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Reduced functional connectivity may drive task-fMRI underactivation within spared language-tissue in chronic post-stroke aphasia
Poster A13 in Poster Session A, Thursday, October 6, 10:15 am - 12:00 pm EDT, Millennium Hall
Also presenting in Poster Slam A, Thursday, October 6, 10:00 - 10:15 am EDT, Regency Ballroom
Andrew DeMarco1,2, Tyler Ketchabaw1,2, Candace van der Stelt1,3, Sachi Paul1,3, Alycia Laks1,3, Elizabeth Dvorak1,3, Sarah Snider1,3, Peter Turkeltaub1,3,4; 1Center for Brain Plasticity and Recovery, Georgetown University, 2Department of Rehabilitation Medicine, Georgetown University, 3Department of Neurology, Georgetown University, 4National Rehabilitation Hospital
A consistent finding in post-stroke aphasia is that spared language regions exhibit underactivation during language processing. When measured reliably, degree of underactivation contributes to the severity of behavioral aphasic impairment, even independent of lesion size. Moreover, resolution of task-related underactivation contributes to early post-stroke aphasia recovery. Therefore, a mechanistic understanding of processes driving underactivating language nodes (ULNs) could enrich our understanding of network dynamics in post-stroke aphasia and help explain aphasic deficits beyond lesion characteristics. Measures of connectivity index the relationship between brain regions, making it a natural candidate for understanding how brain regions might manifest functional changes even when spared by a lesion. Thus, the aim of this project was to identify how reduced connectivity or disconnection might drive underactivation in spared tissue. We tested two main hypotheses, namely that reduced activation in ULNs would correlate: 1) with reduced connectivity within those same ULNs, and 2) with reduced connectivity between ULNs and normally-activating language nodes. A neurotypical cohort (N=66) and a cohort of people with aphasia (PWA) and chronic stroke (N=51) underwent a structural scan, an adaptive semantic decision fMRI task and a movie-viewing resting scan. Lesioned tissue was manually traced and excluded from analyses. We first constructed a language-network parcellation by intersecting a mask of regions activated by the neurotypical group with an anatomical atlas to break up large clusters. We then determined whether each resulting node classifies as a ULN, based on a 2-sample t-test comparing PWAs to neurotypical activation for that node. We then computed functional connectivity between all nodes. Finally, to assay how connectivity might drive underactivation, in the patient cohort, we correlated average activation in ULNs with average functional connectivity both 1) between ULNs, and 2) between ULNs and the rest of the network. The language-network parcellation consisted of 35 nodes, encompassing left temporal and frontal lobes, less-extensive right-hemisphere homotopes, and right cerebellum. Patients exhibited underactivation in 10 nodes (P < .01, unc.), including left mid- and anterior temporal lobe, angular gyrus, retrosplenial cortex, medial prefrontal and bilateral medial temporal lobes, and right cerebellum. Average activation in the ULNs correlated significantly with aphasia quotient, even controlling for lesion volume (r=.42, P=.002). Average activation in ULNs was significantly correlated with average functional connectivity (r=.38, P=.007) between the 10 ULNs, and this relationship remained after controlling for lesion volume (r=.37, P=.009). Similarly, average activation in ULNs was significantly correlated with average functional connectivity (r=.45, P=.001) between the ULNs and the spared language network, and the relationship remained after controlling for lesion volume (r=.46, P<.001). We found evidence that reduced functional connectivity within the language network may play a role in underactivation within spared tissue, which is associated with worse aphasic symptoms. These results suggest that underactivation may relate to network effects caused by the lesion. Next steps include considering how structural disconnection may influence underactivation, fractionating the underactivating nodes into subnetworks, and understanding how these connectivity changes may involve canonical brain networks.
Topic Areas: Disorders: Acquired, Computational Approaches