The study aims to identify sex differences in the lateralization of brain functional networks using a machine learning approach. The key highlights are:
Formulation of sex differences in brain lateralization as a dual-classification problem:
Development of a novel Group-Specific Discriminant Analysis (GSDA) algorithm for the first-order classification, which can effectively capture sex-specific patterns in brain lateralization.
Evaluation of the GSDA-based method on two public neuroimaging datasets (HCP and GSP), demonstrating significant improvement in group specificity over baseline methods.
Identification of sex differences in two aspects of brain lateralization:
The GSDA-based approach is generic and can be adapted to other group-specific analyses, such as handedness-specific or disease-specific analyses.
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by Shuo Zhou,Ju... at arxiv.org 04-10-2024
https://arxiv.org/pdf/2404.05781.pdfDeeper Inquiries