研究大脑连接组的性别差异，发现默认模式网络破坏影响最大。
Sex-based Network-Specific Differences in Connectomes: A Krakencoder-Based Analysis
- 用克雷克编码器模拟移除不同脑网络，分析结构与功能连接的相互影响。
- 移除默认模式网络导致预测偏差最大，性別判别准确率最高达84.76%。
- 完整连接组比受损预测保留更多性别特征，适合神经科学与脑疾病研究者。
本研究利用克雷克编码器作为仿真框架，探究一种脑连接组模态的缺陷如何传播至另一种。基于人类连接组计划中702名健康参与者的数据，分别分析了结构与功能连接组，逐个评估Yeo-7功能网络移除的影响。共设七种情景，每次仅移除一个网络，其余保持完整。通过三种互补指标量化跨模态预测的扰动：特征值谱的KL散度、Frobenius范数和Wasserstein距离。同时评估预测连接组中性别信息的保留程度。在所有指标及两个预测方向中，默认模式网络引发的扰动最大，而躯体运动网络最小。网络级扰动签名中的性别差异较微弱，最佳判别准确率为66.09%（基于网络移除条件预测）。相比之下，完整输入下预测的连接组性别分类准确率显著更高，最高达84.76%。结果表明，完整预测连接组包含远多于单一扰动签名的性别判别信息。
原文摘要 · Abstract (English)
This study examines how deficiencies in one brain connectome modality propagate to the other, using the Krakencoder as a simulation framework. Structural and functional connectomes from 702 healthy participants in the Human Connectome Project were analyzed, with the impact of each of the Yeo-7 functional networks assessed separately. Seven scenarios were considered, each involving the removal of a single network while the remaining networks were preserved. The resulting perturbations in cross-modal predictions were quantified using three complementary metrics: KL divergence on eigenvalue spectra, Frobenius norm, and Wasserstein distance. In addition, the persistence of sex-specific information within the predicted connectomes was evaluated. Across all metrics and both prediction directions, the Default Mode Network produced the largest perturbations, whereas the Somatomotor network yielded the smallest. Sex differences in network-level perturbation signatures were subtle, with the best result being an accuracy of 66.09% from connectomes predicted under network-removal conditions. In contrast, connectomes predicted from intact inputs achieved substantially higher sex classification accuracy, reaching up to 84.76%. These findings confirm that full predicted connectomes retain considerably more sex-discriminative information than perturbation-derived signatures alone.
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