The structural and functional correlates of language skills Today`s

Transcription

The structural and functional correlates of language skills Today`s
The structural and functional
correlates of language skills
Dr Fiona M Richardson
Today’s theme
What can studying brain structure teach us about
the functional anatomy?
• Skills correlated with differences in local brain structure
– Links with functional anatomy
– Methods
• Structural studies of typical language skills
– Bilingualism and vocabulary knowledge
• Structural studies of developmental language disorders
– Dyslexia
Changes in brain structure can occur beyond
those associated with development,
ageing and neuropathology
1
Navigational skill of
London taxi drivers
Taxi drivers have more grey matter
in the posterior hippocampi
Maguire et al. (2000) Science
Time as a taxi driver correlates
with grey matter density in the
posterior hippocampus
The hippocampus
Keyboard playing skill
Gaser & Schlaug (2003) Journal of Neuroscience
Juggling
Draganski et al. (2004) Nature
Changes in the intraparietal sulcus and mid-temporal area
2
Methods
• Voxel Based Morphometry (VBM)
Identify local differences in the composition of brain
tissue, while discounting large scale differences in
gross anatomy and position
Gray Matter
.
3 mm
6 mm
• An unbiased analysis technique
• Possible to analyse the whole
brain
3 mm
VOXEL
(Volumetric Pixel)
Structural studies of typical language
skills: bilingualism and vocabulary
knowledge
Bilinguals
Mechelli et al. (2004) Nature
• We can learn multiple languages
– An ability which has been associated
with functional rather than structural
changes in the brain
• English and Italian bilinguals
– Monolinguals
– ‘early’ bilinguals
(learned 2nd language before the age of
5 years)
– ‘late’ bilinguals
–
(learned 2nd language between the
ages of 10 and 15years)
3
• Grey matter density
greater in the
inferior parietal
cortex of bilinguals
than monolinguals
• Effect significant in the left hemisphere
(x = -45, y = -59, z = 48)
• Trend in the right hemisphere
(x = 56, y = -53, z = 42)
• The effect was greater in early bilinguals
• Second language
proficiency correlated
with grey matter
density
(x = -48, y = -59, z = 42)
• Grey matter density
correlated negatively
with age of second
language acquisition
(x = -50, y = -58, z = 42)
In what way is this region important
for monolinguals?
* Reading: Lee et al. (2007)
4
Monolinguals
Lee et al. (2007) Journal of Neuroscience
• 32 right-handed Englishspeaking adolescents
aged between 12-16
years
– Vocabulary test from
Wechsler Intelligence
Scale for Children
WISC-III
• Grey matter density may
correspond to the
number of words learned
Anatomical Perspective
Posterior supramaginal gyrus
Anterior supramaginal gyrus:
• associated with phonological processing
Anterior angular gyrus:
• associated with semantic processing
• Diffusion Tensor Imaging (DTI)
– Patterns of connectivity between the
anterior ANG and anterior SMG
– No route other than via the posterior
SmG
– Region binds phonological and semantic
dimensions of words
5
Outstanding Questions
• Is this effect in the pSMG consistent across
lifespan?
• Are there any other regions that correlate with
differences in vocabulary knowledge?
• Is functional activation in these regions also
affected by vocabulary knowledge?
Structural and functional correlates of
vocabulary knowledge
Richardson et al. (in press) Journal of Cognitive Neuroscience
• Cross-sectional lifespan trajectory 7 to 75 years
• Structure and function
– Functional tasks: listening to and reading sentences
and words
– Vocabulary task:
British Picture Vocabulary Scale – II (BPVS-II)
Correlation between vocabulary and
activation
activation for sentences
• Functional activation processing sentences and words
• Correlation between vocabulary knowledge and brain activation for
sentences and words
6
Structural analysis
• pSMG Region of Interest (ROI): Significant positive
correlation between vocabulary score and grey matter density
in adolescents only, the pSMG was not detected across
lifespan
Structural and functional analyses across lifespan
Vocabulary and brain structure: lifespan trajectories
7
Could the age effects account for
different trajectories in temporal and
parietal regions across lifespan?
Results Summary
• 3 Regions:
– pSMG: structural adolescents only (no functional activation in
pSMG)
– pT-P: structural and functional across lifespan
– pSTS: structural and functional across lifespan
• Contrasting temporal and parietal effects
- Explanation?
the effects in temporal and parietal areas are driven
by different modes of learning
• left pT-P area is linked to syntactic and semantic processing
• the pSTS interfaces between semantic associations and
speech production
– Increased functional activation and grey matter density in the
temporal regions for those with high vocabulary may reflect
learning by context.
