Assessing Language Lateralization through Gray Matter Volume: Implications for Preoperative Planning in Brain Tumor Surgery.

brain tumor gray matter volume language lateralization regression analysis

Journal

Brain sciences
ISSN: 2076-3425
Titre abrégé: Brain Sci
Pays: Switzerland
ID NLM: 101598646

Informations de publication

Date de publication:
24 Sep 2024
Historique:
received: 09 08 2024
revised: 15 09 2024
accepted: 16 09 2024
medline: 25 10 2024
pubmed: 25 10 2024
entrez: 25 10 2024
Statut: epublish

Résumé

Functional MRI (fMRI) is widely used to assess language lateralization, but its application in patients with brain tumors can be hindered by cognitive impairments, compensatory neuroplasticity, and artifacts due to patient movement or severe aphasia. Gray matter volume (GMV) analysis via voxel-based morphometry (VBM) in language-related brain regions may offer a stable complementary approach. This study investigates the relationship between GMV and fMRI-derived language lateralization in healthy individuals and patients with left-hemisphere brain tumors, aiming to enhance accuracy in complex cases. The MRI data from 22 healthy participants and 28 individuals with left-hemisphere brain tumors were analyzed. Structural T1-weighted and functional images were obtained during three language tasks. Language lateralization was assessed based on activation in predefined regions of interest (ROIs), categorized as typical (left) or atypical (right or bilateral). The GMV in these ROIs was measured using VBM. Linear regressions explored GMV-lateralization associations, and logistic regressions predicted the lateralization based on the GMV. In the healthy participants, typical left-hemispheric language dominance correlated with higher GMV in the left pars opercularis of the inferior frontal gyrus. The brain tumor participants with atypical lateralization showed increased GMV in six right-hemisphere ROIs. The GMV in the language ROIs predicted the fMRI language lateralization, with AUCs from 80.1% to 94.2% in the healthy participants and 78.3% to 92.6% in the tumor patients. GMV analysis in language-related ROIs effectively complements fMRI for assessing language dominance, particularly when fMRI is challenging. It correlates with language lateralization in both healthy individuals and brain tumor patients, highlighting its potential in preoperative language mapping. Further research with larger samples is needed to refine its clinical utility.

Sections du résumé

BACKGROUND/OBJECTIVES OBJECTIVE
Functional MRI (fMRI) is widely used to assess language lateralization, but its application in patients with brain tumors can be hindered by cognitive impairments, compensatory neuroplasticity, and artifacts due to patient movement or severe aphasia. Gray matter volume (GMV) analysis via voxel-based morphometry (VBM) in language-related brain regions may offer a stable complementary approach. This study investigates the relationship between GMV and fMRI-derived language lateralization in healthy individuals and patients with left-hemisphere brain tumors, aiming to enhance accuracy in complex cases.
METHODS METHODS
The MRI data from 22 healthy participants and 28 individuals with left-hemisphere brain tumors were analyzed. Structural T1-weighted and functional images were obtained during three language tasks. Language lateralization was assessed based on activation in predefined regions of interest (ROIs), categorized as typical (left) or atypical (right or bilateral). The GMV in these ROIs was measured using VBM. Linear regressions explored GMV-lateralization associations, and logistic regressions predicted the lateralization based on the GMV.
RESULTS RESULTS
In the healthy participants, typical left-hemispheric language dominance correlated with higher GMV in the left pars opercularis of the inferior frontal gyrus. The brain tumor participants with atypical lateralization showed increased GMV in six right-hemisphere ROIs. The GMV in the language ROIs predicted the fMRI language lateralization, with AUCs from 80.1% to 94.2% in the healthy participants and 78.3% to 92.6% in the tumor patients.
CONCLUSIONS CONCLUSIONS
GMV analysis in language-related ROIs effectively complements fMRI for assessing language dominance, particularly when fMRI is challenging. It correlates with language lateralization in both healthy individuals and brain tumor patients, highlighting its potential in preoperative language mapping. Further research with larger samples is needed to refine its clinical utility.

Identifiants

pubmed: 39451969
pii: brainsci14100954
doi: 10.3390/brainsci14100954
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Agencia Nacional de Investigación y Desarrollo
ID : ICN2021_004
Organisme : Agencia Nacional de Investigación y Desarrollo
ID : ACT210083
Organisme : Fondo Nacional de Desarrollo Científico y Tecnológico
ID : N° 111150429
Organisme : Pontificia Universidad Catolica de Chile
ID : PUENTE VRI 2022-4

Auteurs

Daniel Solomons (D)

Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.
Millennium Institute for Intelligent Healthcare Engineering-iHEALTH, Santiago 7820436, Chile.

Maria Rodriguez-Fernandez (M)

Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.
Millennium Institute for Intelligent Healthcare Engineering-iHEALTH, Santiago 7820436, Chile.

Francisco Mery-Muñoz (F)

Department of Neurosurgery, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.

Leonardo Arraño-Carrasco (L)

Department of Radiology, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.

Francisco Sahli Costabal (FS)

Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.
Millennium Institute for Intelligent Healthcare Engineering-iHEALTH, Santiago 7820436, Chile.
Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.

Carolina Mendez-Orellana (C)

Speech and Language Pathology Department, Health Sciences School, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.

Classifications MeSH