The bulk of the research effort on brain connectivity revolves around statistical associations among brain regions, which do not directly relate to the causal mechanisms governing brain dynamics. Here we propose the multiscale causal backbone (MCB) of brain dynamics shared by a set of individuals across multiple temporal scales, and devise a principled methodology to extract it. Our approach leverages recent advances in multiscale causal structure learning and optimizes the trade-off between the model fitting and its complexity. Empirical assessment on synthetic data shows the superiority of our methodology over a baseline based on canonical functional connectivity networks. When applied to resting-state fMRI data, we find sparse MCBs for both the left and right brain hemispheres. Thanks to its multiscale nature, our approach shows that at low-frequency bands, causal dynamics are driven by brain regions associated with high-level cognitive functions; at higher frequencies instead, nodes related to sensory processing play a crucial role. Finally, our analysis of individual multiscale causal structures confirms the existence of a causal fingerprint of brain connectivity, thus supporting from a causal perspective the existing extensive research in brain connectivity fingerprinting.

翻译：关于脑连接的研究主要集中在脑区之间的统计关联上，但这并未直接涉及控制大脑动力学的因果机制。本文提出了跨多个时间尺度共享于一组个体的大脑动力学多尺度因果骨架（MCB），并设计了一种原则性方法来提取它。我们的方法利用了多尺度因果结构学习的最新进展，优化了模型拟合与其复杂度之间的权衡。在合成数据上的实证评估表明，我们的方法优于基于典型功能连接网络的基线方法。当应用于静息态fMRI数据时，我们发现了左右两个脑半球的稀疏MCB。得益于其多尺度特性，我们的方法显示：在低频段，因果动力学由与高级认知功能相关的脑区驱动；而在较高频率下，感觉处理相关的节点则发挥关键作用。最后，我们对个体多尺度因果结构的分析证实了脑连接因果指纹的存在，从而从因果视角支持了现有关于脑连接指纹的广泛研究。