While a good amount of research has been conducted on brain and behavior impairments in acute stroke survivors, little is known about those who are living with aphasia chronically. That is, the field has yet to understand the long term effects of stroke in terms of brain structure support (viability and functionality) and its lik to observed language impairments. In this exploratory proposal we seek to better understand factors underlying the heterogeneity of this chronic aphasia population by systematically, and with more accuracy, defining areas of the brain that are both structurally and 'functionally' impacted. We defined neural tissue as being functionally impacted if it appears viable, but demonstrates reduced blood flow, or hypoperfusion. Hypoperfused tissue has been linked to impaired cognitive and language abilities in acute aphasia, and, more recently in a scant number of investigations with individuals with chronic aphasia. To accomplish this, we take a novel, cutting edge approach by using newer innovative perfusion mapping protocols which include individual measures of blood transit delay. With this information, we are able to collect optimized perfusion maps to better assess viability of neural tissue in the brain. We approach our work by systematically combining two methods- cytoarchitectonic probability and perfusion-informed (individualized) lesion mapping - to detail site and extent of functional lesions in chronic aphasia while associating the resulting maps to individual language behavior. Our specific aims are to use cytoarchitectonic probability maps to better detail areas of the brain effected by stroke by (1) detailing lesion characteristics by using cytoarchitectonically defined probability maps of the language network; (2) identifying hypoperfused, functional lesions, in chronic aphasia and using individualized blood flow timing to investigate the effects of increase blood delivery time on lesion detection; and (3) examining which method, cytoarchitectonic or perfusion-informed lesion maps, is a better predictor of language impairments.
NIH ExPorter
NIH ExPorter