Dynamic functional network connectivity and neuroplasticity in post-stroke aphasia

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PROJECT SUMMARY/ABSTRACT Language impairment, known as aphasia, is very common after stroke and the extent to which patients recover and respond to language therapy varies greatly. Functional MRI, including resting-state functional connectivity, is a widely used tool to study reorganization in the brain after an acquired brain injury such as stroke. Specifically, resting-state functional connectivity measurements can provide important information on the connectivity and organization of functional networks in the brain and changes in that connectivity over time (i.e., functional network plasticity). This project will make use of both static and dynamic resting-state functional connectivity to identify relationships between the short-term (seconds to minutes) temporal dynamics of functional network connectivity and long-term (months) functional network plasticity in people with post-stroke aphasia. We have previously shown that baseline measures of both static functional connectivity and variability in dynamic functional network connectivity are predictive of treatment response in people with post-stroke aphasia. However, it is not known whether this association is mediated by differences in functional network plasticity. For the studies proposed in this application, we will test the following hypotheses in three aims: (1) that patients with post-stroke aphasia have altered dynamic functional connectivity compared to healthy controls characterized by decreased temporal variability and (2) that higher baseline temporal variability of dynamic functional connectivity (i.e., more like that of healthy controls) is related to greater treatment-induced changes in functional network topology. For the first aim, dynamic functional connectivity will be computed from resting state functional MRI data for 40 individuals with post-stroke aphasia and 40 healthy controls, currently being collected as part of a larger ongoing study. Temporal variability will then be compared between the two groups. For the second aim, the temporal variability of dynamic functional network connectivity computed from existing resting-state functional MRI data from 30 individuals with post-stroke aphasia will be related to changes in graph theory measures of static functional connectivity from pre- to post-treatment. For the third aim computational modeling will be used to test the following proposed mechanism: (1) Transient synchronization between brain regions facilitates activity dependent plasticity and (2) greater diversity of transient connectivity configurations provides a wider range of opportunities for this facilitation of plasticity resulting in greater treatment-induced reorganization of functional networks. The results will yield insights into the factors and mechanisms underlying variation in language recovery after stroke and may inform the development of new or improved tools for prognostication in these patients. These studies will be carried out in the Center for Brain Recovery at Boston University as a part of pre-doctoral training towards a physician- scientist career. As part of the fellowship training, emphasis is placed on hypothesis-driven experimental design, integration of basic research with clinical practice, scientific communication, and mentoring.