ArticleHuman brain mapping2021
Machine learning-based multimodal prediction of language outcomes in chronic aphasia.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Exploring the Effectiveness of Technology-Based Interventions in Aphasia Rehabilitation: A Systematic Review.International journal of language & communication disordersPooled it
- Predicting Outcomes of Language Rehabilitation: Prognostic Factors for Immediate and Long-Term Outcomes After Aphasia Therapy.Journal of speech, language, and hearing research : JSLHR · 2023Trial
- Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery.medRxiv : the preprint server for health sciences · 2026Article
- Multimodal AI Screening of Developmental Language Disorder in Tunisian Arabic Children: Clinical Markers and Computational Detection.Behavioral sciences (Basel, Switzerland) · 2026Article
- How artificial intelligence is shaping neuropsychology: A focus on cognitive assessment of neurodegenerative disorders.Journal of neuropsychology · 2026Review
- Diffusion Tensor Imaging and Advanced Diffusion Imaging in Post-Stroke Aphasia Recovery.Tomography (Ann Arbor, Mich.) · 2026Review
- 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
- Cross-Linguistic and Multicultural Effects on Animal Fluency Performance in Persons With Aphasia.American journal of speech-language pathology · 2025Article
- Naming practice effects and inconsistencies relate to treatment outcome in people with aphasia.Neuropsychologia · 2025Article
- Lesion-Symptom Mapping of Acute Speech Deficits After Left vs. Right Hemisphere Stroke: A Retrospective Analysis of NIHSS Best Language Scores and Clinical Neuroimaging.Brain sciences · 2025Article
- A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography.Human brain mapping · 2025Article
- Article
- 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
- Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.Stroke · 2025Article
- Charting the Course of Aphasia Recovery: Factors, Trajectories, and Outcomes.Annual review of linguistics · 2025Article
- Mediating role of brain aging in the effect of white matter hyperintensities on post-stroke aphasia severity.Frontiers in aging neuroscience · 2025Article
- Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors.Frontiers in neuroimaging · 2025Article
- Partial least squares multimodal analysis of brain network correlates of language deficits in aphasia.Brain communications · 2025Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following this line of work, the current study uses machine learning approaches to predict aphasia severity and specific language measures based on a multimodal neuroimaging dataset. A total of 116 individuals with chronic left-hemisphere stroke were included in the study. Neuroimaging data included task-based functional magnetic resonance imaging (fMRI), diffusion-based fractional anisotropy (FA)-values, cerebral blood flow (CBF), and lesion-load data. The Western Aphasia Battery was used to measure aphasia severity and specific language functions. As a primary analysis, we constructed support vector regression (SVR) models predicting language measures based on (i) each neuroimaging modality separately, (ii) lesion volume alone, and (iii) a combination of all modalities. Prediction accuracy across models was subsequently statistically compared. Prediction accuracy across modalities and language measures varied substantially (predicted vs. empirical correlation range: r = .00-.67). The multimodal prediction model yielded the most accurate prediction in all cases (r = .53-.67). Statistical superiority in favor of the multimodal model was achieved in 28/30 model comparisons (p-value range: <.001-.046). Our results indicate that different neuroimaging modalities carry complementary information that can be integrated to more accurately depict how brain damage and remaining functionality of intact brain tissue translate into language function in aphasia.
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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.