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.

翻译：本研究利用Krakencoder作为模拟框架，探讨一个脑连接组模态的缺陷如何传播至另一模态。分析了来自人类连接组项目中702名健康参与者的结构性与功能性连接组，并分别评估了Yeo-7功能网络中每个网络的影响。共考虑了七种场景，每种场景涉及移除单一网络而保留其余网络。通过三种互补度量指标量化跨模态预测中的扰动：基于特征值谱的KL散度、弗罗贝尼乌斯范数和瓦瑟斯坦距离。此外，评估了预测连接组中性别特异性信息的持久性。在所有度量指标及两种预测方向上，默认模式网络产生的扰动最大，而躯体运动网络产生的扰动最小。网络层面扰动特征的性别差异较为微弱，最优结果是在网络移除条件下预测的连接组中获得的66.09%准确率。相比之下，基于完整输入预测的连接组实现了显著更高的性别分类准确率，最高达84.76%。这些发现证实，完整预测连接组比仅依赖扰动特征保留了更多性别判别信息。