Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Language networks in aphasia and health: a 1000 participant Activation Likelihood Estimate analysis

View ORCID ProfileJames D. Stefaniak, Reem S. W. Alyahya, View ORCID ProfileMatthew A. Lambon Ralph
doi: https://doi.org/10.1101/2020.06.30.179655
James D. Stefaniak
1Division of Neuroscience and Experimental Psychology, University of Manchester, Oxford Road, Manchester, UK
2MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for James D. Stefaniak
  • For correspondence: Matt.Lambon-Ralph@mrc-cbu.cam.ac.uk james.stefaniak@mrc-cbu.cam.ac.uk
Reem S. W. Alyahya
2MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
3King Fahad Medical City, Riyadh, Saudi Arabia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Matthew A. Lambon Ralph
2MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Matthew A. Lambon Ralph
  • For correspondence: Matt.Lambon-Ralph@mrc-cbu.cam.ac.uk james.stefaniak@mrc-cbu.cam.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Aphasia recovery post-stroke is classically and most commonly hypothesised to rely on regions that were not involved in language premorbidly, through ‘neurocomputational invasion’ or engagement of ‘quiescent homologues’. Contemporary accounts have suggested, instead, that recovery might be supported by under-utilised areas of the premorbid language network, which are downregulated in health to save neural resources (‘variable neurodisplacement’). Despite the importance of understanding the neural bases of language recovery clinically and theoretically, there is no consensus as to which specific regions are activated more consistently in post-stroke aphasia (PSA) than healthy individuals. Accordingly, we performed an Activation Likelihood Estimation analysis of language functional neuroimaging studies in PSA and linked control data. We obtained coordinate-based functional neuroimaging data for 481 individuals with aphasia following left hemisphere stroke (one third of which was previously unpublished) and for 530 healthy controls. Instead of the language network expanding by activating novel right hemisphere regions ‘de novo’ post-stroke, as would be predicted by neurocomputational invasion/quiescent homologue engagement mechanisms of recovery, we found that multiple regions throughout both hemispheres were consistently activated during language tasks in PSA and controls. Multiple undamaged regions were less consistently activated in PSA than controls, including domain-general regions of medial superior frontal cortex and right fronto-temporal cortex. In the reverse direction, the right anterior insula and inferior frontal gyrus were more consistently activated in PSA than controls, particularly for executively-demanding comprehension tasks. These regions overlap with control networks known to be recruited during difficult tasks in healthy individuals and were more consistently activated by patients during higher than lower demand tasks in this meta-analysis. Overall, these findings run counter to neurocomputational invasion of the language network into new territory or engagement of quiescent homologues. Instead, many parts of the pre-existing language network are less consistently activated in PSA, except for more consistent use of spare capacity within right hemisphere executive-control related regions (cf. variable neurodisplacement). This study provides novel insights into the language network changes that occur post-stroke. Such knowledge is essential if we are to design neurobiologically-informed therapeutic interventions to facilitate language recovery.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted July 01, 2020.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Language networks in aphasia and health: a 1000 participant Activation Likelihood Estimate analysis
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Language networks in aphasia and health: a 1000 participant Activation Likelihood Estimate analysis
James D. Stefaniak, Reem S. W. Alyahya, Matthew A. Lambon Ralph
bioRxiv 2020.06.30.179655; doi: https://doi.org/10.1101/2020.06.30.179655
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Language networks in aphasia and health: a 1000 participant Activation Likelihood Estimate analysis
James D. Stefaniak, Reem S. W. Alyahya, Matthew A. Lambon Ralph
bioRxiv 2020.06.30.179655; doi: https://doi.org/10.1101/2020.06.30.179655

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (2512)
  • Biochemistry (4955)
  • Bioengineering (3456)
  • Bioinformatics (15142)
  • Biophysics (6866)
  • Cancer Biology (5361)
  • Cell Biology (7692)
  • Clinical Trials (138)
  • Developmental Biology (4509)
  • Ecology (7116)
  • Epidemiology (2059)
  • Evolutionary Biology (10190)
  • Genetics (7494)
  • Genomics (9756)
  • Immunology (4807)
  • Microbiology (13152)
  • Molecular Biology (5112)
  • Neuroscience (29310)
  • Paleontology (203)
  • Pathology (833)
  • Pharmacology and Toxicology (1458)
  • Physiology (2122)
  • Plant Biology (4722)
  • Scientific Communication and Education (1004)
  • Synthetic Biology (1336)
  • Systems Biology (3996)
  • Zoology (766)