Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery. (February 2021)

• Record Type: Journal Article
• Authors: Duncan, E. Susan; Shereen, A. Duke; Gentimis, Thanos; Small, Steven L.
• Is Part Of: Neurorehabilitation & neural repair. Volume 35:Number 2(2021)
• Page Start: 158
• Page End: 168
• Publication Date: 2021-02
• Subjects: stroke -- aphasia -- rehabilitation -- magnetic resonance imaging -- functional neuroimaging -- supervised machine learning; Nervous system -- Diseases -- Patients -- Rehabilitation -- Periodicals; Brain damage -- Patients -- Rehabilitation -- Periodicals; Spinal cord -- Wounds and injuries -- Patients -- Rehabilitation -- Periodicals; Nervous system -- Regeneration -- Periodicals; Neuroplasticity -- Periodicals; 616.804305
• DOI: 10.1177/1545968320981953
• Languages: English
• ISSNs: 1545-9683

Abstract

Background: Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint. Objective: We investigated the stability of functional connectivity across tasks and sessions in a cohort of individuals with chronic stroke using a supervised machine learning approach. Methods: Twelve individuals with chronic stroke underwent functional magnetic resonance imaging (fMRI) seven times over 18 weeks. The middle 6 weeks consisted of intensive aphasia therapy. We collected fMRI data during rest and performance of 2 tasks. We calculated functional connectivity metrics for each imaging run, then applied a support vector machine to classify data on the basis of participant, task, and time point (pre- or posttherapy). Permutation testing established statistical significance. Results: Whole brain functional connectivity matrices could be classified at levels significantly greater than chance on the basis of participant (87.1% accuracy; P < .0001), task (68.1% accuracy; P = .002), and time point (72.1% accuracy; P = .015). All significant effects were reproduced using only the contralesional right hemisphere; the left hemisphere revealed significant effects for participant and task, but not time point. Resting state data could also be used to classify task-based data according to subject (66.0%; P < .0001). While the strongest posttherapy changes occurred among regions outside putative language networks, connections with traditional language-associated regions were significantly more positively correlated with behavioral outcome measures, and other regions had more negative correlations and intrahemispheric connections. Conclusions: Findings suggest the profound importance of considering interindividual variability when interpreting mechanisms of recovery in studies of functional connectivity in stroke.