Extracting the Multiscale Causal Backbone of Brain Dynamics

Gabriele D\textsc\char13Acunto, Francesco Bonchi, Gianmarco De Francisci Morales, Giovanni Petri
Proceedings of the Third Conference on Causal Learning and Reasoning, PMLR 236:265-295, 2024.

Abstract

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 individuals’ 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v236-d-textsc-char13acunto24a, title = {Extracting the Multiscale Causal Backbone of Brain Dynamics}, author = {D\textsc{\char13}Acunto, Gabriele and Bonchi, Francesco and Morales, Gianmarco De Francisci and Petri, Giovanni}, booktitle = {Proceedings of the Third Conference on Causal Learning and Reasoning}, pages = {265--295}, year = {2024}, editor = {Locatello, Francesco and Didelez, Vanessa}, volume = {236}, series = {Proceedings of Machine Learning Research}, month = {01--03 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v236/d-textsc-char13acunto24a/d-textsc-char13acunto24a.pdf}, url = {https://proceedings.mlr.press/v236/d-textsc-char13acunto24a.html}, abstract = {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 individuals’ 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.} }
Endnote
%0 Conference Paper %T Extracting the Multiscale Causal Backbone of Brain Dynamics %A Gabriele D\textsc\char13Acunto %A Francesco Bonchi %A Gianmarco De Francisci Morales %A Giovanni Petri %B Proceedings of the Third Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2024 %E Francesco Locatello %E Vanessa Didelez %F pmlr-v236-d-textsc-char13acunto24a %I PMLR %P 265--295 %U https://proceedings.mlr.press/v236/d-textsc-char13acunto24a.html %V 236 %X 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 individuals’ 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.
APA
D\textsc\char13Acunto, G., Bonchi, F., Morales, G.D.F. & Petri, G.. (2024). Extracting the Multiscale Causal Backbone of Brain Dynamics. Proceedings of the Third Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 236:265-295 Available from https://proceedings.mlr.press/v236/d-textsc-char13acunto24a.html.

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