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.
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.
Authors: Nico U F Dosenbach; Damien A Fair; Francis M Miezin; Alexander L Cohen; Kristin K Wenger; Ronny A T Dosenbach; Michael D Fox; Abraham Z Snyder; Justin L Vincent; Marcus E Raichle; Bradley L Schlaggar; Steven E Petersen Journal: Proc Natl Acad Sci U S A Date: 2007-06-18 Impact factor: 11.205
Authors: Stephen M Smith; Peter T Fox; Karla L Miller; David C Glahn; P Mickle Fox; Clare E Mackay; Nicola Filippini; Kate E Watkins; Roberto Toro; Angela R Laird; Christian F Beckmann Journal: Proc Natl Acad Sci U S A Date: 2009-07-20 Impact factor: 11.205
Authors: Elisa Biou; Hélène Cassoudesalle; Mélanie Cogné; Igor Sibon; Isabelle De Gabory; Patrick Dehail; Jerome Aupy; Bertrand Glize Journal: Ann Phys Rehabil Med Date: 2019-01-17