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.

提取多尺度因果骨架与大脑连接在因果机制、多尺度因果结构学习、休息状态脑成像数据以及大脑连接的因果指纹等方面的关键词，并使用多尺度因果结构验证了大脑连接的因果指纹，从而支持现有关于大脑连接指纹的广泛研究。