also stimulation dependent.
There are different means of stimulating the
damaged visual system: (i) visual experience, (ii)
active behavioral training (massed practice), and
(iii) electrical brain stimulation. As we now
show, all of these methods have some capability
to activate residual visual functions.
Activating residual vision by experience
Experience-dependent plasticity is a wellresearched field in developmental neurobiology.
Deprivation early in life produces long-lasting
visual deficits, and environmental stimulation
and enrichment is beneficial for normal functional
development (Karmarker and Dan, 2006).
For example, enriched environments can enhance
recovery from amblyopia by reducing intracortical inhibition (Cooke and Bear, 2010; Sale
et al., 2007).
To study the role of experience during postlesion recovery after CNS damage, we exposed
adult rats with partial ONC during the first 4
weeks of recovery to different visual environmental conditions: (i) standard 12:12-h light:dark
cycle; (ii) visual enrichment, which consisted of a
2 h selective visual activation (strobe lights,
blinking lamps, and moving bars) and 22 h darkness; and (iii) total darkness only (Prilloff et al.,
2010). Behavioral tests with a 6-choice brightness
discrimination task revealed that rats kept under
normal daylight conditions recovered their visual
functions rather well, but rats housed in complete
darkness failed to recover. However, only 2 h of
daily exposure to visual enrichment (in an otherwise dark environment) led to significant recovery, the speed of which was even faster than in
214
rats housed under normal daylight conditions.
Thus, visual experience, even if provided for short
daily periods, is a critical factor determining the
early phases of recovery. Without any stimulation
of residual vision, recovery seems impossible.
Whereas during the early recovery phase visual
experience is critical, once a plateau has been
reached, visual experience is apparently not sufficient to generate further vision restoration. This is
largely due to the overpowering influence of the
intact visual field (see discussion below). Consequently, a specific activation of residual vision
by behavioral training or electrical stimulation is
needed.
Activating residual vision by training
Behavioral training is more demanding than environmental enrichment. It typically involves a
“massed repetition” of certain behavioral tasks
and this is carried out for up to 1 h daily for weeks
and months. The goal of visual training is not to
enhance the early recovery rate but to improve
final outcome in older lesions with stable visual
impairments. During training, visual stimuli are
presented to which the subject (animal or patient)
has to respond. In a way, training produces a synchronized firing of the residual cells in a small
region of the retina (within a few degrees of
visual angle which leads to visual field
improvements (Figs. 3–5). In contrast, after noninvasive brain electrical stimulation, the entire
visual system is simultaneously synchronized
(see section “Activating residual vision by electrical current stimulation”).
Training residual vision in animal studies
Though visual-training tasks have been studied in
rats (Spear and Barbas, 1975; Stein and
Weinberg, 1978), more studies are available in
cats and monkey. In cats, the effects of visual
stimulation are typically studied after eye closure
early in life (to simulate amblyopia). Chow and
Stewart (1972) showed that in newborn kittens
which had one eye sutured for 16–24 months,
subsequent visual stimulation (experience) by
reopening of the sutured eye, together with the
forced use of it (training), was beneficial. Upon
reopening the eye, the kittens initially appeared
not to use their deprived eye but rather relied
on their intact eye to perform visual tasks, as if
the deprived eye had been “switched off.” The
animals kept walking into objects, failed to follow
a moving light, and did not blink in response to a
suddenly approaching hand. Over the course of
several months, however, vision gradually
returned to levels where no more blindness could
be found, which is what others also found (Ganz
and Fitch, 1968; Riesen, 1961). Chow and Stewart
(1972) attributed much of the recovery to the several hundred trials of training the animals
received in a pattern discrimination task which
was carried out during the recovery phase.
Though it is unclear if the improvement was
caused by the normal visual experience or by
the training sessions, it highlights the fact that
deprivation effects must not be permanent, even
when the eye reopening occurred well after the
critical period. Also in other studies, massive
training of about 1200 trials allowed the cats to
relearn brightness discrimination (Payne et al.,
1996; Urbaitis and Meikle, 1968).
These training effects were also seen in monkeys.
Cowey (1967) studied two rhesus monkeys with
macular blindness and found that training was ineffective. Visual field defects after cortical damage, in
contrast, could be trained to improve sensitivity
threshold. Pasik and Pasik (1973) observed that
bilateral occipital cortex lesions led to an initial
blindness, but 3 months after the injury, the
monkeys showed evidence of brightness discrimination. They were now again able to reach for visual
stimuli. As in other studies, the functional improvement required massive testing (i.e., training
between 300 and 6000 trials with visual tasks).
Moore et al. (1996) described a single case of an
adult monkey with a large unilateral striate cortex
Structure–function mismatch
After training
Case 1
Baseline
Improvements in
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Fig. 4. Objective–subjective mismatch. Visual fields (HRP) of four different patients are shown before versus after 6 months of
behavioral training. Some patients show detection improvements but others do not. The detection changes not always match the
patients’ subjective visual gains as shown by activities of daily living (ADL). Some patients show visual field improvements but
do not notice this subjectively. Others show no alterations of their visual field charts but clearly subjectively report vision
restoration. This mismatch of objective versus subjective visual improvement highlights the fact that the method of measuring
detection of small visual stimuli is not sufficiently sensitive to document all aspects of vision restoration and, when taken alone,
gives us only a limited view as to the patients subjective vision.
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ablation which improved by training, lowering the
error rate from 75% to only 25%. Another monkey,
which had not profited from repeated testing at that
time, however, profited at later time points (2 years
after injury) at which point it had received a more
massive training of over 5500 trials. This animal
now also improved from a 75% error rate to about
25% (Moore, T., personal communication). Interestingly, the retrieval of residual vision was easier
when the fixation point was not present during target stimulus presentation (extinction phenomenon)
and recovered (retrained) functions were lost again
when the luminance of the target stimuli was
reduced (see also Mohler and Wurtz, 1977). A very
impressive observation was made with a trained
monkey, named Helen, that had received a bilateral
occipital lesion at the age of 19 months (Humphrey,
1970, 1974). Helen showed hardly any visually
guided behavior right after the lesion, but then
Helen first learned to reach for moving objects,
then detected a flashing light and thereafter a stationary light. Autopsy revealed that Helen had
some peripheral sparing of V1 tissue, that is, an
island of vision in the far periphery. It is this sparing
that could possibly have mediated visual performance such as stimulus detection in nonperipheral
parts of the field. Just as in other studies (Cowey,
1967), Helens functions did not develop spontaneously but took months of repeated training to
improve.
Thus, different monkey studies clearly show
that visual stimulation (training) is beneficial after
striate cortex injury, but regaining vision requires
extensive visual testing/training in the order of
thousands of stimulus presentations which is similar to the experience with patients (Kasten and
Sabel, 1995; Kasten et al., 1998b, 2006).
Training residual vision in humans
behavioral stimulation (training) aims at
strengthening residual visual structures by repetitively activating them, leading to facilitation of
synaptic transmission, probably similar to longterm potentiation (LTP). Today, behavioral stimulation (training) of residual vision is by far the
most widely used method to stimulate the injured
visual system. This includes the method of VRT
(Kasten and Sabel, 1995; Kasten et al., 1998a,b)
which was based on the observation that repeated
testing at the visual field border may induce border shifts (Zihl and von Cramon, 1979, 1985; Zihl
et al., 1977). Other laboratories have used different kinds of training paradigms, patterns of different orientations (Sahraie et al., 2006), moving
spirals (Jobke et al., 2009), flickering-type
stimulations (Henriksson et al., 2007; Raninen
et al., 2007; Roth et al., 2009), or Gabor patches
with (Polat et al., 2004) or without (Sahraie
et al., 2006) flanker-tasks to train patients. There
are only very few null-findings with training
(Balliett et al., 1985; Reinhard et al., 2005) and
no study exists that found detrimental effects.
There are two major ways to accomplish training effects in patients with brain damage: (i) by
strengthening the function of partially damaged
regions (ARVs) typically located at the visual
field border or in islands of vision inside the blind
field or (ii) by training pathways left intact after
the damage which project directly or indirectly
to higher cortical regions (i.e., those presumably
involved in blindsight, see above). The difference
between the two approaches is both conceptual
and practical. Whereas training ARVs is realized
by positioning the training stimuli in the border
zone of the lesion, the latter requires placing
training stimuli deep in the blind field. Both methods of stimulation enhance residual vision
and combining them optimizes outcome (Jobke
et al., 2009).
The body of literature on vision restoration in
humans has significantly grown in recent years
(see Table 1). As in the animal models,
Training residual vision at the border zone Let us
first consider the behavioral/clinical evidence of
training effects: training studies aimed at
217
Table 1. Studies reporting visual improvements after intervention
Study
Year
N
Preand
postdesign
Control
group
Results
(A) Vision restoration training (VRT) at the lesion border
Zihl and von
1985
44
Yes
No
Visual field improvements between (VFI) of
Cramon
1.5–38˚˜ in 80% of the patients
Balliett et al.
1985
12
Partly
No
No objective VFI (changes of the visual field
border between 2 and þ4 ); subjective
improvements in four patients
Kasten et al.
1998b 19
Yes
Yes
Visual field border shift of about 4.9 in
experimental group versus 0.9 in the control
group; subjective improvements in 72% of the
experimental group versus 17% of the control
group
Werth and
1999
22
Yes
Yes
Recovery of visual functions in 15/22 children
Moehrenschlager
with hemianopia by systematic stimulation of
cerebrally blind areas; no spontaneous
recovery in controls, expansion of visual field
>40
Kasten et al.
2001
16
Yes
Yes
VFI of about 2.7 in the experimental group
versus 0.53 in the control group
Julkunen et al.
2003
5
Yes
No
VFI between 5 and 10 in three patients;
partly validated by visual evoked potentials;
subjective improvements in four patients
Sabel et al.
2004
16
Yes
No
Reinhard et al.
2005
17
Yes
No
Werth and
Seelos
2005
17
Yes
Yes
Kasten et al.
2006
15
Yes
No
Julkunen et al.
2006
1
Yes
No
No VFI when checked with Scanning Laser
Ophthalmoscope, shift of the visual field
border about 5–7 in perimetry and computer
campimetry
No VFI in SLO; increased reading speed in 6%
of the patients; satisfaction with training in
two-third of the patients (Note: same patient
sample than Sabel et al. (2004) study
Study of children; VFI improvements only in
experimental group; control of eye movements;
validation by fMRI; 11 of 17 patients recovered
vision 1 year after the training; none in the
control group
Improvements of stimulus detection in
campimetry about 3.8%; decrease errors in
perimetry OD (2.2%) and OS (3.5%);
improvements of visual field independent from
eye movements
Normalization of P100 latencies after visual
training of the border region, increased
cerebral blood flow restricted to the occipital
lobe in a follow-up study at 3 three months
Follow-up
No
No
No
Yes
On average no
significant decline at
23 months follow-up
At 3 months followup increased or
stable area of
normal vision in
three patients
No
No
Yes
No
Yes
(Continued)
218
Table 1. Studies reporting visual improvements after intervention (Continued)
Study
Year
N
Preand
postdesign
Control
group
Results
Follow-up
Schmielau and
Wong
Kasten et al.
2007
20
Yes
No
No
2007
23
Yes
No
Marshall et al.
2008
6
Yes
No
Gall et al.
2008
85
Yes
No
Gudlin et al.
2008
5
Yes
No
Poggel et al.
2008
19
Yes
No
Jung et al.
2008
10
Yes
Yes
Romano et al.
2008
161
Yes
No
Mueller et al.
2008
17
Yes
No
Jobke et al.
2009
18
Yes
No
Poggel et al.
2010
19
Yes
No
Marshall et al.
2010
7
Yes
No
Raemaekers
et al.
2011
8
Yes
No
VFI of 11.3 on average in 17 patients; subject.
improvements in daily life
VFI in HRP (4.2%), fewer misses within the
central 30 perimetrically (3.7% OD, 4.4%
OS), VFI did not benefit from doublestimulation
Significant increase in BOLD activity in border
zone detections after VRT, relative
improvement in response times in the border
zone, brain activation changes with a shift of
attention from the nontrained seeing field to
the trained border zone
VFI < 5% detected stimuli in 42% of the
patients, 5–10% in 24% and >10% in 28% of
patients
VFI in HRP and 30 white/white perimetry
after the first treatment, stable effects after
training-free interval of 3 months
Significantly improved detection and reaction
times in perimetric and HRP-tests along the
visual field border; no improvement in visual
acuity
VRT compared to intact visual field VRT
Improved binocular reading speed, foveal
sensitivity (trend), HRP detection by 16–17%,
but in both groups
Mean absolute VFI of 12.8% after VRT,
improvements of 3% in 76% of patients
Training effects of 3.5% (OD) and 1.5% (OS)
after 6 months of daily VR training, minor
training effects of long-term training
VFI were twice as good as after extrastriate
VRT (4.2%) than after standard
VRT (2.4%)
VFI in HRP from 53.6% to 57.6%, increase of
intact field size of more than 16% or border
shifts of more than 18
Average improvement in stimulus detection
rate by microperimetry of 12.5% (range–1.4%
to 38.9%). Six of 7 patients had 3%
improvement in stimulus detection by
home-based perimetry
VFI with shifts of the central visual field
border ranging between 1 and 7
No
Yes
No
No
No
No
No
No
Yes
Yes (at 6 months)
No
No
219
Study
Year
N
Preand
postdesign
Control
group
Results
(b) Perceptual training with different stimuli inside the blind field or in amblyopia
Hyvärinen et al.
2002
5
Yes
No
Improvement of flicker sensitivity in the blind
hemifield equal to that in the normal hemifield
in two patients, increased recognition of (non-)
flickering letters at 20 eccentricity in one
patient
Polat et al.
