Issue |
EPL
Volume 147, Number 1, July 2024
|
|
---|---|---|
Article Number | 17001 | |
Number of page(s) | 5 | |
Section | Biological and soft matter physics | |
DOI | https://doi.org/10.1209/0295-5075/ad5468 | |
Published online | 07 August 2024 |
Model-based machine learning of critical brain dynamics
1 Physics Department, University of Buenos Aires, and Buenos Aires Physics Institute - Buenos Aires, Argentina
2 Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez - Santiago, Chile
Received: 16 November 2023
Accepted: 5 June 2024
Criticality can be exactly demonstrated in certain models of brain activity, yet it remains challenging to identify in empirical data. We trained a fully connected deep neural network to learn the phases of an excitable model unfolding on the anatomical connectome of human brain. This network was then applied to brain-wide fMRI data acquired during the descent from wakefulness to deep sleep. We report high correlation between the predicted proximity to the critical point and the exponents of cluster size distributions, indicative of subcritical dynamics. This result demonstrates that conceptual models can be leveraged to identify the dynamical regime of real neural systems.
© 2024 EPLA
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