Time-Resolved Smoothness of Distributed Brain Activity Tracks Conscious States and Unifies Emergent Neural Phenomena

Aditya Nanda,Dario Englot,Sneak Peek Administrator,Misha Ahrens,Yu Mu,Graham Johnson,Michael Breakspear,Catie Chang,Mikail Rubinov

doi:10.2139/ssrn.4025478

Abstract

Time-Resolved Smoothness of Distributed Brain Activity Tracks Conscious States and Unifies Emergent Neural Phenomena

Highlights

Coordinated neuronal interactions give rise to intricate patterns of distributed brain activity
We focused our analysis and modeling on recordings with intracranial EEG modalities, namely stereotactic EEG and electroencephalography
It shows that no model data, apart from smoothness model data, captured empirical time190 resolved smoothness. These observations imply that time-resolved smoothness reflects spatially nonspecific changes of brain activity, and that these changes are distinct from spatially specific network mechanisms. To further interpret these changes, we examined the dynamics of time-resolved smoothness in four electrocorticography recordings of macaque monkeys across consciousness and 195 propofol anesthesia (Nagasaka et al, 2011)

Summary

Introduction

Coordinated neuronal interactions give rise to intricate patterns of distributed brain activity. Studies often seek to explain this structure in terms of emergent neural phenomena, spatiotemporal patterns of brain activity that reflect complex interactions of individual neu rons or brain regions. Many of these phenomena are situated within longstanding theoretical frameworks Dynamical phenomena show up as trajectories in phasespace embeddings of neuronal (Churchland et al, 2012; Kato et al, 2015) or regional (Shine 30 et al, 2019) recordings These embeddings provide a convenient geometric representation of distributed brain activity (Saxena & Cunningham, 2019). The smoothness of dynamical trajectories can endow dynamical activity with robustness to high-dimensional noise (Trautmann et al, 2019)

Methods

Results

Conclusion