2004
77
Yes
Yes
Training with spatial frequency tasks;
improvement in contrast sensitivity, visual
acuity improved by 78% above baseline with
the greater improvement in amblyopics with
lower initial acuity
Sahraie et al.
2006
12
Yes
No
Repeated stimulation inside the blind visual
field resulted in improvements deep in the field
defect, discrimination performance increased
monotonically with increasing contrast
Raninen et al.
2007
2
Yes
No
Improvement of flicker sensitivity in the blind
hemifield within 20 respectively 30
eccentricity, recognition of flickering letters at
10 eccentricity
Henriksson et al.
2007
1
Yes
No
Visual information of flicker training was
mainly processed in the intact hemisphere,
representation of both the intact and the blind
hemifield takes place in the same set of cortical
areas in the intact hemisphere
Chokron et al.
2008
9
Yes
No
Objective improvement of behavioral tasks in
nine patients and objective enlargement of the
visual field in 8/9 patients
Roth et al.
2009
28
Yes
No
No improvement with flicker-stimulation
training deep in the blind field
Jobke et al.
2009
18
Yes
No
Detection performance increased twice as
much after extrastriate VRT (4.2%) than after
standard VRT (2.4%)
Polat et al.
2009
5
Yes
No
Training with Gabor patterns; Visual acuity
improvement of 1.5 Snellen lines, improvement
of contrast sensitivity in children
Sahraie et al.
2010
4
Yes
No
Improved detection ability in 3/4 patients after
visual detection training of spatial grating
patches within the field defect
(C) Noninvasive alternating current stimulation of the brain
Gall et al.
2010b 1
Yes
No
Detection ability increased from 3.44% to
17.75%, mean perimetric threshold from 0 to
2.21 dB
Fedorov et al.
2010
446 Yes
No
VFI in 40.4% (OD) and 49.5% (OS) of the
patients after rtACS, significant increase of
visual acuity (OD: 0.02; OS: 0.015), further
improvement after a second treatment course
Follow-up
Yes
Yes
No
Yes
No
No
Yes
Yes
No
Yes
No
Yes
(Continued)
220
Study
Year
N
Preand
postdesign
Control
group
Results
Follow-up
Sabel et al.
2010
22
Yes
Yes
Yes
Gall et al.
2011
42
Yes
Yes
Significantly greater visual field defect
reductions in the rtACS group (69.25%) than
in the placebo group (16.93%), decrease of
reaction times in rtACS- but not in placebo
patients
Detection ability increase in the defective
visual field was significantly larger after rtACS
(41.1%) than after sham-stimulation (13.6%)
increasing the function of the border areas have
been conducted with stroke and trauma patients
who suffered hemianopia or scotomata. Zihl and
von Cramon (1979, 1985) have trained the border
itself using repetitive visual field testing. Others
have stimulated the border region after first
identifying ARVs and focusing the training area
on them (see Kasten and Sabel, 1995; Kasten
et al., 1998a,b,c). The training is aimed at the border region especially at regions of partial damage,
that is, ARVs (gray in Fig. 2). This approach is
termed VRT. Extensive training over the course
of up to 6 months leads to a reduction of the
scotoma size (Julkunen et al., 2003, 2006;
Kasten et al., 1998a,b; Marshall et al., 2008;
Mueller et al., 2008; Poggel et al., 2008; Romano
et al., 2008; Sabel and Kasten, 2000; Sabel et al.,
2004; Werth, 2008; Widdig et al., 2003), effectively enlarging the visual field by primarily transforming the ARVs into intact areas (Fig. 3).
About half to two-thirds of the patients achieve
visual field expansions (at an average of 5 degrees
of visual angle), but training success varies considerably between patients; some patients (1/3) do
not respond to the therapy, others show moderate
improvements (1/3), and yet others (1/3) have
larger types of field expansions (Mueller et al.,
2008; Romano et al., 2008; Sabel et al., 2004; Sabel
and Kasten, 2000).
No
The effects of VRT were confirmed by others
as well. Romano et al. (2008), for example, carried out a clinical observational study and found
improvements which were even superior to our
previous studies. However, while in our trials,
hemianopics were included irrespective of their
visual defect characteristics, Romano recruited
patients that had clearly identifiable ARV before
therapy, which increased the likelihood of
improvement. This points toward a special role
of ARVs in the recovery process. While patients
with ARVs respond well to therapy, those devoid
of ARVs, that is, with areas of absolute blindness
only (“sharp” visual field borders), do not benefit
as much. Therefore, when the criterion of training
success is “improvements in the field of absolute
blindness only” (ignoring improvements in
ARVs) training has no effects (Reinhard et al.,
2005; Roth et al., 2009).
Yet others used different forms of visual field
border training (Schmielau and Wong, 2007) and
found reliable improvements. Such improvements
are, however, limited: when a second 6-months
training period is given to the patients, there are
no significant, additional effects detectable, at least
not when using simple stimulus detections
(Mueller et al., 2008). Also Bergsma and van der
Wildt (2010) trained 11 subjects with cerebral
blindness with VRT, confirming a “gradual
221
shift of the visual field border” which was independent of the type of stimulus-set used during training
while eye fixation was monitored. Another interesting observation was reported by Jung et al.
(2008) who stimulated the border region in
one group of patients with anterior ischemic
optic neuropathy and the intact region in another
group. Here, detection improvements were seen
in both.
As one would expect, children of different ages
also benefit from vision training. Werth and
Moehrenschlager (1999) and Werth (2008)
looked at very young children at preschool ages,
and Mueller et al. (2008) studied older children
of school age. In these studies, significant visual
field improvements were noted as well. They benefited as much, if not more from the training than
adults, but it is unclear if the training effects at
young or adolescent age are more pronounced
than in adulthood or older age. This is an
open issue.
Not all experiments found evidence for training-induced visual field improvements in patients
with brain damage (Balliett et al., 1985; Reinhard
et al., 2005), but these studies suffer some methodological and interpretative flaws. Balliett et al.
(1985), for example, used very small stimuli for
training, and training time was much shorter (a
few weeks only) than those of all other studies
(several months). In the study by Reinhard et al.
(2005), a visual field expansion could not be confirmed with a laser scanning ophthalmoscope
(SLO), but when the more sensitive high-resolution and standard perimetry methods were used,
visual field enlargement could be found in the
same patients (Sabel et al., 2004). A closer analysis of the exact visual field topographies indicated
that SLO measurements were in fact very difficult
for patients to perform, and the detection task of
the SLO was also not the one which was trained
(Sabel et al., 2004). In fact, the SLO chart displayed ARVs as areas of absolute blindness,
suggesting that the SLO is not sufficiently sensitive to detect areas of relative loss (for details,
see Kasten et al., 2008; Sabel et al., 2004). Yet,
despite their technical and interpretative
limitations, the only two negative studies (Balliett
et al., 1985; Reinhard et al., 2005) point us to
important methodological issues, which have
been the source of some controversial discussions
(Sabel et al., 2004). In summary, the vast majority
of studies found consistently a rather positive outcome of visual training, and they outnumber
experimental studies with null findings by far.
There is also physiological and brain-imaging
evidence for training effect. Physiological
observations have the advantage of providing a
more “objective” means to document vision restoration and plasticity after visual field training.
Julkunen et al. (2003, 2006), for example,
measured VEPs before and after visual training
of the border region and showed a normalization
of P100 latencies. After therapy, the same patient
showed an increased blood flow in cortical and subcortical structures as measured by PET. In a follow-up study 3 months after the end of training,
the increased cerebral blood flow was restricted
to the occipital lobes (Julkunen et al., 2006).
Raninen et al. (2007) trained with flicker light
or flickering letters twice a week for the period
of 1 year which improved flicker sensitivity in
the blind hemifield although no evidence of visual
field changes were observed in perimetry.
Henriksson et al. (2007) found evidence for
reorganized visual cortices using both magnetoencephalography and fMRI recordings after training. The pattern of change suggests that
structures surviving the injury had now participated in the processing of visual information,
that is, the training affected not small (residual)
areas alone but had an influence on the brain network as a whole, that is, other brain regions.
This is in line with Marshall et al. (2008) who
treated six chronic, right hemianopic patients with
VRT and applied fMRI while patients were
responding to stimuli in the trained visual border
zone. The results of the trained region were compared with those of the nontrained seeing field
before and after 1 month of VRT. The authors
found a significant increase in BOLD activity in
222
border zone detections, and this correlated with a
relative improvement in response times in the
border zone. An analysis of the BOLD patterns
revealed brain activation changes that were consistent with a shift of attention from the nontrained seeing field to the trained border zone.
The effect appeared to have been mediated by
the anterior cingulate and dorsolateral frontal
cortex in conjunction with other higher-order
visual areas in the occipitotemporal and middle
temporal regions.
In summary, training of the visual field border
region does not only result in improved
parameters of vision (such as light detection in
perimetry), but it also leads to increased neuronal
activation in wider regions of neuronal networks.
Because the intact hemisphere also seems to contribute to recovery, vision restoration appears to
be the result of both local and global influences
(see discussion below).
Training alternative pathways (blindsight) Some
investigators have repeatedly trained deep in the
blind field with the goal to enhance “blindsightlike” responses. Similar to visual field border
training, they found improvements in visual detection performance (Chokron et al., 2008; Sahraie
et al., 2006). The most famous blindsight case is
patient GY (Weiskrantz, 1996, 2009; Weiskrantz
et al., 1974) who was trained (by virtue of repeated
testing) over many years and showed some
remarkable improvements throughout this time
(see also Chokron et al., 2008; Stoerig, 2008).
Sahraie et al. (2006) asked a group of 12 cortically blind patients to discriminate simple grating
stimuli for a 3-month training period. Repeated
stimulation inside the blind visual field (and not
only at the border zone, as in VRT) resulted in
improvements deep in the field defect. But
Sahraie noted (personal communication) that it
is important to start first with the stimulation of
the border region. In this kind of blindsight training it is apparently necessary to costimulate the
border region in a way that patients are able to
see some portion of the visual stimulus at the
beginning of the training, similar to “prompting”
during behavioral shaping. This may also explain
why others failed to improve visual fields: they
used a simple flickering stimulus which was presented deep in the blind field, ignoring or avoiding
ARVs (Roth et al., 2009). The need to stimulate
the border region in early training phases makes
it difficult to clearly separate an “alternative pathway” (blindsight)-training from the classic visionrestoration training. Perhaps ARVs are initially
needed for some prompting, an issue requiring
further study.
Other investigators have studied the effects of
vision training using slightly different training
paradigms. Chokron et al. (2008) treated nine
patients with unilateral occipital damage for 22
weeks using several blindsight-like forced-choice
visual tasks: pointing to visual targets, letter recognition, visual comparison between the two
hemifields, target localization, and letter identification. An improvement was found in all behavioral tasks for all patients and visual field
enlargements of the contralesional visual field
for all except one patient. Huxlin et al. trained
both animals (Huxlin and Pasternak, 2004) and
patients with brain lesions (Huxlin et al., 2009)
to perform a movement detection task. This
behavioral paradigm stimulated preferentially
extrastriate pathways that directly innervate V5
via the LGN of the thalamus or through the tectal/pulvinar route with an array of moving dots.
They found that in animals or patients with V1
lesions, movement perception could be improved.
Das and Huxlin (2010) report brain activation
changes after such a visual training task in a single
subject with cortical blindness. Before training,
they found widespread hyperactivation in
V1/V2, V3, and hMTþ of the intact hemisphere,
with no measureable activity on the damaged
side. However, after intensive global direction
discrimination training of the blind field (involving as many as 30.000 trials), the hyperactivation
of the intact hemisphere was reduced toward
223
control levels while a recovered activation pattern
was seen in regions of on the lesioned side,
including perilesional tissue (V1/V2) and V3a
and hMTþ confirmed the behavioral studies
(Huxlin et al., 2009).
Jobke et al. (2009) used a combined striate/
extrastriate training approach. Here, visual stimuli
were presented to the border region using classic
VRT, while simultaneously stimulating deep in
the blind field with a moving spiral. The aim of this
“extrastriate-VRT” (eVRT) was to create a maximal behavioral stimulation of both the residual
structures (ARVs) in the border region plus a stimulation of the entire blind field sector to activate
extrastriate (blindsight) pathways. Whereas
standard VRT significantly improved stimulus
detection by 2.9%, eVRT patients improved by
5.8%, doubling the extent of vision restoration.
This confirms the hypothesis that extrastriate
pathways, bypassing the damaged visual cortex,
can be recruited to contribute to vision restoration.
While visual border stimulation and blindsighttraining are different in principle, in practice
residual regions (“islands of residual vision”
within the blind field or border zones between
intact and blind field) are probably trained
together with the alternative pathways in most
studies. Just as in saccadic eye-movement training, it is difficult to fully avoid ARV stimulation
when presenting visual stimuli to patients with
visual field defects in blindsight paradigms. Likewise, when attempting to train only the border
regions alone, a certain proportion of stimuli
(about 20%) are located in the blind field, leading
to a “mini-blindsight”-training, that is, unintentionally also stimulating the extrastriate pathways
in the blind field. Likewise, when aiming at the
blind field only, it can hardly be avoided to also
excite residual tissue.
Training amblyopia Amblyopia is another visual
disorder where research of perceptual learning
(training) has contributed to our understanding of
residual vision. Amblyopia refers to a unilateral
or bilateral decrease of vision caused by abnormal
binocular visual experience during the “critical
period” early in life (Levi and Carkeet, 1993). It
leads to serious deficits in parameters of spatial
vision such as impairments in visual acuity (VA),
contrast sensitivity, vernier acuity, spatial distortion, spatial interactions, or contour detection (for
reviews, see Hess et al., 1990; Levi and Carkeet,
1993). On a physiological level, amblyopia is
thought to be caused by alterations in orientationselective neurons and their interactions in the
primary visual cortex (Polat, 1999).
