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A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

Authors :
Jennifer S. Goldman
Lionel Kusch
David Aquilue
Bahar Hazal Yalçınkaya
Damien Depannemaecker
Kevin Ancourt
Trang-Anh E. Nghiem
Viktor Jirsa
Alain Destexhe
Source :
Frontiers in Computational Neuroscience, Vol 16 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirically observed spontaneous and stimulus-evoked dynamics in space, time, phase, and frequency domains. Large-scale properties of cortical dynamics are shown to emerge from both microscopic-scale adaptation that control transitions between wake-like to sleep-like activity, and the organization of the human structural connectome; together, they shape the spatial extent of synchrony and phase coherence across brain regions consistent with the propagation of sleep-like spontaneous traveling waves at intermediate scales. Remarkably, the model also reproduces brain-wide, enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.

Details

Language :
English
ISSN :
16625188
Volume :
16
Database :
Directory of Open Access Journals
Journal :
Frontiers in Computational Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.595e9a937464489b3bb59a8c6442760
Document Type :
article
Full Text :
https://doi.org/10.3389/fncom.2022.1058957