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

Abstracts of National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental Biotechnology

This book contains the abstracts of the papers presented at the National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental Biotechnology (NCB4EBT-2021) Organized by the Department of Biotechnology, National Institute of Technology Warangal, India held on 29–30 January 2021. This conference is the first of its kind organized by NIT-W which covered an array of interesting topics in biotechnology. This makes it a bit special as it brings together researchers from different disciplines of biotechnology, which in turn will also open new research and cooperation fields for them. Conference Title: National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental BiotechnologyConference Acronym: NCB4EBT-2021Conference Date: 29–30 January 2021Conference Location: Online (Virtual Mode)Conference Organizer: Department of Biotechnology, National Institute of Technology Warangal, Indi

Analysis of Outcomes in Ischemic vs Nonischemic Cardiomyopathy in Patients With Atrial Fibrillation A Report From the GARFIELD-AF Registry

IMPORTANCE Congestive heart failure (CHF) is commonly associated with nonvalvular atrial fibrillation (AF), and their combination may affect treatment strategies and outcomes