The standard amblyopia therapy in children is
to use an eye patch of the normal eye which forces the brain to use the visual input from the weaker (amblyopic) eye (Li et al., 2005). It has been a
long-held notion that this approach is effective
only when applied up to the critical age of 8–9
years. Therefore, any recovery was seen just as
an “extension of normal development,” not an
instance of vision restoration. But even in adults
with amblyopia, recovery of visual functions can
be achieved with occlusion therapy (Wick et al.,
1992) and it also has been noted spontaneously
after the loss of vision in the good eye (El Mallah
et al., 2000). In fact, stimulating the amblyopic
eye by repetitive practice can induce plasticity in
adults, effectively improving visual functions
(Fronius et al., 2005, 2006; Levi and Polat, 1996;
Levi et al., 1997; Polat et al., 2004).
For example, Polat et al. (2004) described a
visual-training procedure specifically designed to
train the abnormal lateral interactions by probing
spatial interactions with flanker tasks. Training
consisted of a Gabor patch detection task. VA
improved by 78% above baseline with the
improvement being greater in patients with lower
initial acuity and improvements in contrast sensitivity at all spatial frequencies and in binocular
functions. The authors pointed out that long-lasting effects are typical for perceptual learning and
that it is a sign that “the high spatial frequencies
are used after the treatment in daily tasks and
thus are naturally practiced” (Polat, 2008).
224
An interesting observation was the transfer of
these improvements to other tasks. Though treatment was monocular, targeting the lateral
interactions of the amblyopic eye, led to a transfer of improvement to other, unrelated functions
such as VA and binocular functions. In contrast
to perceptual learning, where improvement is
usually specific to the trained task (Fahle, 2005),
the transfer of functions in amblyopic patients
shows that there are nonspecific elements
involved in the vision restoration process. It was
proposed that practice restored normal balance
between excitation and inhibition (Mizobe et al.,
2001; Polat, 1999; Polat and Sagi, 2006; Polat
et al., 1997). In summary, training (practice) of
visual functions is currently the most widely used
method to alter visual system plasticity and
induce vision restoration.
Compensatory (eye movement) training Another
training method for hemianopia is saccadic exploration training. As Das and Huxlin (2010)
recently summarized, there is some evidence on
how patients with cortical blindness attempt spontaneously to compensate their deficit by eye
movements toward the hemianopic field. Based
on this observation, some authors argue that
training such eye movements would actually help
patients increase visual orientation (though it usually seems not to enlarge visual fields). We do not
discuss this approach in any detail here, as training of eye-movement behavior does not aim at
vision restoration but at field of view enlargement
so that patients utilize the intact visual field
sectors more (for review, see Kerkhoff and
Schindler, 2000; Kerkhoff et al., 1992a,b, 1994).
Also, we do not believe that training patients to
move their eyes around more vigorously has a
long-lasting benefit because (i) whenever the
patient looks to the right he misses the left, and
(ii) more eye movements means greater effort
for integration of moving images. Still, it is interesting to note that patients performing saccadic
training also experience “unintended” visual field
enlargements (Kerkhoff et al., 1992a,b, 1994)
which is not really surprising because these types
of trainings never specifically avoided the simultaneous stimulation of the border regions where
residual structures are present. Actually, a recent
study found that eye movement training had no
greater effects than attention training alone (Lane
et al. 2010). This suggest that eye movement
training may actually enhance restoration and
not only compensation.
Activating residual vision by electrical current
stimulation
Invasive current stimulation methods
The attempt to restore visual circuitry by artificial
means with invasive electrical stimulation has been
around for almost 100 years. Here, the goal was to
stimulate optic nerve or visual cortex by invasive
current stimulation methods to replace or augment
lost visual input by artificial electrical signals. Historically, the first experiment of electrical stimulation to excite the visual system was reported by
Foerster (1929). He stimulated visual cortex to produce phosphene perceptions and found that their
appearance depended on where the cortex was
stimulated. These findings formed the basis of the
concept of the visual prosthesis, where local electrical stimulation in human visual cortex was used to
excite phosphenes to help facilitate visual perception. Chronic stimulation was later achieved by
electrodes which were implanted directly into cortex (Brindley and Lewin, 1968; Brindley and
Rushton, 1977; Brindley et al., 1972; Dobelle
et al., 1974, 1976; Pollen, 1977). But it turned out
that such cortical stimulation would be only of limited clinical use: the resolution was not only too
low, but it also carried a high risk of inducing
seizures in patients.
Later attempts of applying low current microstimulation of visual cortex achieved a better resolution and improved safety, although this
225
approach has never gone beyond the experimental stage. Here, it was of interest if visually perceived phosphenes are useful to create spatial
patterns of sufficiently high resolution such that
subjects would recognize objects in the environment, to check if perception was stable for
months or years, and to determine how a blind
person with very old visual cortex lesions, who
had become accustomed to the blindness, would
respond to electrical stimulation (Bak et al.,
1990; Schmidt et al., 1996). In animal
experiments, electrically evoked responses of
visual cortex were recorded during electrical stimulation of the optic nerves (Bartley and Ball,
1969; Malis and Kruger, 1956). Intact rabbit optic
nerves were stimulated by needle electrodes
implanted in the optic disc and electrically evoked
potentials (EEPs) could be recorded in the primary visual cortex (Sakaguchi et al., 2004).
More recently, optic nerves were stimulated by
an invasive method in the clinical setting by
Sakaguchi et al. (2009) using a chronically
(6 months) implanted, direct optic nerve electrode in a single blind patient with retinitis
pigmentosa. Visual sensations were elicited by
electrical stimulation through each electrode. This
type of study provided the basis for the most
recent work on retinal implants which is discussed
in Chapter 1.
There is also an early Russian tradition of
using invasive electrical stimulation approaches
to treat vision loss. Here, multiple deep brain
microelectrodes were used for subcorticography
and diagnostic stimulation for the investigation
of extrapyramidal movement disorders, central
pain, epilepsy, and obsessive–compulsive disorders (Bechtereva et al., 1972). This initial work
in the field of stereotaxic neurology was later
extended to the stimulation of damaged optic
nerves using implanted electrodes with the goal
to induce recovery of vision (Bechtereva et al.,
1985). They found significant vision recovery
after 3–4 weeks and this recovery remained stable
for over 2 years.
From our own studies, we know that visual cortex remains responsive to create visual percepts
even in cases of congenital blindness: when V1
is stimulated by TMS in congenitally blind
patients, phosphenes are still generated in
retinotopic order (Gothe et al., 2002). It is this
residual processing capacity that provides an
anchor for enhancing visual functions in the blind.
Noninvasive current stimulation methods
In contrast to invasive approaches, noninvasive
approaches are aimed at influencing brain physiology on a network level and this, in turn, might
affect sensitization of deafferented regions or synchronization (entrainment) of neuronal network
firing with long-lasting (plasticity) changes
(so-called aftereffects, see Zaehle et al., 2010).
These methods do not aim at “replacing” the lost
retinal cells or neuronal circuitry or stimulating
brain nuclei locally which is what retina or brain
implants try to achieve.
The work on noninvasive electrical current
stimulation in the visual system was first developed in Russia, where Bechtereva's started off
with invasive methods using specific stimulation
protocols (see above). These protocols were subsequently applied also noninvasively in patients
with visual system damage (Chibisova et al.,
2001; Fedorov et al., 2005). Here, electrodes were
attached to the eye orbit and repetitive,
transorbital, alternating current stimulation
(rtACS) was applied. In a large clinical observational study of 446 patients with optic nerve damage, they measured visual fields before and after
10 days of rtACS treatment (Fedorov et al.,
2010). rtACS led to significant VA improvements
and visual field enlargements. On average, visual
field sizes improved by up to 9% over baseline.
Also, VA significantly increased in both eyes.
In a subsequent double-blind and placebo-controlled clinical trial, optic nerve patients were randomly assigned to an rtACS or a sham group
226
(Sabel et al., 2010). The treatment was given daily
for 20–40 min for 10 days (EBS Technologies
GmbH, Kleinmachnow, Germany). In the rtACS
group, significant vision improvements were seen
in detection accuracy evident as a shrinkage of
the scotoma by > 40% change from baseline,
reaction time ( 19.63 ms), static near-threshold
perimetry, and VA (Fig. 5). The improvements
of visual functioning in the rtACS group were stable at a 2-month treatment-free follow-up, and
they were associated with improvement in the
patient's quality of life as assessed by standard
questionnaires. Thus, noninvasive current stimulation using rtACS can be used to reduce visual
field defects in patients with long-term optic
nerve lesions.
Electroencephalogram (EEG) power-spectra
analysis also showed significantly increased
alpha-activity, especially in occipital sites following rtACS (data not published, see Fig. 6). In
view of these findings, we proposed that rtACS
leads to increased neuronal network synchronization which is substantiated by lasting bilateral synchronous waves of alpha- and theta-ranges in
central and occipital brain areas. This “synchronization hypothesis” assumes that by firing “artificial”
electrical
trains
of
impulses
at
predetermined frequencies to the brain, neuronal
Visual field improvement after repetitive transorbital
alternating current stimulation
(b)
(a)
1
3
3
1
4
Baseline
2
2
2
Reduction of visual
field defect (%)
(c)
1
4
3
100
rtACS-group (n = 12)
80
Sham-group (n = 10)
60
4
(d)
After rtACS
69.25**
40
20
16.93*
0
Fig. 5. Visual field dynamics after alternating current stimulation. Patients with optic nerve damage were treated with repetitive,
transorbital, noninvasive brain stimulation (rtACS, EBS Technologies GmbH, Kleinmachnow, Germany). (a) The montages of
the electrodes which were placed on the skin around the eye ball. (b and d) The visual field charts before and after 10 days of
rtACS in a single case with traumatic optic neuropathy (Gall et al., 2010b, for explanation of charts, see Fig. 2). As
demonstrated by the brightening of the chart, the patient improved from 3% detection performance to 23%. The area of
improvement was located in the lower left quadrant which already had some minimal residual vision even before therapy. (c)
The group results of a double blind, randomized, placebo-controlled study (unpublished).
227
Power spectra changes after alternating current stimulation
(a)
(b)
10
5
0
–5
–10
Pre
*
-15
F7
*
*
-11,7
Fp2
Fp1
Fz
F3
-8,1
9,5
FC5
FC1
*
*
10,5
-15
-10,3
FC6
*
-10,9
C3
Cz
*
-8,6
*
T8
-5,8
-6,1
*
-10,9
CP1
CP2
*
11,5
-7,4
*
P3
*
CP6
Post
P7
-10
C4
*
13
F8
*
FC2
*
T7
CP5
*
F4
Pz
P4
30,4
P8
*
11,5
O1
0
Alpha power spectra, mV2
*
O2
95
Slow waves activity
Alpha activity
Fig. 6. rtACS and EEG power spectra changes. When patients with partial optic nerve lesions are stimulated by noninvasive,
alternating currents (rtACS), this leads to lasting EEG power spectra changes. (a) Alpha activation in the brain of a single
patient before and 24 h after a 10-day rtACS treatment. Alpha activity was highest in posterior brain region before treatment,
where the visual cortex is located. After 10 days, alpha power increased across the brain, extending more anteriorally. (b)
Results of a clinical trial shows average EEG changes in a group of optic nerve patients. The bars show the power of alpha and
slow wave activity in different brain regions. Stars indicate significant changes. The percentage change were 11% and 30% of
alpha power at occipital sites (O1 and O2), after rtACS while alpha activity increased slow waves decreased in different brain
regions, where primary visual cortex is located. This was not seen in a sham group. These EEG power changes are indicative of
an increased synchronization state of the brain which outlasts the stimulation period.
networks are forced to propagate synchronous firing which, when repeated many times, induces a
“learned synchronization response (LSR)” in the
damaged pathways. This idea of LSR is compatible with the observation that synchronization
can be entrained by external, transcranial pulsed
stimulations and such alpha entrainment has
already been observed in normal subjects (Zaehle
et al., 2010). As a consequence of this increased
synchronization, the injured visual system reacts
to the reduced and unchanged input in a more
sensitive manner (supersensitivity), similar to
what the brain does on its own during the natural
or training-induced recovery phase where spontaneous visual phosphenes are seen (Poggel et al.,
2007; Tan and Sabel, 2006; Tan et al., 2006).
228
Here, one can think of cortical plasticity as serving
the role of an “amplifier” that increases the signal
above noise in an area with reduced visual input.
There are other studies using noninvasive
visual system stimulation, particular with transcranial direct current stimulation (tDCS)
protocols: in normal subjects, tDCS induces
changes in phosphene thresholds and excitability
(plasticity) of the human primary visual cortex
(Antal et al., 2003), affecting different visual perception phenomena (Antal et al., 2006, 2008;
Chaieb et al., 2008).
The only other alternating current stimulation
study is one from a Japanese group (Fujikado
et al., 2006) who applied transcorneal electrical
stimulation (TES) in patients with ischemic optic
neuropathy. TES was applied only once for
30 min at 600–800 mA with a frequency of 20 Hz
and this led to improvements in VA in six of eight
treated patients. Due to the small sample, a definitive conclusion about this approach is still pending. Also, Inomata et al. (2007) studied TES of
the retina to treat longstanding retinal artery
occlusion. Here, TES (20 Hz biphasic pulses,
30 min, up to 1100 mA) was delivered by a bipolar
contact lens electrode once a month for 3 months.
VA was found to have improved in two cases, and
the visual fields were improved in all three cases.
Improvements in the electroretinogram indicate
some recovery of function distal to RGCs which
may explain the visual field improvements.
When viewed together, noninvasive current
applications can (i) provoke visual percepts
(phosphenes) in visual cortex, (ii) lead to excitability changes in visual cortex and other brain
structures, and (iii) improve visual functions after
damage to the optic nerve showing some therapeutic efficacy. This is quite similar to what is
seen after visual training (Fig. 7).