• left pSMG is only active during tasks that involve word
learning rather than passive tasks, and correlated with grey
matter in adult bilinguals and teenage monolinguals
– Learning by lexical or conceptual equivalents
8
Further work
•
What do these differences in grey matter
represent?
2 possibilities:
1)
2)
Grey matter increases as a consequence of task demands
More grey matter facilitates learning, i.e. an inherited ability to
acquire more vocabulary knowledge
–
Longitudinal studies are needed to answer this question to
examine changes in grey matter within individuals
Structural studies in developmental
disorders of language: dyslexia
About dyslexia
• Children with dyslexia experience particular difficulty in
learning to read, despite normal non-verbal intelligence
and access to educational resources
• There can be accompanying non-verbal deficits, such as
difficulties in balance, co-ordination and fine motor skills
• Subtle difficulties can still be detected in adulthood
9
•
Dyslexia is predominantly associated with a deficit in
phonological processing, however there are multiple
theories regarding potential causes:
1.
Double deficit in phonological and rapid temporal processing
2.
Multisensory temporal processing
3.
Magnocellular deficit hypothesis
4.
Cerebellar deficit hypothesis
5.
Phonological deficit with co-occurring general deficit in
sensorimotor processing
The neural basis of dyslexia
The study of brain structure in this context is
carried out on the premise that deviations in
typical morphology may provide an explanation
for the behavioural profiles and identify potential
neural markers of the disorder
There are problems with this approach…
given that brain structure correlates with learning and
ability, less grey matter could simply be a consequence
rather than a cause of the disorder.
Different approaches to the structural study
of dyslexia
• Post-mortem studies
• Anatomical MRI studies
ROI
approach
• VBM studies
• DTI studies
Whole brain
analysis
10
• There are 7 published VBM studies of dyslexia to date
• These studies compare dyslexics to age-matched
controls
• These studies differ in their methodology on the basis of
3 criteria:
VB M STUDIES OF GREY M ATTER IN DYSLEXIA
Study
Brown et al.(2001)
Brambati et al. (2004)
Silani et al. (2005)
Vinckenbosch et al. (2005)
Eckert et al (2005)
Hoeft et al. (2007)
Kronbichler et al. (2008)
Sample
adults
adults
adults
adults
children
adolescents
adolescents
M eas ure
density
volume
volume
density
volume
volume
volume
Statistical analys is
whole brain p < 0.05, corrected p < 0.05 for extent
small volume correction, p < 0.05
small volume correction, p < 0.05
corrected p < 0.01 for height & p < 0.05 extent
p < 0.01 uncorrected
whole brain corrected at p = 0.01 for height and extent
whole brain p < 0.005 uncorrected and volume of
interest corrected FDR < 0.05
Findings from VBM studies of dyslexia
• The majority of studies detect regions where dyslexics have less
grey matter in comparison to controls
Regions identified as having less
grey matter in dyslexics
Regions showing positive
behavioural correlations
Most consistently occurring across these studies:
1) Posterior temporal/ temporal-parietal regions
2) 2) Occipital/occipito-temporal regions
3) Cerebellum
Links with functional anatomy
1)
Posterior temporal/ temporal-parietal regions
- Dyslexics show hypo-activation in these areas
From: Paulesu et al. (2001) Science
* Reading: Hoeft et al. (2007)
11
2) Occipital/occipito-temporal regions
- Also known as the fusiform gyrus or Visual Word Form Area
(VWFA)
- Dyslexics also show less activation (bilaterally) in occipitotemporal regions
- Activation in this area is also correlated with reading skill and
increases following remediation
From: Shaywitz et al. (2002) Biological Psychiatry
3) Cerebellum
- Less asymmetrical in dyslexics in comparison to the normal
population
- Abnormal increase in symmetry has also been linked to poorer
performance in non-word reading (Rae et al., 2002).
From: Desmond & Fiez (1998) TICS
Regions showing increases in grey matter
• These studies were carried out in older teenagers and adults
• Potential compensation?
From a remediation study by Temple et al. (2003) PNAS
12
White matter studies of Dyslexia using DTI
• Degree of directional diffusion (fractional anisotropy: FA) positively
correlated with behavioural performance on reading and rapid
naming tasks in a left temporo-parietal region.
