  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.
