Group-specific discriminant analysis reveals statistically validated sex differences in lateralization of brain functional network
This work addresses the need for more effective validation of group-specific patterns in neuroscience, such as sex differences in brain lateralization, though it is incremental as it builds on existing classification approaches.
The paper tackled the problem of validating sex differences in brain lateralization by formulating it as a dual-classification problem and developing Group-Specific Discriminant Analysis (GSDA), achieving significant improvement in group specificity over baseline methods on two neuroimaging datasets.
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.