Brain health imaging markers, post-stroke aphasia and Cognition: A scoping review.


Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
2023
Historique:
received: 30 01 2023
revised: 18 07 2023
accepted: 19 07 2023
medline: 18 9 2023
pubmed: 4 8 2023
entrez: 3 8 2023
Statut: ppublish

Résumé

For the past decade, brain health has been an emerging line of scientific inquiry assessing the impact of age-related neurostructural changes on cognitive decline and recovery from brain injury. Typically, compromised brain health is attributed to the presence of small vessel disease (SVD) and brain tissue atrophy, which are represented by various neuroimaging features. However, to date, the relationship between brain health markers and chronic aphasia severity remains unclear. Thus, the goal of this scoping review was to assess the current body of evidence regarding the relationship between SVD-related brain health biomarkers and post-stroke aphasia and cognition. In all, 187 articles were identified from 3 databases, of which 16 articles met the criteria for inclusion. Among these studies, 11 focused on cognition rather than aphasia, while 2 investigated both. Of the 10 studies that used white matter hyperintensities (WMHs) as an indicator of SVD severity, 8 studies (80%) demonstrated a relationship between WMH load and worse cognition in stroke patients. Interestingly, among the studies that specifically investigated aphasia, all 5 studies (100%) demonstrated a relationship between SVD and worse language performance. They also indicated that factors other than brain health (e.g., lesion, age, time post onset) played an important role in determining aphasia severity at a single timepoint. These findings suggest that brain health is likely a crucial factor in the context of aphasia recovery, possibly indicating the necessity of cognitive reserve thresholds for the multimodal cognitive demands associated with language recovery. While SVD and structural brain health are not commonly considered as predictors of aphasia severity, more comprehensive models incorporating brain health have the potential to improve prognosis of post-stroke cognitive and language deficits. Given the variability in the existing literature, a uniform grading system for overall SVD would be beneficial for future research on the mechanisms related to brain networks and neuroplasticity, and their translational impact.

Identifiants

pubmed: 37536153
pii: S2213-1582(23)00171-7
doi: 10.1016/j.nicl.2023.103480
pmc: PMC10412866
pii:
doi:

Types de publication

Journal Article Review Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

103480

Subventions

Organisme : NIDCD NIH HHS
ID : P50 DC014664
Pays : United States

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Jade Hannan (J)

Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA. Electronic address: jhannan@email.sc.edu.

Janina Wilmskoetter (J)

Department of Health and Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA.

Julius Fridriksson (J)

Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA.

Argye E Hillis (AE)

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Physical Medicine, Rehabilitation, and Cognitive Science, Johns Hopkins University, Baltimore, MD, USA.

Leonardo Bonilha (L)

Department of Neurology, Emory University, Atlanta, GA, USA.

Natalie Busby (N)

Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA.

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Classifications MeSH