One goal of the Healthy People 2010 program is to reduce health
disparities across different segments of the population. Diagnosis and
treatment of bilingual aphasia is one area where disparities continue
to exist even though this topic is of great importance in an
increasingly bilingual world. The current research on this topic,
however, lacks specific recommendations on which languages should be
trained in a bilingual aphasic individual and to what extent
cross-language transfer occurs subsequent to rehabilitation. Factors
contributing to the paucity of research in this area relate to the
multitude of possible language combinations in a bilingual individual,
the relative competency of the two languages of the bilingual
individual and the effect of focal brain damage on bilingual language
representation. It is, however, unfeasible to examine these issues
without undertaking a large scale longitudinal study in this
population.
As a potential solution, this project will systematically examine
the extent of cross-language transfer subsequent to rehabilitation
using a computational model. This model will be developed to simulate
a bilingual language system in which language representations can vary
by age of acquisition and relative proficiency, and will be
subsequently lesioned and retrained to improve output. The training
will be provided in one language and the extent of cross-language
transfer will be examined. It is predicted that age of acquisition,
the level of pre-morbid language proficiency and post-morbid language
performance will influence the nature and degree of cross-language
transfer. Further, the model's power to predict the optimal language
to be treated will be compared to data obtained from behavioral
interventions from a sample of patients with bilingual aphasia. The
work is innovative because it uses a computational model to predict
optimal rehabilitation protocols to facilitate the greatest amount of
language recovery in bilingual aphasia. The successful completion of
this project is expected to have an important impact on rehabilitation
of stroke and bilingual aphasia as well as on the applications of
computational modeling.
This research is supported by the National Institutes of Health
under grant R21-DC009446, with Swathi Kiran of Boston University as a
co-PI.