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Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia
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Billot, Anne; Lai, Sha; Varkanitsa, Maria; Braun, Emily J.; Rapp, Brenda; Parrish, Todd B.; Higgins, James; Kurani, Ajay S.; Caplan, David; Thompson, Cynthia K.; Ishwar, Prakash; Betke, Margrit; Kiran, Swathi
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In: Stroke (2022)
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Abstract:
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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Keyword:
Original Contributions
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022691/
http://www.ncbi.nlm.nih.gov/pubmed/35078348
https://doi.org/10.1161/STROKEAHA.121.036749
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| 2 |
Factors affecting outcomes for semantic feature analysis treatment in post-stroke bilingual aphasia
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Behavioral and neural effects of intensive cognitive and communication rehabilitation in young college-bound adults with acquired brain injury
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Varying degrees of cognitive control and its impact on lexical access during verbal fluency tasks in bilingual persons with aphasia
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Acquired Brain Injury in Adults: A Review of Pathophysiology, Recovery, and Rehabilitation
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In: Perspect ASHA Spec Interest Groups (2021)
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Clustering and Switching in Verbal Fluency Across Varying Degrees of Cognitive Control Demands: Evidence From Healthy Bilinguals and Bilingual Patients With Aphasia
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In: Neurobiol Lang (Camb) (2021)
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Predicting language treatment response in bilingual aphasia using neural network-based patient models
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In: Sci Rep (2021)
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Telerehabilitation for Word Retrieval Deficits in Bilinguals With Aphasia: Effectiveness and Reliability as Compared to In-person Language Therapy
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In: Front Neurol (2021)
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Predicting language recovery in post-stroke aphasia using behavior and functional MRI
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In: Sci Rep (2021)
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Telerehabilitation for word retrieval deficits in bilinguals with aphasia: Effectiveness and reliability as compared to in-person language therapy
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A Virtual, Randomized, Control Trial of a Digital Therapeutic for Speech, Language, and Cognitive Intervention in Post-stroke Persons With Aphasia ; Frontiers in Neurology
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| 12 |
Aphasia management in growing multiethnic populations
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In: Research outputs 2014 to 2021 (2020)
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Epilogue: Harnessing the experimental and clinical resources to address service imperatives in multiethnic aphasia caseloads
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In: Research outputs 2014 to 2021 (2020)
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| 14 |
Predicting treatment outcomes for bilinguals with aphasia using computational modeling: Study protocol for the PROCoM randomised controlled trial
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Videoconference Administration of the Western Aphasia Battery–Revised: Feasibility and Validity
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In: Am J Speech Lang Pathol (2020)
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Predicting treatment outcomes for bilinguals with aphasia using computational modeling: Study protocol for the PROCoM randomised controlled trial
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In: BMJ Open (2020)
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Verbal fluency as a measure of lexical access and cognitive control in bilingual persons with aphasia
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In: Aphasiology (2020)
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Functional Near-Infrared Spectroscopy in the Study of Speech and Language Impairment Across the Life Span: A Systematic Review
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In: Am J Speech Lang Pathol (2020)
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Pre-treatment graph measures of a functional semantic network are associated with naming therapy outcomes in chronic aphasia
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In: Brain Lang (2020)
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