ArticleStroke2022
Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia.
Article in Stroke, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004-2023).Frontiers in neurology · 2025Pooled it
- White matter predictors of cerebellar tDCS treatment effects in aphasia rehabilitation.Frontiers in neurologyTrial
- Scalp acupuncture coupled with speech-language therapy modulates the ventral-dorsal structural brain network in patients with post-stroke anomia: a randomized clinical and DTI neuroimaging trial.Chinese medicine · 2026Article
- Narrative Discourse Predictors of Response to Naming Intervention in Aphasia.Journal of speech, language, and hearing research : JSLHR · 2026Article
- Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery.medRxiv : the preprint server for health sciences · 2026Article
- Decoding Post-Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework.CNS neuroscience & therapeutics · 2026Article
- Predicting Post-Stroke Aphasia Speech Performance from Multimodal Data with Explainable Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Network flexibility facilitates treatment-induced recovery in post-stroke aphasia.Brain communications · 2026Article
- Test-retest reliability of edge-level resting-state functional connectivity in people with aphasia.Neuroimage. Reports · 2025Article
- Navigating the Complexity of Bilingual Aphasia: Current Insights and Future Directions.Brain sciences · 2025Review
- Multimodal data fusion in neuroscience: promises, challenges and future directions.IEEE signal processing magazine · 2025Article
- Article
- Using unsupervised dimensionality reduction to identify lesion patterns predictive of post-stroke aphasia severity.Cortex; a journal devoted to the study of the nervous system and behavior · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.Perspectives of the ASHA special interest groups · 2025Article
- Article
- Charting the Course of Aphasia Recovery: Factors, Trajectories, and Outcomes.Annual review of linguistics · 2025Article
- Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.Stroke · 2025Article
- Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors.Frontiers in neuroimaging · 2025Article
- A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundPoststroke 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.
methodsFifty-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.
resultsThe 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,
conclusionsWhile 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.