Multimodal neural and behavioral data predict response to rehabilitation in chronic post-stroke aphasia

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.P50 DC012283 - NIDCD NIH HHShttps://www.ahajournals.org/doi/10.1161/STROKEAHA.121.036749Published versio

Resting State fMRI in Parkinson’s Disease and Progressive Supranuclear Palsy

Resting state functional magnetic resonance imaging (RS-fMRI) is a noninvasive method used for investigating the functional organization of the human brain. By using a seed based approach one can examine low frequency fluctuations in the BOLD signal during non-activity, in order to identify areas that are functionally connected with each other. Moreover, this technique has been applied to identify changes in functional connectivity between health controls and subjects with neurodegenerative diseases, such as Parkinson’s disease (PD) and Progressive Supranuclear Palsy (PSP). Study 1 compares resting state connectivity between healthy controls, de novo and off-medicated PD subjects. The study expands upon previous RS-fMRI PD studies by examining subthalamic nucleus – sensorimotor cortex in subjects who have never been treated with anti-parkinsonian medication. We found that Parkinson’s subjects exhibited increased subthalamic nucleus – sensorimotor cortex functional connectivity compared with healthy controls. Moreover, there was a shift in the area of increased functional connectivity between de novo PD and moderate PD. The region of overlap between the two groups correlated with UPDRS motor section scores. Study 2 compares functional connectivity of basal ganglia and cortical motor regions between healthy controls, off medicated PD and off medicated PSP subjects. PSP subjects exhibited widespread changes in functional connectivity patterns when compared with controls and PD subjects. Moreover, the supplementary area showed the greatest change in patterns between PD and PSP. This area is associated with gait and posture – two of the cardinal symptoms associated with PSP. Collectively, these findings enhance our understanding of the underlying functional changes associated with various stages of Parkinson’s disease as well as provide evidence as to the extent of changes in PSP compared with PD, since PSP is often misdiagnosed as PD in the earlier stages