Vision restoration, subjective vision, and activities
of daily life
Improvement of psychophysical parameters or
plasticity of RF changes may be of great interest
to scientists, but unless vision restoration is shown
to be clinically relevant, contributing to a higher
quality of life, clinicians will not pay attention
and patients will not become aware of this new
vision restoration option nor use it.
Obviously, vision loss and blindness have a
much feared negative impact on functional
abilities and quality of life. In patients where the
visual field loss is caused by cerebral damage,
the reduction of quality-of-life domains is mainly
due to problems in reading, driving, visual clarity,
and peripheral vision (Gall et al., 2009, 2010a,c).
The status of vision-related quality of life is somewhat dependent on the size of visual field loss
after damage to the post-chiasmatic (Gall et al.,
2008; Papageorgiou et al., 2007) or prechiasmatic
pathway (Cole et al., 2000). These impairments
are typically assessed by perimetry and VA tests,
but this type of evaluation may fail to assess certain aspects of visual disability that are identified
by visually impaired persons as being important
for their daily well-being (see below). The optimal approach to measure quality of life in vision
research is therefore to measure both visionrelated and health-related quality of life (Franke
and Gall, 2008).
Subjective improvements after visual border
training
As discussed above, different types of VRTs can
improve stimulus detection in patients with
postchiasmatic and optic nerve lesions (Julkunen
et al., 2003; Kasten et al., 1998a,b; Sabel et al.,
2004). Here, about two-third of the patients
reported subjective improvements as measured
in post-training interviews (Mueller et al., 2003)
or by analysis of pre- and post-training drawings
of subjective visual field sizes (Poggel et al.,
2008). Other studies have developed their own
methods and confirmed subjective improvements
(Chokron et al., 2008; Julkunen et al., 2003; Sabel
et al., 2004). Everyday life activities were also
recorded in hemianopic patients by structured
229
Visual field size improvements after optic nerve damage
30
No. of patients
25
Mean change after
rtACS (10 days)
39.6%
Mean change after
behavioral training
(VRT, 6 months)
34.7%
20
15
10
5
0
0 15 30 45 60 75 90
120 150
0 10 20 30 40 50 60 70 80 90 100
Relative improvement of detection accuracy (%)
120
140
Fig. 7. Vision restoration after optic nerve damage: rtACS treatment versus behavioral training. To compare the extent of recovery
from optic nerve damage after rtACS with vision restoration training (VRT), we plotted the number of patients versus the different
improvement levels (in percent change over baseline). As the figure demonstrates, 10 days of rtACS resulted in similar activation of
residual vision (39.6% detection improvement over baseline) as 6 months of visual training (34.7% improvement). The data were
taken from different studies (rtACS: Fedorov et al., 2010; VRT: Kasten et al., 1998b) but plotted on the same scale to allow
comparison.
post-training interviews in a larger sample
(n ¼ 69) (Mueller et al., 2003). Here, the percentage of patients reporting training-induced subjective improvements were as follows: reading
(43.5%), ability to avoid collisions (31.9%), general vision improvement (47.8%), ability to perform hobby activities (29%), and confidence in
mobility (75.4%). Objective improvements of
visual field parameters correlated significantly
with the number of named activities of daily living
categories, but not all patients who reported subjective improvements also showed objective
improvements in perimetry results, that is, there
was a certain number of cases with a “mismatch”
(see below).
To try getting a better handle on subjective
vision, we recently adopted the National Eye Institute-Visual Functioning Questionnaire (NEIVFQ) as a standardized instrument to assess
vision-related quality of life and found significant
improvements after visual field training in
hemianopic patients (Gall et al., 2008) which were
also correlated with objective perimetry results.
Given that questionnaires are sufficiently sensitive
to detect VFI, standardized questionnaires of
health- and especially vision-related quality of
life should be used on a regular basis in future
rehabilitation studies (Bouwmeester et al., 2007).
This will enhance our understanding of the
clinical relevance of functional improvements
230
and standardized methods can also be more easily
compared between laboratories.
Subjective improvements after noninvasive
current stimulation
One may argue that patients having undergone
a long and laborious training for many months
may be biased to report subjective visual
improvements after such a substantial effort and
time commitment. We therefore used a nontraining type therapy, rtACS, which might be less
prone to such artifacts. We have measured subjective visual functioning and vision-related quality of life before and after rtACS and assessed
self-estimated visual and health-related quality
of life (Gall et al., 2011). rtACS led to partial restoration of visual fields which was accompanied
by improvements of vision-related quality of life
(NEI-VFQ) and health-related quality of life
(Short Form Health Survey, SF-36). Some, but
not all, NEI-VFQ scales were sensitive to
improvements in visual field size after rtACS,
and particularly, the subscale “general vision”
improved to a clinically relevant extent in the
rtACS group. The improvements were dependent
on the magnitude of the visual field expansion: rtACS-treated patients with detection
improvements > 20% had a significantly greater
increase in NEI-VFQ scores than patients with
smaller detection improvements (< 20%). Thus,
rtACS treatment is capable of modifying the adult
visual system in a noninvasive manner and this is
of subjective, functional relevance to the patients’
everyday life.
It is interesting to note that only some NEIVFQ scales were sensitive to visual field
expansions after visual training or rtACS. In any
event, vision restoration studies ought to include
assessments of vision-related quality of life, a
meaningful and valuable complement to objective
visual field data that better reflects on the
patient's individual self-perceived situation.
Because the correlation of both is modest at best,
these assessments represent different aspects of
vision. One reason why this relationship between
subjective and objective visual measures is only
small to moderate is the mismatch problem, an
issue that adds complexity to the discussion of
vision restoration (see section “The mismatch
problem”). In any event, a definitive advantage
of using questionnaires such as NEI-VFQ is that
they help to weigh the risk (ratio of effort/cost)
and benefits of interventions.
The mismatch problem
Visual field impairments are typically assessed by
perimetry. However, perimetry was not designed
to assess vision in everyday life, and the detection
of small dots presented on ambient background is
not a typical real life event. The visual world is much
more complex, comprising different shapes, colors,
contours, cluttered scenes, moving objects, etc.
In reference to vision restoration, the question is
frequently asked how perimetric improvements
and everyday vision relate. Also, critics claim that
self-perceived training effects may be “only psychological” or “subjective” and therefore “not real.”
We have found that there is only a small to
moderate overlap of subjective vision and
perimetric measures. In many patients, perimetric
improvements are associated with subjective
improvements, but in other patients, there is seemingly a mismatch: subjective improvements can be
reported without visual field expansions and, vice
versa, visual field expansions may happen without
being subjectively noticed (Mueller et al., 2003;
Fig. 4). Also Chokron et al. (2008) described a
patient who experienced a progression of subjective improvement after vision training despite lack
of improvement in perimetry. Of course, the location and size of the scotoma has a large influence
on individual subjective vision and this alone can
account for some of the unexplained variance.
For example, a gain of visual field size at or near
fixation has a much greater subjective impact than
peripheral visual field gains (Poggel et al., 2008).
231
But both the low correlations and the mismatch
problem raise another possibility: other factors of
vision might account for this mismatch: (i) the
“intact field” also has subtle deficits in visual cognition (contour integration deficits), (ii) temporal
processing (reaction time) is impaired, (iii) spatial
resolution (VA) is reduced, and (iv) steady fixation of the eyes and eye-movement control may
be impaired, making the perception of stationary
or moving objects more demanding (Mueller
et al., 2003; Paramei and Sabel, 2008; Schadow
et al., 2009).
In the context of a discussion on residual vision,
the issue of subjective visual improvements is complex because everyday life vision is dependent on
different factors: (i) visual field size, (ii) exact location of the field defect (foveal vs. peripheral), (iii)
deficits in the “intact” field sector, (iv) temporal
processing deficits, (v) decline in spatial resolution,
and (vi) variable degrees of residual vision at the
border zone or deep in the blind field (with unconscious elements of vision (blindsight). Further, (vii)
fixation accuracy and (viii) eye movements are
part of the subjective vision equation.
Thus, mismatch cases, where subjective vision
improves while the visual field size remains
unchanged, cannot disprove vision restoration as
being “purely psychological.” We rather propose
that functions other than those tested with perimetry
have improved as well. Indeed, VRT speeds up reaction time (Kasten and Sabel, 1995; Kasten et al.,
1998b; Mueller et al., 2003), increases VA (e.g.,
Kasten et al., 1998b), and improves fixation accuracy
(Kasten et al., 1998b). Just as subjective visual
impairments are a multifactorial and rather complex
affair, so is the subjective improvement associated
with vision restoration (Poggel et al., 2008).
Alternative explanations of vision restoration
The claim that vision restoration is possible at all
after lesions in the adult brain is shared by many
scientists (see below), but it has also attracted
some opposition. Although most critics have not
actually studied vision restoration experimentally,
their theoretical arguments are based on circumstantial evidences but nevertheless raised considerable debate. Yet, this discussion is valuable as
it directs our attention to possible alternative
explanations which need to be carefully considered, particularly related to the following issues.
Vision restoration is just normal learning
Some sceptics have argued that vision is just an
effect of perceptual learning and that there may
be no true restoration of vision. We concur with
the argument and believe that perceptual learning
actually is an important element when the brain
tries to repair the damage. To the best of our
knowledge, no author studying vision restoration
has claimed that restoration requires pathologyinduced repair mechanism(s). In fact, just as in
normal perceptual learning—which requires
many repetitions (Fahle and Poggio, 2002) in
massed practice sessions (see above)—vision restoration is not easily accomplished either. It also
requires many stimulus presentations, in the
order of 50,000–100,000, which usually takes
months of laborious work. Here, it is not only
the intact structure that is involved, but rather
perceptual learning takes place within the partially damaged structures (within-systems plasticity) or the remaining (even intact) neuronal
networks (network plasticity).
Is restoration just the result of spontaneous
recovery?
This argument is frequently raised but ignores
that all vision stimulation procedures such as
training or current stimulation were given to
patients with lesions that were many years old
(e.g., 6.8 years in Kasten et al., 1998a,b). Because
spontaneous recovery is only rarely seen beyond
6 months postlesion (see discussion above), these
232
clinical improvements many years after damage
cannot be explained by spontaneous recovery.
Functional improvements are caused by
attention changes
Similar to the “its-just-learning” argument, we
agree that there is a special role of attention in
vision restoration. Attention is, in fact, a major
contributing factor to vision restoration. Both
behavioral and brain-imaging evidence exist
supporting the special role of attention in restoration. Just as in normal perceptual learning, attention is a necessary requirement for improving
visual functions and also for long-term and stable
vision restoration after visual system damage.
Is vision restoration an artifact of eye
movements?
This is perhaps the most serious concern when
interpreting vision restoration studies. The issue
was raised that vision restoration is not due to
increased visual detection (perceptual improvement), but that the visual border shift can rather
be explained by increased eye movements toward
the scotoma after training, leading to an apparent,
but not real, shift of the visual field border.
In principle, there are different ways how eye
movements could influence diagnostic testing
and only “mimic” a visual field expansion: (i) the
eyes could scan more in both directions, to the
right or left side; (ii) the eyes could intermittently
and preferentially saccade toward the visual field
border, resulting in an artificial shift of the scotoma away from fixation; and (iii) the eye position
could permanently shift toward the hemianopic
side, which would require the establishment of
eccentric fixation which patients with central
vision loss, such as AMD patients, regularly do.
First of all, there are some logical problems
with these possibilities. Let us consider the three
cases during the post-therapy perimetric assessment: (i) firstly, if the eyes would scan more in
both directions, the patient would have as many
detection gains by looking toward the blind side
as detection losses by looking to the other side;
(ii) if the patient would intermittently scan toward
the hemianopic side only, then the patient would
not only have to move the eyes just prior to the
short stimulus presentation (which cannot be anticipated). Doing this while having to pay attention
to the fixation point would be an extremely hard
task. (iii) Stable, eccentric fixation does not occur
in hemianopic patients who are able to fixate well
(an inclusion criterion in restoration studies); also
the blind spot remains in its expected place.
Besides these arguments of logic, there are
many experimental indications why the “eyemovement artifact hypothesis” is unreasonable.
1. The nature of the visual field border: If the
“eye-movement artifact hypothesis” was correct, one would expect that the training-induced
visual field border shifts as a whole to one side.
However, the border shift dynamics are rather
variable: some patients show a shift of the entire
border to the hemianopic side, while others
show a shift only in one sector of the border
(Fig. 8a). Also, in patients with glaucoma, the
visual field borders move in a ring-like centrifugal direction toward the periphery (Gudlin
et al., 2008) which is incompatible with moving
eyes preferentially toward one side. Thus, the
local border shift and the centropedal border
shift dynamics are incompatible with the eyemovement theory.
2. Blind spot position: If patients would intermittently or continuously move their eyes toward
the scotoma, the position of the blind spot
would shift, which is not what is seen (Kasten
et al., 2006).
3. Measuring
eye
movements:
Actual
measurements of the eye positions with an
eye tracker before versus after training are
the most direct way to clarify the role of eye
movements in restoration. We found no
increase but rather a decrease of eye movements after VRT (Fig. 8b) (Kasten et al.,
233
(a)
Vision restoration and eye movements
−25 −20 −15 −10 −5
(b)
0
5
10 15 20
20
20
15
15
10
10
5
5
0
0
−5
−5
−10
−10
−15
−15
−20
−20
−5 0
25
5
10 15 20
25
Average visual field border shift (5°)
60
50
%
40
Left
Right
30
Pre
Post
20
10
0
>−5° −5° −4° −3°
−2° −1°
1°
2°
3°
4°
5°
>5°
Fig. 8. Vision restoration and eye movements. Eye movements need to be observed in studies of vision restoration. (a) In a patient
with a complete upper right and incomplete lower right damage before and after vision restoration training (VRT). Visual field
improvement occurred mainly in the lower right quadrant shifting the relative border; but the upper visual field border did not
change (the arrows indicate the border position parallel to the vertical midline). If eye movements were responsible for the
border shift, then the entire border would be expected to shift position, not just the lower half. (b) Eye movements can also be
directly measured before and after VRT. This graph shows the time spent at fixation or to the right or left. A pre- versus posttraining comparison did not find any differences. If anything, fixation improved (see Kasten et al., 2006). Thus, eye movements
cannot explain visual field border shifts and therefore vision restoration is real.
