Aphasia severity prediction using a multi-modal machine learning approach
Open accessAbstract
The present study examined an integrated multiple neuroimaging modality (T1 structural, Diffusion Tensor Imaging (DTI), and resting-state FMRI (rsFMRI)) to predict aphasia severity using Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) in 76 individuals with post-stroke aphasia. We employed Support Vector Regression (SVR) and Random Forest (RF) models with supervised feature selection and a stacked feature prediction approach. The SVR model outperformed RF, achieving an average root mean square error (RMSE) of 16.38±5.57, Pearson's correlation coefficient (r) of 0.70±0.13, and mean absolute error (MAE) of 12.67±3.27, compared to RF's RMSE of 18.41±4.34, r of 0.66±0.15, and MAE of 14.64±3.04. Resting-state neural activity and structural integrity emerged as crucial predictors of aphasia severity, appearing in the top 20% of predictor combinations for both SVR and RF. Finally, the feature selection method revealed that functional connectivity in both hemispheres and between homologous language areas is critical for predicting language outcomes in patients with aphasia. The statistically significant difference in performance between the model using only single modality and the optimal multi-modal SVR/RF model (which included both resting-state connectivity and structural information) underscores that aphasia severity is influenced by factors beyond lesion location and volume. These findings suggest that integrating multiple neuroimaging modalities enhances the prediction of language outcomes in aphasia beyond lesion characteristics alone, offering insights that could inform personalized rehabilitation strategies.
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The machine learning algorithms used in post-stroke aphasia models were mainly supervised methods, including random forests, neural networks, and support vector machines.
“The analysis results showed that the machine learning algorithms used in post-stroke aphasia models were mainly supervised methods, including random forests, neural networks, and support vector machines.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
The Introduction and Materials and Methods (Model Development; page not provided) describe random forest and support vector regression models for post-stroke aphasia severity prediction, with supervised feature selection. The paper does not establish that post-stroke aphasia models mainly use supervised methods or identify neural networks as one of the methods used. (LLM, conf=0.87)
Experimental — accuracy not yet measuredFive studies (21, 23, 24, 28, 36) constructed machine learning models for predicting the severity of aphasia by integrating multi-dimensional features such as neuroimaging and language samples.
“Five studies (21, 23, 24, 28, 36) constructed machine learning models for predicting the severity of aphasia by integrating multi-dimensional features such as neuroimaging and language samples.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
The Abstract and Materials and Methods (Modality Predictors; page numbers unavailable) describe machine-learning models that combine structural MRI, DTI, and resting-state fMRI features to predict aphasia severity. They do not describe language samples as input features; the Western Aphasia Battery-Revised Aphasia Quotient is the outcome being predicted. This source supports one of the five cited studies, not the count of five. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn severity assessment and prediction, most models rely on single-center, small-sample data (21, 23, 24, 28, 36).
“In terms of severity assessment and prediction, most models rely on single-center, small-sample data (21, 23, 24, 28, 36)”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
The Introduction (page unavailable) says prior predictive studies face small sample sizes relative to their number of predictors, supporting the small-sample part of the claim. It does not say that most severity models use single-center data; the Materials and Methods (page unavailable) instead describe this paper’s own recruitment from three universities. (LLM, conf=0.87)
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 [21]
Experimental — accuracy not yet measuredAmong the included studies, 18 employed supervised machine learning algorithms.
“Among the included studies, 18 (19–29, 31–37) employed supervised machine learning algorithms, including support vector machines, random forests, decision trees, logistic regression, and various neural networks (such as feedforward neural networks, convolutional neural networks, and recurrent neural networks).”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
Seven included studies primarily relied on error metrics, such as root mean square error and mean absolute error, combined with indicators such as the Pearson correlation coefficient and coefficient of determination.
“Seven studies (21, 23, 26, 31, 32, 35, 36) primarily relied on error metrics, such as root mean square error and mean absolute error, combined with indicators, such as the Pearson correlation coefficient and coefficient of determination, to provide a more comprehensive assessment.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
Four studies (21, 23, 24, 36) focused on fine prediction in the chronic phase.
“four studies (21, 23, 24, 36) focused on fine prediction in the chronic phase”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
Seven studies (19-21, 24, 27, 29, 31) used a variety of algorithms to build models and compare them in order to obtain the best model.
“seven studies (19–21, 24, 27, 29, 31) used a variety of algorithms to build models and compare them in order to obtain the best model”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
In this review, 18 studies used supervised machine learning algorithms, and 1 study used unsupervised machine learning algorithms.
“In this study, 18 studies (19–29, 31–37) used supervised machine learning algorithms, and 1 study (30) used unsupervised machine learning algorithms”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
Hu et al. (21) recommended support vector regression.
“Hu et al. (21) and Teghipco et al. (24) recommended the use of support vector regression and a convolutional neural network, respectively.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
A total of 19 articles were included in the review.
“A total of 19 articles were included.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [21]
The Materials and Methods passages (Cohort 1 and Modality Predictors; page unavailable) describe imaging data and predictors, not a review selecting articles. A search for “19 articles” and review inclusion found no passage in our copy supporting that count. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Hu, X., Varkanitsa, M., Kropp, E., Betke, M., Ishwar, P., & Kiran, S.
- Affiliations
- Boston University (US)
- Corresponding author
- Hu, Xinyi
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 317
- Pages
- 121300
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC12261925
- OpenAlex
- W4411369589
- Cited by
- 8
- References
- 66
- Citations by year
- 2026: 5 · 2025: 3
- FWCI
- 4.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute on Deafness and Other Communication Disorders (P50DC012283, R01DC016950)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- post-stroke aphasia, multimodal neuroimaging, diffusion tensor imaging, resting-state functional connectivity, support vector regression, random forest, feature selection, language outcome prediction, stroke rehabilitation
- MeSH terms
- Machine Learning, Adult, Aged, Aphasia, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Severity of Illness Index, Stroke, Diffusion Tensor Imaging, Neuroimaging, Multimodal Imaging
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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