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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.
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Efficient orthogonal functional magnetic resonance imaging designs in the presence of drift
To study brain activity, by measuring changes associated with the blood flow in the brain, functional magnetic resonance imaging techniques are employed. The design problem in event-related functional magnetic resonance imaging studies is to find the best sequence of stimuli to be shown to subjects for precise estimation of the brain activity. Previous analytical studies concerning optimal functional magnetic resonance imaging designs often assume a simplified model with independent errors over time. Optimal designs under this model are called g-lag orthogonal designs. Recently, it has been observed that g-lag orthogonal designs also perform well under simplified models with auto-regressive error structures. However, these models do not include drift. We investigate the performance of g-lag orthogonal designs for models that incorporate drift parameters. Identifying g-lag orthogonal designs that perform best in the presence of a drift is important because a drift is typically assumed for the analysis of event-related functional magnetic resonance imaging data.
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- Award ID(s):
- 1935729
- PAR ID:
- 10546895
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Statistical Methods in Medical Research
- Volume:
- 30
- Issue:
- 1
- ISSN:
- 0962-2802
- Format(s):
- Medium: X Size: p. 277-285
- Size(s):
- p. 277-285
- Sponsoring Org:
- National Science Foundation
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