• White matter pathways linking frontal, parietal and posterior
temporal regions
Summary: Neural basis of Dyslexia
• It is only possible to broadly characterise regions linked
with dyslexia on the basis of present structural studied
• Majority of regions that differ between dyslexics and
controls show less grey matter
– 3 most consistently identified regions
• Consistency across DTI studies of white matter in
dyslexia
Conclusions
13
• Clear relationship between local brain structure and
language skills
– May also be linked to differences in functional activation in the
same brain regions.
• Local brain structure and functional activation may be
influenced by a common underlying mechanism
• Structural studies are useful to detect regions engaged
in tasks that are difficult to tap in functional studies
• What do these changes/differences in brain structure
represent?
– Changes (longitudinal)
– Differences (cross-sectional)
• Are differences a precursor or consequence of skill
acquisition?
Work currently in progress aims to answer these
outstanding questions
That’s it for now folks, thanks for listening!
The End
14
References:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Ashburner, J., & Friston, K. (2000). Voxel-based morphometry – the methods. Neuroimage, 11, 805-821.
Brambati, S.M., Termin, C., Ruffino, M., Stella, G., Fazio, F., Cappa, S.F., & Perani, D. (2004). Regional reductions
of gray matter volume in familial dyslexia. Neurology, 63, 742-745.
Brown, W.E., Eliez, V., Menon, J. M., Rumsey, C. D., White, B.A., & Reiss, A.L. (2001). Preliminary evidence of
widespread morphological variations of the brain in dyslexia. Neurology, 56, 781-783
Cohen, L., Dehaene, S., Naccache, L., Lehéricy, S., Dehaene-Lambertz, G., Hénaffm M.-A., & Michel, F. (2000).
The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects
and posterior split-brain patients. Brain, 123, 291-293.
Cohen L, Lehéricy, S., Chochon, F., Lemer, C., Rivaud, S., & Dehaene, S. (2002). Language-specific tuning of
visual cortex: functional properties of the Visual Word Form Area. Brain, 125, 1054-1069.
Desmond, J.E., & Fiez, J.A. (1998). Neuroimaging studies of the cerebellum: language, learning and memory.
Trends in Cognitive Sciences, 2, 355-361.
Deutsch, G.K., Dougherty, R.F., Bammer, R., Siok, W.T., Gabrieli, J.D.E., & Wandell, B. (2005). Children’s reading
performance is correlated with white matter structure measured by diffusion tensor imaging. Cortex, 41, 354-363.
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: changes in
grey matter induced by training. Nature, 427, 311-312.
Elbro, C., Nielsen, I., & Petersen, D.K. (1994). Dyslexia in adults: evidence for deficits in non-word reading and in
the phonological representation of lexical items. Annals of Dyslexia, 44 (1), 203-226.
Fawcett, A. J., & Nicolson, R. I. (1995). Persistent deficits in motor skill of children with dyslexia. Journal of Motor
Behavior, 27 (3), 235-240.
Gaser, C., & Schlaug, G. (2003). Brain structures differ between musicians and non-musicians. Journal of
Neuroscience, 23, 21-24.
Habib, M. (2000). The neurological basis of developmental dyslexia an overview and working hypothesis.
Brain,123, 2373-2399.
Hoeft, F., Meyler, A., Hernandez, A., Juel, C., Taylor-Hill, H., & Martindale, J.L., et al. (2007). Functional and
morphometric brain dissociation between dyslexia and reading ability. Proceedings of the National Academy of
Sciences, 4 (10), 4234-4239.
Kibby, M.Y., Fancher, J.B., Markanen, B.S.,& Hynd, G.W. (2008). A quantitative magnetic resonance imaging
analysis of the cerebellar deficit hypothesis of dyslexia. Journal of Child Neurology, 23 (4), 368-380.
Klingberg, T., Hedehus, M., Temple, E., Salz, T., Gabrieli, J.D.E., & Moseley, M.E., et al. (2000). Microstructure of
temporo-parietal white matter as a basis for reading ability: evidence from diffusion tensor magnetic resonance
imaging. Neuron, 25, 493-500.
Kronbichler, M,. Wimmer, H., Saffen, W., Hutzler, F., Mair, A., & Ladurner, G. (2008). Developmental dyslexia:
grey matter abnormalities in the occipitotemporal cortex. Human Brain Mapping, 29, 613-625.
Le Bihan, D., Mangin, J-F., Poon, C., Clark, C.A., Pappata, S., Molko, N., & Hughes, C. (2001). Diffusion tensor
imaging: concepts and applications. Journal of Magnetic Resonance Imaging, 13, 534-546.