2006). There was also no evidence for preferred directions of the eye movements before
or after restoration. Moreover, the eye movements were rather small: 95% of the times
the eyes were positioned 2 around fixation
before training and 99% after training, that
is, fixation quality actually increased.
4. Eye movement-adjusted retinal charts: Another
approach to determine the role of eye movements is to adjust visual field charts as a function of eye movements. When this is done
(Fig. 9), stimulus detections (hits) after training
are observed in areas of the visual field sector
that previously had been blind.
5. Fixation performance: If eye movements were
more frequent after VRT, then one would
expect fixation performance to worsen. Actually,
there is no reason to assume that training would
induce patients to start moving their eyes around
more because the training task requires stable
fixation performance. In fact, none of the patients
carrying out training developed eccentric fixation
(Reinhard et al., 2005) and fixation performance
actually improved in all of our prior studies.
234
Eye tracker adjusted visual charts
15
(a)
15
(b)
10
10
5
5
0
80 %
Y
100 %
0
–5
–5
–10
–10
Before
60 %
–15
–20
–15
–10
–5
0
5
10
15
–15
20
40 %
–20
15
15
(c)
10
20 %
–10
0
10
20
0
10
20
(d)
10
5
5
0
After
–20
–15
–10
–5
0
5
10
15
Y
0%
0
–5
–5
–10
–10
–15
–15
20
–20
–10
X
160
**
No. of stimuli
140
120
n = 16
100
80
**
60
40
20
Pre Post
Missed stimuli
Pre Post
Hits
Fig. 9. Eye-tracker adjusted visual charts. Eye movements always occur during perimetric assessment, even when patients are
instructed to fixate well. To evaluate the role of eye movements in restoration, an eye-tracker adjusted visual field chart was
calculated in which the position of the recording was adjusted to accommodate the position of the eye at the time of stimulus
presentation. Upper graph: The left(a, c) shows HRP charts before and after VRT. On the right, the adjusted charts are
displayed. Clearly, the improved areas as shown in (c) can also be found when the stimulus positions are adjusted as a function
of the eye position at the time of presentation. Numerous stimulus detections (hits) are now seen on the far right after VRT (d)
in the previous area of absolute blindness (shown in (b)). Lower panel shows the average results of a group of patients (n ¼ 16).
Detection performance was expressed as number of hits (left) and number of misses (right) inside the previously blind field only.
Whereas the number of misses significantly decreased, the number of hits significantly increased.
6. Locally induced visual field border shifts:
Training residual vision with an attention
cue amplifies restoration precisely in the
region where the cue was positioned (Fig. 10).
This leads to a selective and regionally
restricted visual field border shift which can
also not be achieved by eye movements.
235
Vision restoration and attention
Acute attention
Chronic attention (training)
20⬚
15
10
5
0⬚
0
–5
-10
-20⬚
-15
-20 -15 -10 -5
0
5
10
15
20
15
100 %
80 %
60 %
40 %
20 %
0%
Fixation
point
10
5
0
-5
-10
-15
-20 -15 -10 -5
0
5
10 15 20
Fig. 10. Vision restoration and attention. To evaluate the role of attention in vision restoration, hemianopic patients were asked to
focus their attention to a cue in the shape of a square (attentional spotlight) that was positioned on the border region. The left panel
shows the immediate effects of such local attention on the visual field. When the cue (square) is placed on the upper visual field
border, the number of stimulus detections is increased compared to a comparable square shaped region without attention (lower
visual field). Thus, attention led to residual functions in the previously blind regions (see Poggel et al., 2006). The panels on the
right show the effects of daily attention training over the course of 6 months with a cue positioned also on the visual field
border. Vision restoration developed precisely inside the region (square) which was activated by the attentional spot light
(Poggel et al., 2004). This shows that attention is a key factor in vision restoration.
7. Visual field improvements were recently confirmed with microperimetry, which allows the
exclusion of eye movement artifacts (Marshall
et al., 2010).
While we acknowledge that the majority of visual
stimulus presentations are given when the eye is not
exactly at fixation, there is no evidence of a change or
induction of preferred saccades toward the scotoma
after restoration training. Actually, eye movements
are a physiological necessity and they do always
occur. Though eye movements cannot explain restoration, they are always a possible source of error
(variability) in visual field diagnosis. Monitoring
their influence therefore helps to control and reduce
the variability which increases the validity of vision
restoration measurements. In summary, eye-movement artifact cannot rule out the available experimental evidence in favor of vision restoration.
Factors influencing vision restoration
To understand mechanisms of restoration and to
optimize outcome, several potential factors have
to be considered: patient demographics, the
nature of the disease, and the topography of the
specific visual field defects and transfer effects.
236
Patient parameters
Age of the patient (at least after early adulthood)
has no major influence on vision restoration outcome (Mueller et al., 2007; Zihl and von Cramon,
1979, 1985). The activation of residual vision and
plasticity is also not gender dependent (Mueller
et al., 2007). It is possible that restoration is
greater in children before or at school age
(Werth, 2008; Werth and Seelos, 2005), but outcome of children versus adults has never been
directly compared.
Lesion parameters
Lesion age
Our experience is that lesion age (at least at times
beyond 6 months) has little, if any, influence on
training-induced vision restoration. Although
most of the spontaneous visual field recovery
occurs early after the lesion, all visual field restoration studies (using training or noninvasive brain
stimulation) have treated patients with lesions
older than 6 months. Having very old lesions
was of no apparent disadvantage for prognosis.
Little is known if vision restoration is more effective when applied during the very early spontaneous recovery phase. Our preliminary studies did
not find any evidence for this and patients tended
to actually do worse if behavioral training started
earlier (Mueller et al., 2006), though this needs
further study before a conclusion can be reached.
Lesion type
Vision restoration occurs no matter where the
lesion is located along the visual system pathway.
Contrary to our intuition, more peripheral (retinal and optic nerve) lesions tend to have greater
restoration potential than central lesions of the
visual radiation or visual cortex (Kasten et al.,
1998a,b). This is surprising but highlights the
special role of visual cortex in post-lesion plasticity. Perhaps “cortical amplification” simply works
better when the cortex is not deafferented or
damaged directly.
Visual field defect type
It does not matter whether the visual field defect
is a smaller type of scotoma, a quadrantanopia,
a hemianopia, or a peripheral, concentric visual
field loss (as in glaucoma). All lesion types may
respond to treatment, with no major difference
between any of them. The only known parameter
that matters for restoration, though, is the size
and topography of ARVs (Guenther et al.,
2009). This is in line with the argument that vision
plasticity is mediated by residual structures.
Visual field topography
Visual field defects may have areas of absolute
blindness or ARVs (relative defects), where
patients respond unreliably to visual detection
tasks (Fig. 2). The size of these ARVs, that is,
the degree of residual vision, is currently the only
factor that has a notable influence on outcome.
Though large ARVs are no guarantee that
functions will improve after therapy, the size of
the ARVs is positively correlated with outcome.
A detailed analysis of the visual field topography
also attests to the special role of this factor (see
below). Here, self-organizing map (SOM) chart
analyses revealed that the vision restoration hot
spots are not randomly distributed but they are
a function of the amount of residual activity in
the immediate surround (Guenther et al., 2009).
For more detail, see legend to Fig. 11. In our
experience, 80% of the visual field locations that
improve (vision restoration hot spots) are located
in the ARVs; only 20% are found deep in the
blind field (unpublished observation).
Though stimulation of only the field of absolute
blindness has been tried by several authors,
237
(a) Dynamic chart construction
Vertical visual angle [°]
Baseline chart
Dynamic chart
Posttraining chart
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(b) Feature calculation: neighborhood activity
(c) Self-organizing maps (SOM): restoration hot spots
15.2 mm
0 mm
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Distance to
scotoma
Neighborhood
activity
Defectdepth
Quadrantanopia
Hemianopia
Fig. 11. Factors influencing vision restoration hot spots. To study factors (features) of the visual field defect that influence whether a
given spot of the visual field can recover, “restoration hot spots” (improved vision) and “cold spots” (no change) were determined
as shown in (a): Visual field charts before (left panel) and after vision restoration training (middle panel) are used to calculate the
dynamic chart (right panel) which shows the change pre- versus post-VRT (for explanation of charts see Fig. 2). “Hot spots” are
indicated by dark square, cold spots by gray squares. (b) One sample feature which was of special interest: “neighborhood
activity.” A computer-simulation used data mining method of SOMs to examine for each spot of the baseline visual chart ((a),
left panel) a value that represents this feature (in this example of a feature, low values represents little activity and high values
much activity in each spots immediate surround/neighborhood). The SOM then calculated to what extent this feature at baseline
is able to predict a “hot spot.” This feature represents residual visual activity (indicated by levels of gray) in the immediate
neighborhood of a given spot. (c) The results of the SOM-analysis for different such features in 2D SOM charts. For each
feature, a separate chart was created which was subdivided in a hot spot (þ) and cold spot region (0), separated from each other
by a border line. The gray levels represent how well a given feature (e.g., neighborhood activity) is associated with cold or hot
spots. In these SOM charts, white represent tight associations while gray and black represent no association. As the graph shows,
the features “neighborhood activity” and “residual activity of the spot itself” (defect depth) are closely associated with the hot
spot (þ) region. Other features, such as type of visual field defect (quadrantanopia/hemianopia) or distance to the scotoma are
not associated with the occurrence of hot spots. Thus, an SOM analysis revealed that residual activity within a limited surround
has a great influence on vision restoration and, in fact, predicted the restoration potential rather well (see Guenther et al., 2009).
238
we question if they tested sufficiently for possible
residual vision inside the blind field. Namely, an
area that appears completely blind may actually
show residual vision when tested with brighter
stimuli (Kasten et al., 2008).
Specificity and transfer of training effects
When vision restoration is accomplished by training the question arises if training effects are specific or if they transfer to other functions as well.
In the normal brain, improvements in perceptual
learning tasks are rather specific to the features
that were trained (spatial frequency, orientation),
to the retinal position, or to the eye which was
trained (Gilbert et al., 2001; Karni and Sagi,
1991). However, there are also examples of transfer (Beard et al., 1995), though only “easy-tolearn” tasks seem to be transferable (Gilbert
et al., 2001). The information on transfer in clinical cases is still rather ambiguous. In patients with
hemianopia, for example, vision training of the
border region, that is, in ARVs, a task involving
the detection of small dots presented at different
brightness levels, also improved color detection
(Kasten et al., 2001) and VA (Kasten et al.,
1998b; Mueller et al., 2007), both of which were
not trained. Also in regained regions of the visual
field, there is not only improved detection of simple light stimuli (which were used for training)
but also improvement of VA, critical flicker
fusion frequency, and color vision (which was
not trained) (Bergsma and Van der Wildt, 2008).
Thus, there is no clear conclusion as to the
transferability of training effects. This may
depend on the task and the size and localization
of the lesion or other nonspecific factors (such
as attention and temporal processing). We need
to keep in mind, though, that in contrast to normal subjects, patients with brain damage may
have problems with more general cognitive
functions such as attention, temporal processing,
contour integration, brain synchronization, etc.
It is therefore reasonable to assume that a specific
training task may lead to gains in these general
factors which, in turn, would benefit other visual
tasks as well (such as color recognition). In fact,
it is not conceivable that any particular, specific
training task is “specific” in the true sense since
carrying out a visual task (even if simple) always
engages other functions (e.g., visual attention) as
well. It is practically impossible to train single
features alone (such as a contour without a shape,
a shape without attention, etc.). As a consequence,
stimulation of more general functions (such as
attention or brain electrophysiological synchronization) might be beneficial to a specific task or to
a variety of tasks. In this context, the observation
is of interest that visual improvements occur also
in regions well outside of the trained region itself
(unpublished observations). Thus, while the “specificity” issue is difficult to answer at this point, it
seems clear that some generalization always occurs
because any specific training task has also more
generalized, global effects on visual cognition.
Neurobiological mechanisms of vision restoration
The “minimal residual structure” hypothesis
The minimal residual structure hypothesis (Sabel,
1997) states the following: as long as a small, minimum number of cells survive within the damaged
structure, recovery of function is possible. This
hypothesis needs to be expanded to include the
downstream neuronal networks: it is the total
number of fibers of the different pathways surviving the lesion that determines how much information reaches higher cortical regions. For example,
after lesions in the retina or optic nerve, no alternative routes exist whereby visual information
can travel to the brain. In such peripheral lesions,
the functional loss (and the restoration potential)
is at least initially a function of the number of surviving cells. After post-chiasmatic lesions, even a
complete lesion, for example, of V1, does not lead
to a complete functional impairment: information
can still travel alternative extrastriate routes to
239
reach higher cortical regions (e.g., V2–V5) via the
tectal–pulvinar pathways or direct geniculate–V5
fibers. The term minimal residual structure should
therefore include both the local structure and all
other alternative or upstream structures that contribute to visual information processing and
restoration.
Although a certain minimal number of residual
cells is critical for vision restoration to occur at all,
because of the “network” plasticity the precise
number of surviving cells in the lesion site is a
rather poor predictor of restoration (Sautter and
Sabel, 1993). As Fig. 1 shows, with only about
20% of the RGCs, rats reach up to 80% performance in visual tasks. For a given visual performance, the questions are as follows: (i) How
much primary tissue (number of cells and their
connections) is left? (ii) Are there other
(rerouting) pathways for visual information to
reach higher-up brain regions? The smaller the
residual capacities of both, the lower the chances
for vision restoration.
