Extracting the Multiscale Causal Backbone of Brain Dynamics
This work addresses the challenge of moving beyond statistical associations to causal understanding in brain connectivity, which is incremental by applying existing causal structure learning to multiscale brain data.
The authors tackled the problem of identifying causal mechanisms in brain dynamics by proposing a multiscale causal backbone (MCB) and a method to extract it, showing superiority over baseline functional connectivity on synthetic data and revealing sparse MCBs in resting-state fMRI data with frequency-dependent roles of brain regions.
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.