Evidence map›Paper›PMID 40519564›Full record

ArticleFrontiers in neuroimaging2025

Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors.

Deepa Tilwani, Christian O'Reilly, Nicholas Riccardi, Valerie L Shalin, Dirk-Bart den Ouden, Julius Fridriksson, Svetlana V Shinkareva, Amit P Sheth, Rutvik H Desai

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In one paragraph

Article in Frontiers in neuroimaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Deepa TilwaniArtificial Intelligence Institute, University of South Carolina, Columbia, SC, United States.
Christian O'ReillyArtificial Intelligence Institute, University of South Carolina, Columbia, SC, United States.
Nicholas RiccardiDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United States.
Valerie L ShalinArtificial Intelligence Institute, University of South Carolina, Columbia, SC, United States.
Dirk-Bart den OudenDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United States.
Julius FridrikssonDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United States.
Svetlana V ShinkarevaInstitute for Mind and Brain, University of South Carolina, Columbia, SC, United States.
Amit P ShethArtificial Intelligence Institute, University of South Carolina, Columbia, SC, United States.
Rutvik H DesaiInstitute for Mind and Brain, University of South Carolina, Columbia, SC, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (

Indexed as

aphasialesion-symptom mappingmachine learningmultivariate analysisneuroimagingstroke

Identifiers

PMID40519564
PMCPMC12163030

What OpenQuestion holds

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Registered trials

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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.