Lee, H.L., Devlin, J.T., Shakeshaft, C., Stewart, L.H., Brennan, A., & Glensman, J., et al. (2007). Anatomical
traces of vocabulary acquisition in the adolescent brain. Journal of Neuroscience, 27, 1184-1189.
Maguire, E.A., Gadian, D.G., Johnrude, I.S., Good, C.S., Ashburner, J., Frackowiack, R.S.J., & Frith, C.D. (2000).
Navigation-related structural changes in the hippocampi of taxi drivers. Proceedings of the National Academy of
Sciences, USA, 97, 4398-4403.
McCandliss, B.D. & Noble, K.G. (2003). The development of reading impairment: a cognitive neuroscience model.
Mental Retardation and Developmental Disabilities Research Reviews, 9, 196-205.
Mechelli, A., Crinion, J.T., Noppeney, U., O’Doherty, J., Ashburner, J., Frackowiak, R.S.J., & Price, C.J. (2004).
Neurolinguistics: structural plasticity in the bilingual brain. Nature, 431, 757.
Nicholson, R.I., Fawcett, A.J., & Dean, P. (2001). Developmental dyslexia: the cerebellar deficit hypothesis.
Trends in Neurosciences, 24 (9), 508-511.
Niogi, S.N., & McCandliss, B.D. (2006). Left lateralized white matter microstructure accounts for individual
differences in reading ability and disability. Neuropsychologia, 44, 2178-2188.
Paulesu, E., Demonet, J.-F., Fazio, F., McCrory, E., Chanoine, V., & Brunswick, N., et al. (2001). Dyslexia: cultural
diversity and biological unity. Science, 291, 2165-2167.
Powell, R.H., Parker, G., Alexander, D.C., Symms, M.R., Boulby, P.A., & Wheeler-Kingshott, C.A. (2006).
Hemispheric asymmetries in language-related pathways: a combined functional MRI and tractography study.
Neuroimage, 32, 388-399.
Ramus, F. (2004). Neurobiology of dyslexia: a reinterpretation of the data. Trends in Neurosciences, 27, 721-726.
Richardson, F.M., Thomas, M.S.C., Filipii, R., Harth, H., & Price, C.J. (in press). Contrasting effects of vocabulary
acquisition on temporal and parietal brain structure across lifespan. Journal of Cognitive Neuroscience.
Shaywitz, B.A., Shaywitz, S.E., Pugh, K.E., Fulbright, R.K., Mencl, W.E., & Fulbright, R.K., et al. (2002). Disruption
of posterior brain systems for reading in children with developmental dyslexia. Biological Psychiatry, 52, 101-110.
Shaywitz, B.A., Shaywitz, S.E., Blachman, B.A., Pugh, K.E., Fulbright, R.K., & Skudlarski, P., et al. (2004).
Development of left occipitotemporal systems for skilled reading in children after phonologically-based
intervention. Biological Psychiatry, 55, 926-933.
Silani, G., Frith, U., Demonet, J.-F., Fazio, F., Perani, D., & Price, C., et al. (2005). Brain abnormalities underlying
altered activation in dyslexia: a voxel based morphometry study. Brain, 128, 2453-2461.
Simos, P.G., Fletcher, J.M., Bergman, E., Breier, J.I., Foorman, B.R., & Castillo, R.N., et al. (2002). Dyslexiaspecific brain activation profile becomes normal following successful remedial training. Neurology, 58, 1203-1213.
Snowling, M. J. (2000). Dyslexia (2nd edition). Oxford, England: Blackwell.
Stein, J., & Walsh, V. (1997). To see but not to read: the magnocellular theory of dyslexia. Trends in
Neurosciences, 20 (4), 147-152.
Temple, E., Deutsch, G.K., Poldrack, R.A., Miler, S.L., Tallal, P., Merzenich, M.M. & Gabrieli, J.D.E. (2003). Neural
deficits in children with dyslexia ameliorated by behavioral remediation: Evidence from functional MRI.
Proceedings of the National Academy of Sciences USA, 100 (5), 2860-2865.
Vinckenbosch, E., Robichon, F., & Eliez, Z. (2005). Grey matter alteration in dyslexia: converging evidence from
volumetric and voxel-by-voxel MRI analyses. Neuropsychologia, 43, 324-331.
Wolf, M., & Bowers, P.G. (1999). The double-deficit hypothesis for developmental dyslexias. Journal of
Educational Psychology, 91 (3),415-438.
15