The Pasik and Pasik study (1973) may serve to
illustrate this point: 14 macaque monkeys were
trained in a two-choice task (light vs. no light) following bilateral occipital cortex removal. Immediately after the injury, the animals were blind,
bumping into objects and falling from platforms,
though pupillary reactions and eye movements
appeared intact. After 3 months of recovery time,
the monkeys were again able to carry out brightness discrimination tasks and reach for visual
stimuli. Concomitant removal of ventrolateral
portions of the temporal lobe, posterior portions
of the parahippocampal gyrus, the pulvinar, the
superior colliculus, or the medial tectum resulted
in only a temporary visual defect from which the
animals recovered. But when the lateral pretectal
region was injured as well, which caused a bilateral, severe degeneration of the nucleus of the
accessory optic tract, the monkeys were no longer
able to relearn the brightness discrimination task.
Thus, not only does the visual system have capacities to recover from damage, but more generally speaking, just as Lashley (1939) stated, the
extent of visual dysfunction depends on how
much of the visual system as a whole is injured
(which Lashley termed the “principle of mass
action”). While this somewhat holistic interpretation of visual system function seems perhaps a bit
simplistic, yet from today's point of view it points
to the great potential that just a small amount of
residual tissue may have.
Apparently, only a surprisingly small number of
neurons is required for functional restoration to
take place and this is also known in other brain systems. In studies of patients with Parkinson's disease, for example, a loss of dopamine cells greater
than 70% has to occur before patients start even
noticing symptoms, that is, the brain is able to compensate up to 70% loss rather well. Also, recovery
of spinal cord lesions is a rule when lesions are not
complete, that is, leaving behind small remnants of
residual tissue, in the order of 5–20%, which is similar to the 80% vision recovery in rats with only
20% RGCs (Fig. 1).
Given this tremendous dynamics of the brain, it
is perhaps not surprising that the number of surviving neurons correlates rather poorly with functional outcome. First, the plasticity of the areas of
primary damage introduces variability to behavioral performance, and second, both downstream
neuronal nuclei and alternative pathways contribute in a significant way to recovery and reorganization (amplification) of vision. This markedly
dilutes the structural–functional correlation and
is a source of variability. The good news is that
it gives more therapeutic wiggle room.
Within-systems plasticity
In order to get a better understanding of the
mechanisms of vision restoration, we need to differentiate between plasticity of residual tissue in
the damaged structure itself—within-systems plasticity - and the responses of all other brain regions
located downstream, the “network plasticity”.
Within-systems plasticity relates to changes in
the remnants of the damaged structure itself
240
(see Sabel, 1997). This kind of plasticity involves
local cellular changes—such as activation (or reactivation) of surviving cells (Prilloff et al., 2007)
or enhancement of their synaptic transmission
(synaptic plasticity). Within-systems plasticity can
best be studied in animal experiments where an
incomplete (partial) lesion can be studied at the
cellular level, such as in optic nerve preparations.
It can also be investigated in areas surrounding
the injured visual cortex (Imbrosci et al., 2010;
Wood et al., 1974) or spared tissue remnants of
injured superior colliculus (Stein and Weinberg,
1978). The key question here is this: “How many
neurons need to survive and what changes do they
undergo so that restoration of vision becomes
possible?”
We studied this issue by creating partial crush
lesions of the ONC in adult rats. Here, only about
10–20% of RGCs are sufficient for vision recovery to occur (Sautter and Sabel, 1993). That this
rather small number of cells sustain function confirms observations by Lashley (1939) who
estimated that as little as one-sixtieth of the neocortex is sufficient for visual discrimination. Chow
(1968) and Galambos et al. (1967) found 2–3% of
the optic tract fibers to the LGN to be sufficient
for “normal vision” (which appears to be an overstatement). Chow and Stewart's figure is about
28% (Chow and Stewart, 1972) much larger than
the figure given by Hubel and Wiesel (1970) who
observed that with as little as 1% of the cells
responsive, limited recovery of form deprivation
is possible. The finding of RGC hyperactivation
after partial optic nerve damage supports the concept of within-systems plasticity: a delayed, moderate calcium hyperactivation of surviving RGCs
was associated with greater responsiveness of
the cells to visual stimuli (Prilloff et al., 2007).
deafferentation.
It
includes
also
other,
nondeafferented regions involved in the postlesion response. For example, partial retinal or
optic nerve damage will lead to primary
deafferentation both in the superior colliculus,
the main retinofugal target in the rat and in the
LGN of the thalamus, the main retinofugal target
in humans. Visual cortex would then be the
region of secondary deafferentation.
If network plasticity exists, one would predict
that damaging all nuclei of the network should
reduce the chance of recovery toward zero if no
other pathways can drive the function. Indeed,
combined, simultaneous lesions of all alternative
pathways result in more severe deficits and recovery is precluded. For example, there is less recovery in cats with combined visual cortex and
suprasylvian gyrus lesions (Wood et al., 1974),
when creating combined lesions of different visual
areas simultaneously, there may still be visual
sparing (in luminous flux). But when the
suprachiasmatic nucleus was also damaged, the last
visual structure still available, there was no restoration whatsoever (Pasik and Pasik, 1973).
Thus, a loss of all visual structures clearly precludes restoration (which is not at all surprising).
Fortunately, in the clinical world, complete visual
system lesions are extremely rare (other than
complete eye or optic nerve damage). As a consequence, even in patients considered to be “legally
blind,” there is almost always some degree of
residual vision and therefore some restoration
potential. In lesions acquired later in life, complete (and not only apparent) blindness is an
extremely rare exception and at least some residual vision is usually present.
Receptive field plasticity in deafferented
brain structures
Network plasticity
Network plasticity refers to all changes in areas
not directly affected by the injury but suffering
from “primary” and/or “secondary” (functional)
Cell loss in one structure of the brain destroys
their projection fibers to remote areas. If these
fibers are excitatory, deafferentation depression
in remote regions takes place, a phenomenon
241
also known as “diaschisis” (Von Monakow,
1914). If the projection fibers are inhibitory,
deafferentation excitation results. In cases of
deafferentation depression, spontaneous recovery
may be achieved by reactivation of metabolic
activity which happens spontaneously during the
early recovery period, for example, after optic
nerve damage in the LGN and visual cortex
(Schmitt and Sabel, 1996a,b, 1998). This reactivation may be mediated either by surviving neurons
increasing their strength to above-normal levels
(Prilloff et al., 2007) or by excitability changes in
the deafferentation zone itself (Giannikopoulos
and Eysel, 2006). In the computation model of
cortical plasticity, increased neuronal gain in the
deafferented zone has been shown to be crucial
for consistent experimental RF shifts (Young
et al., 2007). Whatever the mechanism, the region
of primary deafferentation is key in the restoration equation and reorganization of its neuronal
network.
The classic example of network plasticity in the
visual system is RF reorganization, a field
pioneered by Eysel (e.g., Eysel and Grüsser,
1978). He and others showed RF reorganization
after retinal lesions with RF shifts up to 5–9 of
visual angle and up to 10-fold initial increase of
RF size followed by shrinkage to nearly normal
levels (Darian-Smith and Gilbert, 1995;
Giannikopoulos and Eysel, 2006; Gilbert and
Wiesel, 1992; Heinen and Skavenski, 1991; Kaas
et al., 1990; Waleszczyk et al., 2003). Also, direct
injury to visual cortex produces a local RF reorganization seen as both increases in RF size but also
in shift of RF location (Eysel, 1997). RF reorganization also occurs in the surround of the lesion,
the “penumbra,” leading to physiological hypoexcitability and more distally, hyperexcitability
(Dohle et al., 2009; Eysel, 1997). This RF reorganization is mediated by long-range intracortical
horizontal connections which are either activated
after deafferentation (Darian-Smith and Gilbert,
1995) and which show axonal sprouting
(Darian-Smith and Gilbert, 1994).
Lateral influences of cortical interneurons have
also been proposed to underlie the “filling-in phenomenon,” that is, the curious observation that a
retinal scotoma is subjectively perceived to be
much smaller than expected (Murakami et al.,
1997). In fact, the compensation potential of even
the normal brain is so great that by filling-in the
blind spot escapes conscious detection. In addition, the size of the RF varies considerably in
the normal brain, depending on the brain's synchronization state (Wörgötter et al., 1998). Thus,
RFs plasticity is dependent on lateral influences
from neighboring regions, which can exert either
inhibitory or excitatory influences.
It is likely that RF plasticity is the mechanism
of both normal learning and adaptation of the
visual system to damage. Because lateral
interactions in visual cortex are involved in perceptual learning (Gilbert, 1998; Gilbert et al.,
2001) and RF plasticity (Gilbert and Wiesel,
1992), the possibility exists that lateral influences
are also involved in vision restoration following
behavioral training or electrical stimulation.
Recent findings by Raemaekers et al. (2011) are
consistent with this possibility (further discussed
below).
If the assumption is true that lateral interaction
contributes to vision restoration, one would
expect that vision restoration does not exceed
the boundaries imposed by the lateral extent of
these interactions. We have studied this question
in visual field charts of hemianopic patients after
a repetitive perceptual learning task (training).
We reasoned that if RF plasticity is involved in
vision restoration similar to that found in cats
(Giannikopoulos and Eysel, 2006) or monkeys
(Gilbert and Wiesel, 1992), improvements should
not be distributed randomly in the visual field.
Rather, they should be a function of the distance
of the immediate surround and span a finite distance, that is, their influence is spatially limited.
Visually driven spike activity recovers within
a deafferented region up to 3.5 mm from the
scotoma border (Das and Gilbert, 1995;
242
Giannikopoulos and Eysel, 2006). Others report
even larger range of RF shifts (5–6 mm;
Waleszczyk et al., 2003). Computer simulations
and physiological recordings from macaque area
MT suggest that dynamic alterations in neural
activation alone are sufficient to allow large RF
changes (Sober et al., 1997) and perilesion cortical activity is a critical factor in reorganization
(Eysel et al., 1999). Thus, restoration of vision in
patients (i) may be mediated by areas which are
not completely but only partially damaged, (ii) it
may be influenced by perilesion activity of the
cortical surround, and (iii) if reorganization of
RFs is the underlying neuronal substrate, visual
field expansions should be spatially limited.
Based on these considerations, we have hypothesized that vision restoration is governed by the
same rules and principles imposed by the spatial
limits of lateral interaction. To test this, we have
measured in visual field charts of hemianopic
patients the precise topography of changes after
stimulation by first creating dynamic visual field
charts and then calculating the differences before
versus after training. The obtained “dynamic
charts” then permitted the identification of areas
of the visual field where vision restoration occurred
(hot spots) and those where it did not (cold spots).
Using “SOM”-data mining tools, we then related
the location of the restoration hot and cold spots
to certain features in the baseline visual field
topography (Guenther et al., 2009). The goal of
this approach was to uncover possible rules of RF
plasticity and to check the influence of lateral
interactions (Fig. 11).
Indeed, when the location of the restoration
hot spots was compared to the precise topography
of baseline charts, we found that vision restoration follows indeed rules of RF plasticity: restoration hot spots were primarily located in areas of
the visual field that had either a high level of local
residual activity and greater amounts of residual
activity in the immediate spatial 5 surround.
The level of global activity (lesion size) or other
parameters (such a type of visual field defect)
were of no influence (Guenther et al., 2009).
This observation is confirmed by recent imaging studies by Raemaekers et al. (2011). They
found direct evidence for RF plasticity: in
hemianopic patients participating in VRT RF
changes in visual cortex could be imaged. The
findings are thus compatible with our own
observations of a special role of lateral
interactions in vision restoration. The authors
concluded that small visual field enlargements
(such as those at the border region of the visual
field) could be explained by this more “local”
RF plasticity, but that massive visual field
expansions, which are sometimes observed in
patients, cannot be explained by this mechanism.
Yet, one important question remains: Is RF
plasticity good or a bad? Whether RF reorganization (RF location shift or enlargement) is functionally adaptive or maladaptive is not yet clear.
Enlarged RFs might facilitate detection, but at
the same time, they might reduce the ability to
see in ambient light or detect objects at higher
resolution or more complex objects. Likewise, a
shift of the RFs location might be helpful to
engage deafferented regions of the brain to participate in visual processing, but if this is helpful
at all or instead leads to scrambling or noise in
regions adjacent to the lesion remains to be
determined.
There is one phenomenon that nicely illustrates
the ambiguous role of RF plasticity. Dilks et al.
(2007) described a patient with a left upper
quadrantanopia carrying out detection tasks of
different shapes (squares, circles, triangles).
When presented near the lesion in the lower left
quadrant, the subject perceived objects as vertically elongated, extending toward and into
the damaged area. The lesion affected “visionfor-perception” tasks as well as visually guided
motor
response
(vision-for-action).
fMRI
measurements confirmed the hypothesis that the
deprived cortex became responsive to nearby
(intact) regions in a retinocentric manner, an
issue also related to the filling-in effect. One
may argue that visual distortions might be maladaptive from the point of view of “what is it?,”
243
but they might be adaptive from the point of view
“is there something?” Currently, we cannot tell if
cortical reorganization after lesions is a good or
bad thing (enhancing perception or distorting is).
Clearly, this issue is a critical one in need of further study.
The role of downstream networks
After considering plasticity of the damaged structure itself and the primary deafferented structure,
let us now turn to neuronal networks beyond the
deafferented region.
It is reasonable to assume that reorganization
does not stop at the area of primary
deafferentation. It also leads to changes in secondary brain structures. In addition, as we know
from post-chiasmatic damage, information flow
can bypass the lesion site, using a detour of alternative routes to higher cortical regions (see the
discussion on blindsight above), leading to a kind
of remote neuronal network response.
