Evidence map›Paper›PMID 24179796›Full record

ArticleNeuroImage. Clinical2013

Predicting outcome and recovery after stroke with lesions extracted from MRI images.

Thomas M H Hope, Mohamed L Seghier, Alex P Leff, Cathy J Price

Abstract read
In one paragraph

Article in NeuroImage. Clinical, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers.

0numbers the graph read from it
0cells of the map it votes in
137citing 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

137 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Article
  5. Review
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  7. Article
  8. Digital Twin Model of Treatment Outcomes in Post-Stroke Aphasia.medRxiv : the preprint server for health sciences · 2026
    Article
  9. Review
  10. Article
  11. Cognitive Functions Supporting Learning Over Time in Naming Treatment for Aphasia.Journal of speech, language, and hearing research : JSLHR · 2025
    Article
  12. Review
  13. Article
  14. Article
  15. Precision-Optimised Post-Stroke Prognoses.Annals of clinical and translational neurology · 2025
    Article
  16. Article
  17. Current Clinical Applications of Structural MRI in Neurological Disorders.Journal of clinical neurology (Seoul, Korea) · 2025
    Review
  18. 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 · 2025
    Article
  19. Article
  20. Article

77 more citing papers are in PubMed but not listed here.

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

4 authors.

Thomas M H HopeWellcome Trust Centre for Neuroimaging, University College London, UK.
Mohamed L Seghier
Alex P Leff
Cathy J Price

Funding

Wellcome Trust 091593Wellcome Trust 097720
6 · The paper itself

Abstract

Here, we present and validate a method that lets us predict the severity of cognitive impairments after stroke, and the likely course of recovery over time. Our approach employs (a) a database that records the behavioural scores from a large population of patients who have, collectively, incurred a comprehensive range of focal brain lesions, (b) an automated procedure to convert structural brain scans from those patients into three-dimensional images of their lesions, and (c) a system to learn the relationship between patients' lesions, demographics and behavioural capacities at different times post-stroke. Validation against data collected from 270 stroke patients suggests that our first set of variables yielded predictions that match or exceed the predictive power reported in any comparable work in the available literature. Predictions are likely to improve when other determinants of recovery are included in the system. Many behavioural outcomes after stroke could be predicted using the proposed approach.

Indexed as

AphasiaGaussian processesMachine learningRecoverySpeech productionStroke

Identifiers

PMID24179796
PMCPMC3778268

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.