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.

通过第一级分类左右功能网络和第二级分类男女模型，我们开发了一种用于学习性别特定模型的Group-Specific Discriminant Analysis (GSDA) 方法，评估结果表明GSDA能在功能网络中学习性别特定模型，并在组特异性方面显著提高，主要性别差异在于侧化的强度和区域内部及区域间的相互作用。

特异性判别分析揭示大脑功能网络侧化中性别差异的统计验证