  Lateralization is a fundamental feature of the human brain, where sex
differences have been observed. Conventional studies in neuroscience on
sex-specific lateralization are typically conducted on univariate statistical
comparisons between male and female groups. However, these analyses often lack
effective validation of group specificity. Here, we formulate modeling sex
differences in lateralization of functional networks as a dual-classification
problem, consisting of first-order classification for left vs. right functional
networks and second-order classification for male vs. female models. To capture
sex-specific patterns, we develop the Group-Specific Discriminant Analysis
(GSDA) for first-order classification. The evaluation on two public
neuroimaging datasets demonstrates the efficacy of GSDA in learning
sex-specific models from functional networks, achieving a significant
improvement in group specificity over baseline methods. The major sex
differences are in the strength of lateralization and the interactions within
and between lobes. The GSDA-based method is generic in nature and can be
adapted to other group-specific analyses such as handedness-specific or
disease-specific analyses.
