Poster A53, Tuesday, August 20, 2019, 10:15 am – 12:00 pm, Restaurant Hall
A computational mechanistic account of hemisphere differences in language processing
Ya-Ning Chang1, Matthew Lambon Ralph1;1MRC Cognition and Brain Sciences Unit, University of Cambridge
A classic view of the neural basis of language has postulated the left hemisphere is dominant for language. However, a growing number of neuroimaging studies have demonstrated contributions, albeit weaker, from language regions in the non-dominant right hemisphere. While neuroimaging studies in healthy subjects increasingly show language network is leftward asymmetric but bilateral, patients studies commonly report that aphasia results predominantly from left hemisphere damage in stroke patients. It appears difficult to reconcile the seemingly contradictory findings, and there has yet to have a clear mechanistic account to explain mechanisms underlying changes to the language network between the two cerebral hemispheres. Using neural network modelling, we constructed a bilateral model of language processing to explore how the left and right hemispheres contribute normal and impaired language processing. Specifically, we implemented the model with reference to hemispheric structural asymmetry to investigate if bilateral but asymmetric (left > right) activation in the majority of healthy subjects could result from the greater computational resources available for language processing in the left hemisphere compared to that in the right. With the consideration of hemispheric structural asymmetry, we also investigated if damage to the language regions in the left hemisphere would be more likely to result in impaired language performance (aphasia) because of the removal of major language processing resources. Lastly, we examined if the dynamic changes in activation patterns between the two hemispheres in aphasia recovery could be related to the differential capacity of the left and right hemispheres. The bilateral model demonstrated a link between differential computational resources with language lateralisation. In particular, damage to the model with different levels of severity reproduced the changes in brain activation patterns observed in post-stroke aphasia. The resulting patterns suggest the unequal distribution of resource available for language processing in both the hemispheres is key to accounting for language lateralisation in normal and impaired populations. The simulations provide insights into neural machinery underlying hemispheric language lateralisation and its shifts for recovery from post-stroke aphasia.
Themes: Language Production, Disorders: Acquired
Method: Computational Modeling