By Adrian Ponce Alvarez (Polytechnical University of Catalunya, Barcelona, Spain). November 8th, 2022.

Scale invariance is a characteristic of neural activity and how it emerges from neural interactions remains a fundamental question. Here, we studied the relation between scale-invariant brain dynamics and structural connectivity by analyzing human resting-state (rs-) fMRI signals, together with diffusion MRI (dMRI) connectivity and its approximation as an exponentially decaying function of the distance between brain regions. We analyzed the rs-fMRI dynamics using functional connectivity and a recently proposed phenomenological renormalization group (PRG) method that tracks the change of collective activity after successive coarse-graining at different scales. We found that brain dynamics display power-law scaling as a function of PRG coarse-graining. By studying a whole-brain computational models, we showed that the observed scaling features emerge from critical dynamics and connections exponentially decaying with distance. These results validate the PRG method using large-scale brain activity and theoretical models and suggests that scaling of rs-fMRI activity relates to criticality and the geometry of the brain.

Furthermore, we tested the method on whole-brain calcium imaging data (~50,000 neurons) acquired using selective-plane illumination microscopy (SPIM) in combination with transgenic GCaMP zebrafish larvae. We studied the scaling properties in two different conditions: during spontaneous activity and in the presence of mild electric shocks that induce a change in arousal state. We found power-law scaling as a function of coarse-graining and scaling exponents that were significantly different between spontaneous and stimulated conditions. These results suggest that the PRG method can be used to distinguish different states of neural systems.

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