Global brain dynamics modulates local scale-free neuronal activity
Giovanni Rabuffo*1,2, Pietro Bozzo1, Marco Pompili1, Damien Depannemeacker1, Bach Nguyen2, Tomoki Fukai2, Pierpaolo Sorrentino1, Leonardo Dalla Porta3
1 Institut de Neurosciences des Systèmes (INS), Aix Marseille University, Marseille, France
2Okinawa Institute for Science and Technology (OIST), Okinawa, Japan
3Institute of Biomedical Investigations August Pi i Sunyer (IDIBAPS), Systems Neuroscience, Barcelona, Spain
*Email: giovanni.rabuffo@univ-amu.fr
Introduction
The brain's ability to balance stability and flexibility is thought to emerge from operating near a critical state [1]. In this work we address two major gaps of the “brain criticality hypothesis”:
First, local (between neurons) and global (between brain regions) criticality are often investigated independently, and a unifying framework is lacking.
Second, local neuronal populations do not maintain a strictly critical state but rather fluctuate around it [2]. The mechanisms underlying these fluctuations remain unclear.
To bridge these gaps, we introduce a connectome-based model that allows for a simultaneous assessment of local and global criticality (Fig.1). We demonstrate that long-range structural connectivity shapes global critical dynamics and drives the fluctuations of each brain region around a local critical state.
Methods
Decoupled brain regions are described by a mean-field model [3] which exhibits avalanche-like dynamics under stochastic input (Fig.1, Blue). Brain regions are connected via the Allen Mouse Connectome [4], and simulations are performed for different values of the global coupling parameter [5]. Simulated data consists of fast LFP, and slow BOLD signals (Fig.1, Red). The model results are validated against empirical datasets (Fig.1, Gray), including a mouse fMRI dataset [6] and LFP recordings from the Allen Neuropixel dataset [7]. To quantify the fluctuations around criticality, we identified neuronal avalanches as deviations of the local LFP signals below a fixed threshold (Fig.1, Blue) and measured sizes (area under curve) and durations (time to return within threshold). The magnitude of the fluctuations around criticality is assessed by analyzing the variance of the range of avalanche sizes across 2s-long epochs.
Results
For low global coupling, individual brain regions maintains local criticality (Fig.1, Blue) but remains globally desynchronized. Increasing coupling induces spontaneous long-range synchronization, paralleled by local fluctuations around criticality (Fig.1, Red). Notably, the working point where the simulations match the experiments corresponds to the regime with the largest range of avalanches sizes and durations (Fig.1, Grey). Strongly connected regions exhibit greater fluctuations around criticality, a testable prediction of the model. To verify this, we examined Allen Mouse Brain Atlas ROIs with LFP data and found a significant correlation between empirical critical fluctuations and regional structural connectivity properties (Fig.1, Green).
Discussion
Our results, comparing brain simulations and empirical datasets across scales, support the brain criticality hypothesis and suggest that criticality is not a static regime for a local neuronal population, but it is dynamically up- and down- regulated by large-scale interactions.
Figure 1. (Blue) Local neural mass model displays critical-like avalanche dynamics. (Red) Coupling brain regions via the empirical Allen structural connectivity we simulate fast LFP and slow BOLD global dynamics. (Gray) Simulated LFP displays global critical activity and simulated BOLD data matches fMRI experiments. (Green) The fluctuations around criticality correlate with structural in-strength.
Acknowledgements
We thank the Institut de Neurosciences des Systèmes (INS), Marseille, France, and the Okinawa Institute for Science and Technology, Japan for their generous support and sponsorship of this research. Their contributions have been instrumental in advancing our understanding of brain criticality and its implications.
References
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[2] Fontenele, A. J., et al. (2019) https://doi.org/10.1103/physrevlett.122.208101
[3] Buendía, V., et al., (2021) https://doi.org/10.1103/physrevresearch.3.023224
[4] Oh SW, et al. (2014) https://doi.org/10.1038/nature13186
[5] Melozzi F, et al. (2017) https://doi:10.1523/eneuro.0111-17.2017
[6] Grandjean, J., et al. (2023). https://doi.org/10.1038/s41593-023-01286-8
[7] https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html
