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