Whole-brain functional connectome-based multivariate classification of post-stroke aphasia

Patients with post-stroke aphasia (PSA) show abnormalities of intrinsic functional connectivity. However, whether the whole-brain functional connectome can be used as a feature to distinguish patients with PSA from healthy controls is poorly understood. We aim to distinguish PSA patients from controls using whole-brain functional connectivity-based multivariate pattern analysis. These features would be helpful in the understanding of the pathophysiology of PSA. In the present study, resting-state functional magnetic resonance images (fMRI) were acquired in 17 patients with PSA and 20 age- and sex-matched healthy controls. We used functional connectivity pattern and linear support vector machine to classify two groups. The results showed that the accuracy of classification reached to 86.5%, sensitivity reached to 76.5%, and specificity reached to 95.0%. In addition, consensus connections were mainly located in the fronto-parietal, auditory, sensory-motor, and visual networks. Furthermore, the right rolandic operculum contributed the highest weight. We suggest that whole-brain functional connectivity could be used as a potential neuromarker to distinguish PSA patients from controls.