Multimodal neural and behavioral data predict response to rehabilitation in chronic Poststroke aphasia
Open accessAbstract
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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Two studies (20, 29) used models to predict response to specific rehabilitation treatments to distinguish treatment responders from non-responders.
“two studies (20, 29) were then used to predict their response to specific rehabilitation treatments to distinguish treatment responders from non-responders.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [29]
The Methods (Model development and evaluation) classify participants as responders or nonresponders and describe models predicting those labels. But the Methods (Behavioral assessment and treatment) say the study examined response to overall language rehabilitation, “not to any treatment type in particular,” so this source does not support the claim’s specificity about treatments. (LLM, conf=0.90)
Experimental — accuracy not yet measuredIn study 30, the top 20 SVC and RF models all incorporated neuroimaging-based features, highlighting the importance of such data in achieving higher performance (F1 scores of 0.941 for SVC and 0.873 for RF).
“Their top 20 SVC and RF models all incorporated neuroimaging-based features, highlighting the importance of such data in achieving higher performance (F1 scores of 0.941 for SVC and 0.873 for RF).”
From Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence (2025) · cites this paper as [30]
The Results report the stated best F1 scores (0.941 for SVM and 0.873 for RF), and the best-performing models used neuroimaging features. They say all top 10 SVM models included structural and resting-state imaging data, but do not establish that every model in the top 20 for both algorithms included neuroimaging features. The paper calls the algorithm SVM, rather than SVC. (LLM, conf=0.86)
Experimental — accuracy not yet measuredWhen using a common predictor (WAB-R AQ), our models performed similarly to those in the most comparable study (30), with F1 scores of 0.842 for SVC and 0.771 for RF models.
“Next, it is notable that when using a common predictor (WAB-R AQ), our models performed similarly to those in the most comparable study30 (F1 scores of 0.842 for SVC and 0.771 for RF models).”
From Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence (2025) · cites this paper as [30]
The Methods section identifies AQ as the WAB-R measure of aphasia severity, and the Results report an F1 score of 0.842 for an SVM model using AQ alone. The passages from our copy do not report the claimed RF F1 score of 0.771; the RF scores stated in the Results are for other feature-set combinations. The citing authors’ comparison with their own models is their conclusion, not a result attributed to this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSeven included studies used multiple algorithms to construct models and compare them.
“Seven studies (19–21, 24, 27, 29, 31) used multiple algorithms to construct models and compare them.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [29]
Twelve included studies mainly used accuracy, AUC, F1-score, precision, recall, and other indicators to assess model performance.
“Twelve studies (19, 20, 22, 24, 25, 27–30, 33, 34, 37) mainly used accuracy, AUC, F1-score, precision, recall, and other indicators.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [29]
The support vector machine model constructed by Billot et al. (29) reached an accuracy of 92.7%.
“The accuracy of the support vector machine model constructed by Billot et al. (29) reached 92.7%.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [29]
Most models (20, 26, 29, 32) only completed internal validation.
“most models (20, 26, 29, 32) only completed internal validation”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [29]
Five studies (20, 26, 29, 32, 34) constructed models that were mainly used to predict language function or rehabilitation outcomes in patients.
“Five studies (20, 26, 29, 32, 34) constructed models that were mainly used to predict language function or rehabilitation outcomes in patients.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [29]
When predicting the potential response of a chronic aphasia patient to comprehensive rehabilitation, the F1 score reaches 0.941 (29).
“Similarly, when predicting the potential response of a chronic aphasia patient to comprehensive rehabilitation, the F1 score reaches 0.941 (29)”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [29]
In language function and rehabilitation outcome prediction, most studies remain in the performance validation stage and lack prospective clinical utility studies (20, 26, 29, 32, 34).
“In terms of language function and rehabilitation outcome prediction, most studies remain in the performance Validation stage and lack prospective clinical utility studies (20, 26, 29, 32, 34)”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [29]
Recent explorations into machine learning in speech-language pathology have shown strong predictive performance of treated language improvement using behavioral, demographic, and neuroimaging data.
“Recent explorations into ML in speech-language pathology have shown strong predictive performance of TLI using behavioral, demographic, and neuroimaging data30.”
From Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence (2025) · cites this paper as [30]
Studies in aphasia have shown that certain machine learning algorithms outperform others on outcome prediction.
“Studies in aphasia have shown that certain ML algorithms outperform others on outcome prediction30,31, suggesting the need to explore a variety of algorithms.”
From Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence (2025) · cites this paper as [30]
Our models' performance (F1 scores of 0.767 for TLI and 0.790 for CLG) approached that of study 30, despite their inclusion of neuroimaging data.
“Notably, our models’ performance (F1 scores of 0.767 for TLI and 0.790 for CLG) approached that of 30, despite their inclusion of neuroimaging data.”
From Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence (2025) · cites this paper as [30]
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“Next, it is notable that when using a common predictor (WAB-R AQ), our models performed similarly to those in the most comparable study 30 (F1 scores of 0.842 for SVC and 0.771 for random forest models).”
InfluentialSelf-citationAphasia severity prediction using a multi-modal machine learning approach
Hu, X., Varkanitsa, M., Kropp, E., Betke, M., Ishwar, P., & Kiran, S. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121300 · Cited by 8 · see citing works
Self-citationMočilnik, V., Rutar Gorišek, V., Sajovic, J., Pretnar Oblak, J., Drevenšek, G., & Rogelj, P. (2024) · Sensors · doi:10.3390/s24020329 · Cited by 8 · see citing works
Machine learning models in post-stroke aphasia: a scoping review
Li, X., Song, H., Guo, N., Kang, C., Gong, X., Ji, X., & Zheng, J. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1806856
“The research designs included seven prospective studies (20, 25, 27, 29, 31, 34, 35), 11 retrospective studies (19, 21, 23, 24, 26, 28, 30, 32, 33, 36, 37), and 1 mixed-methods study (22).”
Influential
- Authors
- Billot, A., Lai, S., Varkanitsa, M., Braun, E. J., Rapp, B., Parrish, T. B., Higgins, J., Kurani, A. S., Caplan, D., Thompson, C. K., Ishwar, P., Betke, M., & Kiran, S.
- Affiliations
- Boston University (US)
- Johns Hopkins University (US)
- Northwestern University (US)
- Harvard University (US)
- Corresponding author
- Billot, Anne
- ORCID
- Venue
- Stroke
- Published
- 2022-05
- Volume
- 53
- Issue
- 5
- Pages
- 1606-1614
- ISSN
- 0039-2499 (ISSN-L)
- 1524-4628
- PMC
- PMC9022691
- OpenAlex
- W4210310081
- Cited by
- 47
- References
- 69
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 12 · 2023: 6 · 2022: 3
- FWCI
- 4.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Stroke (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- OpenBU (Boston University) (publishedVersion, cc-by-nc-nd)
- Funders
- National Institute on Deafness and Other Communication Disorders (P50DC012283)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- post-stroke aphasia, chronic aphasia, language rehabilitation, treatment response prediction, resting-state functional connectivity, structural neuroimaging, aphasia severity, demographic factors, multimodal data integration, support vector machine, random forest
- MeSH terms
- Aphasia, Brain, Humans, Magnetic Resonance Imaging, Stroke, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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