The excitation–inhibition balance
From the network point of view, reestablishing
homeostasis, an evolutionary principle, is the
key goal, that is, the proper balancing of excitation and inhibition. This issue receives sparse
attention in the vision restoration literature. Let
us consider, for example, the case of an incomplete hemianopia caused by an incomplete V1
lesion. Here, we have a loss of lateral interactions
(presumably horizontal cells) which impairs local
information. Second, there is the loss of longrange interhemispheric fibers that terminate in
the mirror-symmetric position of the opposite,
intact hemisphere, particularly in the region that
corresponds to the vertical midline. Because
interhemispheric fibers are believed to be inhibitory (Sprague, 1966), their loss would result in a
hyperexcitation of the intact hemisphere with a
secondary inhibitory ripple effect by the
reciprocal interhemispheric, inhibitory fibers
originating from the intact side and terminating
on the damaged side. The final outcome would
be a disaster for the damaged hemisphere: additional inhibition of all those regions that were
partially damaged (ARVs). ARVs are probably
the greatest victims of excitation–inhibition
dysbalance.
In the RF microenvironment, the consequences
are several-fold. We expect local inhibitory and
over-excitatory changes in the immediate surround of the lesion plus a functionally hyperactive
state of the intact hemisphere with a secondary
overinhibition of the reciprocal interhemispheric
inhibitory back-projections. In this scenario, any
cells that managed to survive inside the blind or
partially blind field would be inhibited from the
opposite (intact) hemisphere (ARV suppression)
and this happens irrespective of whether they
are located in the ARVs near the visual border
or in any islands of residual vision. The net outcome of all of this would be as follows: the intact
hemisphere ends up with subtle deficits in vision,
possibly by being hyperexcitable, and additionally, residual tissue on the lesion side is
suppressed by overinhibition. If this theory of
imbalance is correct, restoration would be a
rebalancing act: (i) inhibiting the overexcited
intact tissue and/or (ii) increasing excitability of
the residual tissue in the region of the lesion.
There are several lines of evidence that
network balance is critical for proper vision.
There are (i) subtle deficits in the intact hemisphere and (ii) visual hallucinations found during spontaneous and training-induced visual
recovery which have been interpreted as signs
of interhemispheric dysbalance. In addition,
rebalancing can restore vision: (iii) restoration
of vision can be achieved by additional lesions
in the hemisphere contralateral to the lesion
(also called the “Sprague effect”), (iv) cortical
reorganization as demonstrated by imaging studies, and (v) the restoration effects of functional
silencing of the intact side while stimulating
ARVs (as in VRT).
244
Subtle deficits in the intact hemisphere The presumably intact visual field in hemianopes is actually impaired. It has difficulties to detect
incomplete figures embedded in a noisy background (Paramei and Sabel, 2008; Schadow
et al., 2009). For example, three hemianopia
patients had to detect with their intact side of
the visual field a figure (square) composed of
interrupted contours created by Gabor patches
embedded in a random patch array (Paramei
and Sabel, 2008). Two of the patients had marked
deficits in the response accuracy and reaction
times and also showed “figure confabulations.”
This can be explained by impaired top-down
influences from higher visual centers and/or loss
of proper interhemispheric balance, both of which
impair the function of the intact hemisphere. This
interpretation was confirmed by Gamma
response analyses of EEG recordings (Schadow
et al., 2009). Interestingly, Corbetta et al. (2005)
found in patients with attentional dysfunctions
after parietal lesions fMRI evidence of hyperactivation in contralateral, intact cortical regions.
As hyperactivation declined, attentional dysfunction recovered. Thus, reduced transcallosal inhibitory interaction (directly or indirectly) may
reinstate interhemispheric balance after lesions
and interhemispheric modulation may improve
perceptual functioning and recovery.
Hyperactivations as a result of the loss of inhibitory fibers in deafferented brain regions may also
explain the figure confabulation in parietal
patients (a kind of “reverse diaschisis”) (Paramei
and Sabel, 2008): In a way, the patient's “expectation” of a visual stimulus (such as a square) “outcompeted” the evidence from the sensory input,
or, in Corbetta's terms, there was a top-down bias
along with a decreased stimulus-driven capture.
Visual hallucinations during recovery of
vision The notion of top-down hyperactivation
is also in agreement with observations in
hemianopic patients that report simple phosphene perceptions (hallucinations) during the
time of spontaneous recovery and during
training-induced visual field expansions. Kölmel
(1985, 1993) was the first to propose that visual
hallucinations in partially blind patients are a positive sign of neural plasticity and recovery of function. But direct proof of a link between
hallucinations and recovery of visual functions
was first shown by Poggel et al. (2007). They
observed hallucinations in hemianopic patients
during the days and weeks of early spontaneous
recovery and also again when visual field recovery was induced by training. Here, simple and
complex hallucinations were associated in time
and space with increased visual field size and
recovery: (i) hallucinations were more frequently
in patients who benefited from training, (ii) they
were typically located in ARVs, and (iii)
hallucinations coincided in time with the period
of greatest visual field expansion. It should be
mentioned in passing that patients are usually
aware that these “hallucinations” are not real.
But the patients usually do not talk about it
because they are worried that it is seen by others
as signs of a psychiatric disorder (which it is not).
Restoration
of
vision
by
additional
lesions Because the intact hemisphere has an
inhibitory effect on contralateral, cortical, and
subcortical areas (such as the superior colliculus),
cross-hemispheric inhibition may contribute to
dysfunction. Consequently, lifting this inhibition
may restore functions. This was first demonstrated by Sprague (1966) in cats where additional
lesions in the intact hemisphere restored some
of the lost functions induced by a tectal lesion
(sometimes referred to as the Sprague effect).
Here, a unilateral lesion of the superior colliculus
led to orienting deficits which were counteracted
by contralateral visual cortex damage.
Perhaps the most impressive proof of interhemispheric interactions after brain lesions in
humans was published by Pöppel and Richards
(1974). They described a hemianopic patient
who also had a small lesion in the contralateral
intact field. Here, an “island of vision” of vision
was seen in the otherwise absolute blind
245
hemifield. Because this island of vision was mirror
symmetric to the island of blindness, this effect
was interpreted as an example of a lifting
deafferentation effect.
Cortical reorganization as demonstrated by
imaging studies fMRI studies have revealed the
first evidence of visual system reorganization in
humans. Whereas in healthy subjects, brain activation is found particularly in contralateral V1
(area 17), in patients with post-geniculate lesions,
in contrast, activation changes are found bilaterally in the extrastriate areas with a stronger activation on the intact (contralesional) hemisphere
(areas 18 and 19) (Nelles et al., 2002, 2007).
Brodtmann et al. (2009) showed that bilateral striate and ventral extrastriate activation was
reduced in stroke patients, while activation
increased in dorsal sites, indicating a greater utilization of the dorsal visual system. These findings
are in agreement with the interhemispheric imbalance hypothesis.
Cortical reorganization was also reported in
patients suffering from macular degeneration
who develop a preferred retinal locus (PRL)
(see discussion above). Imaging studies showed
that the PRL has a larger cortical representation
than other retinal regions of the same eccentricity
(Liu et al., 2010) and isoeccentric peripheral
locations are represented in the formerly foveal
cortex (Dilks et al., 2009). Thus, there are both
“active” mechanisms of reorganization which are
use dependent and passive ones which are use
independent.
When stimulating the brain by behavioral training, activation changes in fMRI are observed.
After eye-movement training, for example, there
are changes in the unaffected extrastriate cortex
(Nelles et al., 2010). Also when patients carry
out VRT, activations are found in the anterior
cingulate and dorsolateral frontal cortex together
with other higher order visual areas in the
occipitotemporal and middle temporal regions
(Marshall et al., 2008). Along similar lines,
Henriksson et al. (2007) trained hemianopic
patients using flicker stimulation which caused
an ipsilateral representation of the trained visual
hemifield in different cortical areas, including
the primary visual cortex. Similar findings were
reported by Raninen et al. (2007).
Retinotopic mapping was used by Ho et al.
(2009) to show residual visual function in a
patient with complete homonymous hemianopia.
Retinotopic representation was found in the surviving visual cortex around the infarcted area
and stimulating the blind field led to a response
in extrastriate areas above the calcarine sulcus.
So far, fMRI imaging results are compatible
with the concept of large scale visual cortex reorganization (network plasticity). Unfortunately, in
many studies, the patient numbers have been
too small (often single cases) to reach final conclusions on the generality and reliability of cortical reorganization by brain imaging (however,
see Raemaekers et al., 2011). More information
is now required using larger patient samples in
combination with sophisticated behavioral
paradigms.
Functional silencing of the intact hemisphere The
easiest way to achieve functional balance is to
silence the undamaged hemisphere by simply
exposing the subject to darkness. In this manner,
both hemispheres are functionally inactivated,
processing less visual information. In this situation, a functional “advantage” can be created for
ARVs by stimulating them selectively while the
intact field remains in the dark. This is what the
classic VRT does: here patients train in the dark
which has two simultaneous effects: functional
silencing of intact regions while being able to activate ARVs (see discussion of the restoration
potential of VRT above). In a way, VRT creates
a double-punch situation: on one hand, a reduced
activation by darkness of the intact hemisphere
with the consequence of reduced interhemispheric inhibition in the lesioned hemisphere,
and on the other hand, the simultaneous activation by visual stimulation of the previously
inhibited ARVs inside or near the lesion.
246
The net result is a functional rebalancing which,
if practiced regularly, is stabilized.
Thus, the imbalance between excitatory and
inhibitory neural influences aggravates the visual
loss after brain lesions. As the brain can be
induced to reach a more homeostatic, balanced
state (by training or brain current stimulation),
partial vision restoration is achieved. If homeostatic balance is the key in vision restoration, then
it should not matter if inhibition is reinstated in
regions that are overexcited or excitation is
reinstated in areas suffering deafferentation
depression (diaschisis). Preferably both can be
used to reestablish the balance at different levels
of the nervous system: at local, lateral interactions
or at long-range intra- and interhemispheric
(transcallosal and subcortical) projections.
Both “within-systems plasticity” and “network
reorganization” are part of the post-lesion
response in the brain. They act in concert to optimize residual vision and restoration, but there
may also be some maladaptive elements to reorganization which need to be explored. Because
lesions vary greatly from minor loss to complete
blindness, the extent of restoration is variable as
well. But it is the sum of local (within-systems
plasticity) and global (network plasticity)
influences that will determine the final extent of
recovery of perception.
Cellular mechanisms of vision restoration
and plasticity
How can such within systems and network plasticity be explained on a cellular level? We believe
that the cellular mechanism of vision restoration
involves the strengthening of surviving neurons
in the damaged system itself and/or reorganization of higher-up (intact) neuronal networks. This
issue was already discussed above. Building on
the assumption that a stable within-systems and
network plasticity change requires stable changes
at the synaptic level, the questions arise how synaptic plasticity can be achieved by surviving cells.
We would like to propose that vision restoration rests upon cellular and molecular
mechanisms of normal learning. We believe that
just as in the normal brain, repetitively activating
surviving (residual) cells lead to synaptic plasticity, and this is relevant for both for surviving cells
of the damaged structure itself and for cells in
upstream networks (network plasticity).
Interestingly, it seems less critical as to which
precise method of stimulation is used to achieve
reactivation of residual structures: (1) regular
training where patients (or the animals) have to
respond to many thousands of visual stimuli or
(2) by noninvasive brain current stimulation
protocols which are fairly nonspecific (see above).
At a cellular level, learning was studied in
rodents where the concept of LTP was established
(Bliss and Lomo, 1973). LTP is defined as a longlasting enhancement in the cell response to highfrequency stimulation (Fig. 12). LTP maintenance
is mediated by both an increased transmitter
release per presynaptic impulse and an increased
postsynaptic responsiveness to a fixed amount of
transmitter (Voronin et al., 1995). LTP has been
induced also in the human visual system by noninvasive “photic tetanus” (Sale et al., 2010). Interestingly, LTP as a model of learning and memory has
been used already to investigate post-lesional plasticity and, of most relevance here, in residual
structures in the vicinity of a lesion (Dohle et al.,
2009; Huemmeke et al., 2004). Electrophysiological recordings of ex vivo/in vitro preparations of
the post-lesional visual cortex revealed that LTP
is enhanced while LTD (long-term depression) is
impaired (Imbrosci et al., 2010). This
“metaplasticity” may provide the physiological/
molecular basis of the rewiring of synaptic
connections and restoration of visual function.
Postsynaptic NMDA receptors play a special role
in LTP in the lesion surround, and this is compatible with the hypothesis that LTP in horizontal
connections in visual cortex might comprise the
cellular mechanism of vision restoration (Imbrosci
et al., 2010). However, although these results suggest that post-lesion neuronal plasticity is possible,
247
Synaptic plasticity after partial brain injury
Residual neurons
Presynaptic
Postsynaptic
Resting state
Stimulated
Fig. 12. Synaptic plasticity after partial brain injury. The hypothesis of within-systems plasticity proposes that synaptic plasticity
contributes to restoration of vision. It assumes that in a partially injured area of the brain, the physiological activity (sum of all
action potentials) produced by surviving neurons is below normal values, insufficient to drive the postsynaptic neuron (partially
deafferented structure) at full throttle (upper panel). By stimulating the presynaptic neurons of the partially damaged region by
training or electrical stimulation, the silenced activation state (middle panel) changes to greater activation. Repeated activation
then elevates cell activity (number of action potentials) above-normal levels, strengthening synaptic efficacy (lower panel). This,
in turn, leads to induction of long-term synaptic plasticity which outlasts the stimulation period. On a molecular level of analysis,
the process of synaptic plasticity is achieved by the release of trophic factors from postsynaptic cells (adapted from Kolarow
et al., 2007)
one has to keep in mind that there is still a significant loss of function in neuronal populations in the
vicinity of cell death/damage (Aoyagi et al., 1998;
Henrich-Noack et al., 2005). Also, as following
traumatic optic nerve damage, there are molecular
changes in the surviving cells such as alterations in
the splicing variance of different NMDA receptors
(Kreutz et al., 1998). It is not clear if these
alterations are adaptive or maladaptive.
