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 fit 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 the existing extensive research in brain connectivity fingerprinting from a causal perspective.

翻译：脑连接研究的大部分工作集中在脑区之间的统计关联上，这些关联并不直接涉及支配脑动力学的因果机制。本文提出了脑动力学的多尺度因果骨架（MCB），该骨架由一组个体在多个时间尺度上共享，并设计了一种原理性方法来提取它。我们的方法利用了多尺度因果结构学习的最新进展，并优化了模型拟合与其复杂性之间的权衡。对合成数据的实证评估表明，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，我们发现左右脑半球均存在稀疏的MCB。由于其多尺度特性，我们的方法显示在低频带中，因果动力学由与高级认知功能相关的脑区驱动；而在较高频带中，与感觉处理相关的节点则起着关键作用。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，从而从因果角度支持了现有广泛的脑连接指纹研究。