Evidence map›Paper›PMID 31772339›Full record

ReviewNature reviews. Neurology2020

The neural and neurocomputational bases of recovery from post-stroke aphasia.

James D Stefaniak, Ajay D Halai, Matthew A Lambon Ralph

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 4 pooled it
9.0field-weighted citation impact, top 1% of its field
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

92 citing papers in PubMed, 4 syntheses or guidelines pooled it, 164 citations in OpenAlex.

  1. Pooled it
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  11. Review
  12. Observational
  13. Article
  14. Right hemisphere language network plasticity in aphasia.Brain : a journal of neurology · 2026
    Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. The nonlinear trajectory of post-stroke aphasia recovery.Frontiers in human neuroscience · 2026
    Review
  20. Article

32 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

3 authors at 1 institution in 1 country.

James D StefaniakDivision of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.ORCID http://orcid.org/0000-0002-1362-0538
Ajay D HalaiMRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0003-1725-7948
Matthew A Lambon RalphMRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK. matt.lambon-ralph@mrc-cbu.cam.ac.uk.ORCID http://orcid.org/0000-0001-5907-2488
MRC Cognition and Brain Sciences Unit · GB

Funding

Medical Research Council MC_UU_00005/18
6 · The paper itself

Abstract

Language impairment, or aphasia, is a disabling symptom that affects at least one third of individuals after stroke. Some affected individuals will spontaneously recover partial language function. However, despite a growing number of investigations, our understanding of how and why this recovery occurs is very limited. This Review proposes that existing hypotheses about language recovery after stroke can be conceptualized as specific examples of two fundamental principles. The first principle, degeneracy, dictates that different neural networks are able to adapt to perform similar cognitive functions, which would enable the brain to compensate for damage to any individual network. The second principle, variable neuro-displacement, dictates that there is spare capacity within or between neural networks, which, to save energy, is not used under standard levels of performance demand, but can be engaged under certain situations. These two principles are not mutually exclusive and might involve neural networks in both hemispheres. Most existing hypotheses are descriptive and lack a clear mechanistic account or concrete experimental evidence. Therefore, a better neurocomputational, mechanistic understanding of language recovery is required to inform research into new therapeutic interventions.

Indexed as

Neural Networks, ComputerAphasiaBrainHumansRecovery of FunctionStroke

Identifiers

PMID31772339
OpenAlexW2990952490

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.