In any event, considering the evidence for
both, metaplasticity and silencing of neurons,
post-lesion plasticity may be induced by overcoming injury-related blockades. Interestingly, at
a cellular level, this is possible by mechanisms of
learning: high-frequency stimulation which under
physiological conditions induces LTP is also able
to restore lost functions in silent neuronal
populations after brain injury (Henrich-Noack
et al., 2005). However, restoration of function by
learning is not possible at very early post-lesion
times. It can therefore be hypothesized that
neurons need some time after an impact to
248
recover and adapt their molecular/morphological
integrity. After this delay, changes in the
micromilieu or inhibitory feedback loops prevent
the post-lesion neuronal plasticity as ex vivo
investigation of silenced neuronal population
shows normal function and plasticity. This restoration of plasticity and function also depends on
changes in postsynaptic sensitivity (HenrichNoack et al., 2005), and this is compatible with
the hypothesis that hallucinations reported by
visually impaired patients are a sign of post-lesion
denervation supersensitivity.
LTP or LTD, supported by the release of trophic factors, may explain the strengthening of
synaptic transmission (plasticity), possibly also
involving axon terminal sprouting, but single cells
alone do not explain the reaction of the entire
residual network. Rather, network reactions as a
whole generate function and alter RF plasticity.
LTP/LTD may thus provide the cellular condition
for an overall change at the network level.
regions fire in a synchronized manner to drive
normal vision (jointly firing action potentials and
oscillating network in perfect temporal coordination), areas of partial damage are initially nonsynchronized, with poor firing synchrony. After
external stimulation (induced by training or during electric current stimulation), the partially
damaged regions are forced to fire jointly in temporal coordination. We hypothesize that such a
repeated stimulation induces a “forced synchronized firing” which then leads to synaptic plasticity
of the partially damaged structures and downstream areas. By doing this repeatedly, LTP-like
mechanisms lead to stabilized synchronous firing
in the network which lasts beyond the treatment
period (aftereffects). This improved or stabilized
synchronization is a key mechanism of the proposed neurophysiological mechanism of vision
restoration.
Vision restoration and attention
Vision restoration and neuronal synchronization
When a visual stimulus hits the retina, retinal cells
fire together in a timely synchronized fashion and
information travels to higher brain centers. Under
normal conditions, this synchronization works
perfectly, evoking many secondary ripple effects
in the brain (such as oscillations) which jointly
create the percept. However, when cells are lost
and primary and secondary disorganization of
neuronal networks happens, one would expect a
loss of synchrony, that is, a worst coordination
of timed events. A slowing of mental processing
would be expected which is what we see in
patients who show reduced reaction times and
feel uncomfortable observing the fast moving
world (navigating in a busy crowd or driving a
car). Thus, to restore vision requires a better neuronal synchronization.
Figure 2 shows the concept of “stimulationinduced synchronization” after partial nervous
system damage. While neurons in intact brain
It is well known that neural activation enhances
visuospatial attention. Behavioral, neurophysiological, and imaging experiments show that focusing
attention to a specific part of the visual field
benefits visual processing in that area, for example,
reaction times are reduced, and stimuli are detected
or discriminated more easily then when attention is
distributed more diffusely across the visual field or
focused elsewhere (Eriksen and Rohrbaugh, 1970;
Nakayama and Mackeben, 1989; Posner, 1980;
Treisman and Gelade, 1980). This can be explained
by increased neuronal activation (synchronization)
in circumscribed regions of the visual cortex as
shown in single-cell recordings in animals (Gilbert,
1998; Ito and Gilbert, 1999), electrophysiological
experiments in humans (Mangun and Hillyard,
1987) and in brain-imaging studies (Martinez
et al., 1999; Somers et al., 1999). In the attention
spotlight, the signal-to-noise ratio increases,
resulting in improved performance of the normal
brain. The benefit of attention is particularly obvious under difficult perceptual conditions with low
249
signal-to-noise ratio. Attentional load modulates
not only responses of invisible stimuli in human primary visual cortex, but it also improves normal
vision at low contrast viewing conditions (Bahrami
et al., 2007).
Such an attentional advantage is also found in
patients with visual field defects. Here, visuospatial cues can acutely improve detection performance: when patients are asked to focus their
attentional spotlight at the visual field border, this
immediately enhances perceptual performance
within a few hundred milliseconds after stimulus
presentation precisely in the region of the cue
(Poggel et al., 2006; see Fig. 10). This
demonstrates that residual vision can be immediately accessed by activating attentional resources.
In this context, a rather curious observation by
Schendel and Robertson (2004) might be of interest. They reported that visual detection can be
(acutely) increased in hemianopic patients by
placing their arm near the visual stimuli when
this was located in the blind hemifield. This
arm placement might have simply increased the
attention to the stimulus location, elevating its
excitability. Another example is a patient with
near-blindness that one of us (B. A. S.) studied
in Wisconsin/USA in 2000. When asked to
describe what he sees he said: “just darkness,
nothing else.” When being confronted with a sudden noise (B. A. S. clapped his hands unexpectedly near the patients ears, a rather startling
sound), the patient suddenly said he could see a
person (the attending physician) standing in front
of him, stating with joy: “I can see the doc, he is
wearing a red tie” (which he actually did). This
is a dramatic example of how temporary vision
improvement that can be achieved by raising the
patients level of alterness or attention.
Directing the attentional spotlight repetitively
onto the ARVs in a repetitive practice tasks
(training) leads to permanent improvement of
vision in patients suffering from visual field
defects. Poggel et al. (2004) combined standard
VRT in hemianopics with an attentional cueing
task focusing attention to ARVs and found this
to enhance the restoration level of VRT (Fig. 10).
Also, the Jung et al. (2008) study, where training
of the intact region of the visual field in patients
with anterior ischemic optic neuropathy improved
function, was taken to conclude that this “may
reflect diffusely increased visual attention (neuronal activation), or improvement of an underlying
subclinical abnormality in the seeing visual field”
(p. 145). The Poggel study supports the hypothesis
that both ARVs normally receive insufficient
attentional resources; the intact visual field sector
simply captures all of the attention in everyday life,
at the expense of the partially damaged areas by
inhibiting them (excitatory/inhibitor dysbalance).
In summary, attention plays a key role in the plasticity of partially damaged areas of V1 at a network level, even in the presumably intact visual
field sector.
The residual vision activation theory
Only partially damaged brain systems have a
potential for restoration of vision. Clearly, if there
is no structure (e.g., complete eye damage), there
is no chance for recovery. Rather, recovery or restoration of function requires some minimal amount
of tissue that is (or becomes) dedicated to this task.
One of us has earlier postulated the “hypothesis of
minimal residual structures” (Sabel, 1997). It is
remarkable how much a relatively small number
of cells can accomplish. Rats with mild optic nerve
injury with only 10–20% of the RGCs survival
(Sautter and Sabel, 1993) recovered their ability
to perform visual tasks again in about 2–3 weeks.
The recovery was not complete and also not all
animals recovered to the same extent. Yet, visual
performance improved from complete visual dysfunction to about 70–80% performance. This
rather remarkable and unexpected observation
suggests that a small percentage of neurons and
their intact axons are sufficient to allow considerable recovery.
250
The behavioral tasks we used to test our rats
employed rather simple brightness discrimination
or pattern discrimination tasks. We do not know
if perhaps some more complex functions such as
ambient stimulus perception, fast or complex
tasks might remain deficient. Nevertheless, these
findings clearly attest to a considerable postlesion plasticity potential after partial visual system damage which opens new possibilities to take
advantage of this restoration potential by developing new therapeuties.
Our own research and that of others have confirmed the restoration potential of residual vision.
There are many publications on the subject of
vision restoration and plasticity by now from different fields of study (Table 1). With this review,
we have attempted to arrange these many puzzle
pieces to a coherent picture. Based on several
decades of research by us and others since the
1970s, we now propose the residual vision activation theory as follows:
Damage to visual structures is usually not complete but some structures survive the damage.
Together with structures of the intact hemisphere, they provide residual capacities to support vision restoration.
Residual structures include (i) partially damaged
tissue that sustains “areas of residual vision”
(ARV) at the visual field border, (ii) “islands of
residual vision” inside the blind field, (iii) alternate visual pathways unaffected by the damage
(sustaining “blindsight”), and (iv) down-stream,
higher-level neuronal networks. Because patients
with retina or brain damage tend to focus their
attention on the “intact” visual field sectors in
everyday life, a result of a hyperactivation of the
intact hemisphere, residual structures lack sufficient attentional resources, reducing their activation state and impairing physiological activation
and synchronization. Residual structures thus suffer a triple handicap: (i) they have fewer neurons,
(ii) they are disturbed in their excitation/inhibition balance and temporal processing, and (iii)
they lack sufficient attentional activation. ARVs
are therefore down-regulated, unable to contribute much to every-day vision. “Non-use” then
impairs their synaptic strength even further.
Residual structures can be (re-)activated/restored
by engaging them in repetitive activation and
stimulation. This repetitive activation of residual
vision can be achieved by different means such
as (i) visual experience, (ii) visual training, or
(iii) noninvasive electrical current brain stimulation. This may lead to reorganization by the
strengthening of synaptic transmission of the partially damaged structures themselves (“withinsystems plasticity”) and of downstream neuronal
networks (“network plasticity”) in cortical or subcortical areas of the damaged and the intact
hemisphere. This leads to improved synchronization of neuronal firing in the brain network.
Cellular mechanisms of vision restoration are similar to, if not identical with those involved in normal
perceptual learning (such as long-term potentiation) which is why long-lasting reorganization and
re-synchronization of synaptic plasticity can sustain
long term activation of residual structures. Vision
restoration should therefore not be regarded as a
pathology-specific phenomenon but an expression
of normal learning which explains that it can be
induced at any time after the lesion, at all ages and
in most, if not all, visual field impairments (scotoma,
tunnel vision, hemianopia, acuity loss), irrespective
of their etiology (e.g. stroke, neurotrauma,
glaucoma, amblyopia, AMD).
However, vision restoration is rarely complete
and does not take place in all patients. If and to
what extent restoration can be achieved is a function of the precise nature and extent of residual
structures and their activation state. In addition,
the extent of restoration depends on the proper
activation
methodology
and
appropriate
parameters, and it requires the allocation of sufficient attentional resources directed toward the
residual structures.
Thus, the more ARV are available, the greater is the
restoration potential. Whereas the acute activation
of residual vision leads to only temporary functional
251
improvements, permanent improvements require
repetitive stimulation for many days, weeks or
months (depending on the stimulation method).
By becoming again engaged in every day vision,
(re-) activation and synchronization of ARV
outlasts the stimulation period, leading to long-term
improvements in vision and quality of life.
Considering the large body of evidence, we
now have many reasons to be more optimistic
about the fate of partial blindness. The visual system has an excellent potential for plasticity and
self-repair, much more than previously thought
which is a paradigm shift. If in doubt, consider
the following quote by perhaps the most prominent visual system scientist, Torsten N. Wiesel,
who received the Nobel Prize for his work on
visual system specificity and RF organization. In
a lecture held at the symposium “Restoration of
vision after brain damage” during the “VISION
2005” meeting (organized by B. A. S. and T.
Wiesel; Royal National Institute of the Blind,
London) he emphasized the value of vision restoration research:
Restoration of vision after damage is an issue I am
very interested in and I think that there is progress; to find different means of restoring visual
functions is very interesting and encouraging. . .
(My experiments on receptive field enlargements)
are hard evidence that it is possible to restore
(visual) function through time. In this case we
did not make any special effort by stimulating
the eyes,. . . trying to restore visual functions. . .
but this kind of experiment gives you hope that
there is more to learn from this kind of
experiments and also from the clinical work that
it should be possible to have patients restore vision
in spite of initially apparent lack of vision. . .
Time will tell if the proposed residual vision
activation theory is a paradigm shift (Kuhn,
1962) in the fields of low vision, neuro-ophthalmology, and restorative neurology. In any event,
we hope the theory will stimulate others to get
engaged in further discussion and experimental
verification. We do not expect that each individual aspect of the proposed theory will hold forever. But it is a start to better understand the
complex mechanisms of how the brain may overcome visual impairments. For sure, new aspects
will have to be added to the proposed theory.
But nevertheless, it provides a heuristic basis for
further studies in the field of vision restoration,
a rather complex issue in restorative neuroscience. Hopefully, the theory will inspire others to
carry out new experiments and develop new
treatment options. Perhaps the current theory will
be modified or extended at some point. In this
manner, vision restoration may mature to become
a more widely accepted subject. The theory
should lead our way to go beyond the widely
accepted notion that (partial) blindness after retinal and cerebral damage is forever and unchangeable. Rather than turning a “blind eye” on vision
restoration as a real possibility, we shall recognize
that the theory is a basis for a more hopeful attitude: that vision restoration is possible and that
new and innovative solutions may be found that
reduce the impact of visual impairments. Future
research and development will help improve
visual impairments, extending far beyond the conceptual borders that currently limit our view. We
are at the dawn of a better medical care for
patients that greatly suffer from partial blindness
which is inflicted by retinal and cerebral visual
injury.
Acknowledgments
We thank Steffi Matzke and Sylvia Prilloff for
their excellent help preparing the chapter, and
special thanks to W. Waleszczyk (Nencki Institute
of Experimental Biology, Warsaw, Poland) for
insightful comments on a previous version of the
chapter.
252
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enhancing performance for action and perception: multisensory

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