Poster A7, Tuesday, August 20, 2019, 10:15 am – 12:00 pm, Restaurant Hall
Functional connectivity underlying language reorganization in chronic post-stroke aphasia using resting-state magnetoencephalography
Priyanka Shah-Basak1,2, Gayatri Sivaratnam1, Selina Teti1, Alexander Francois-Nienaber1, Aneta Kielar3, Jed Meltzer1,2,4,5;1Rotman Research Institute, Baycrest Health Sciences, Toronto, 2Canadian Partnership for Stroke Recovery, Ottawa, 3Department of Speech, Language, and Hearing Sciences, University of Arizona, 4Department of Speech-Language Pathology, University of Toronto, 5Department of Psychology, University of Toronto
Background: Post-stroke aphasia is a consequence of localized stroke-related damage as well as global disturbances in highly interactive and bilaterally-distributed brain networks. It is widely accepted now that aphasia is a network disorder and that it should be treated as such when examining the reorganization and recovery mechanisms after stroke. A number of functional MR imaging (fMRI) studies have explored changes in functional connectivity (FC) and network properties in post-stroke aphasia. However, the results from these studies are limited to very low-frequency ranges because of fMRI’s low temporal resolution. In contrast, electrophysiological methods such as magnetoencephalography (MEG) offers a much higher temporal resolution. In the current study, we sought to investigate FC using resting-state MEG (rs-MEG) in the alpha frequency band (8-12 Hz) by estimating amplitude envelope correlations across nodes in the whole-brain. Methods: Rs-MEG was recorded for 300 seconds in 21 chronic stroke survivors with aphasia and in 20 age- and sex-matched controls. Source-level MEG activity was reconstructed at voxels spaced 10 mm apart using a synthetic aperture magnetometry (SAM) beamformer. Principal component analysis was used to extract a representative signal from each of 72 cortical and subcortical atlas-defined regions (or nodes) from the voxel-wise source activity. The representative node signals were band-pass filtered into the alpha band (8-12 Hz), and were corrected for spatial leakage using the closest symmetric method introduced by Colclough and colleagues (2015). The amplitude envelope obtained from Hilbert transformation was down-sampled, and pairwise Pearson’s correlation coefficients were computed between each pair of nodes as a measure of FC. The 72×72 adjacency matrices of amplitude correlations were compared between groups in a Partial Least Squares (PLS) analysis, and were subjected to graph theoretical analysis. Within the stroke group, correlations between FC and the fluency measure from the Western Aphasia Battery (WAB) were computed. Results: The intra- and inter-hemispheric FC was greatly reduced in stroke among the temporal, parietal and frontal language regions. These reductions in stroke were accompanied by significant increases in FC among the right language homolog regions, involving the right angular and inferior frontal gyri as well as the domain-general anterior cingulate gyri (p<0.001, interpreted at a bootstrap ratio of 3.0). The small-worldliness (p=0.025) was significantly reduced in stroke, with increases in characteristic path length (p=0.038). At the regional level, the hubs based on degree and betweenness were shifted from left in controls to the right angular and inferior parietal regions in stroke. Betweenness was increased for the right parietal regions in stroke. Finally, correlations with WAB fluency scores indicated that better performance is associated with rich intra-right and interhemispheric connections among the temporo-occipital and fronto-parietal regions, especially involving the frontal orbital cortex. Conclusions: Our findings support the adaptive role of connections formed with the right homolog regions and domain-general regions in language reorganization after stroke. These findings will aid in hypothesis-driven examination of changes in connectivity and network properties as a function of language and non-invasive brain stimulation therapies.
Themes: Disorders: Acquired, Computational Approaches
Method: Electrophysiology (MEG/EEG/ECOG)