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Abstract Stroke-induced alterations in cerebral blood flow (perfusion) may contribute to functional language impairments in chronic aphasia, particularly in perilesional tissue. Abnormal perfusion in this region may also serve as a biomarker for predicting functional improvements with behavioral treatment interventions. Using pseudo-continuous arterial spin labeling in magnetic resonance imaging (MRI), we examined perfusion in chronic aphasia, in perilesional rings in the left hemisphere and their right hemisphere homologues. In the left hemisphere we found a gradient pattern of decreasing perfusion closer to the lesion. The opposite pattern was found in the right hemisphere, with significantly increased perfusion close to the lesion homologue. Perfusion was also increased in the right hemisphere lesion homologue region relative to the surrounding tissue. We next examined changes in perfusion in two groups: one group who underwent MRI scanning before and after three months of a behavioral treatment intervention that led to significant language gains, and a second group who was scanned twice at a three-month interval without a treatment intervention. For both groups, there was no difference in perfusion over time in either the left or the right hemisphere. Moreover, within the treatment group pre-treatment perfusion scores did not predict treatment response; neither did pre-treatment perfusion predict post-treatment language performance. These results indicate that perfusion is chronically abnormal in both hemispheres, but chronically abnormal perfusion did not change in response to our behavioral treatment interventions, and did not predict responsiveness to language treatment. 

Background: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke. Methods: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data. Results: The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P <0.001) or a single feature set (F1 range=0.68–0.84, P <0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87). Conclusions: While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.