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

Area of Science:

• Neuroimaging
• Neuroscience
• Radiology

Background:

• Functional MRI (fMRI) is standard for language lateralization but faces challenges in brain tumor patients.
• Cognitive impairments, neuroplasticity, and artifacts can limit fMRI accuracy.
• Gray matter volume (GMV) analysis offers a stable, complementary approach using voxel-based morphometry (VBM).

Purpose of the Study:

• To investigate the relationship between GMV and fMRI-derived language lateralization.
• To assess the utility of GMV analysis in healthy individuals and left-hemisphere brain tumor patients.
• To enhance the accuracy of preoperative language mapping.

Main Methods:

• Analyzed MRI data from 22 healthy participants and 28 brain tumor patients.
• Assessed language lateralization using fMRI during language tasks.
• Measured GMV in language regions using VBM and analyzed associations with lateralization.

Main Results:

• Healthy participants: Left-hemispheric dominance correlated with higher left pars opercularis GMV.
• Tumor patients: Atypical lateralization linked to increased right-hemisphere GMV.
• GMV accurately predicted fMRI language lateralization (AUCs 78.3%–94.2%).

Conclusions:

• GMV analysis in language regions is a valuable adjunct to fMRI for language dominance assessment.
• This method shows potential for preoperative language mapping, especially when fMRI is compromised.
• Further research with larger cohorts is recommended to confirm clinical utility.