Characterizing sleep stages through the complexity-entropy plane in human intracranial data and in a whole-brain model

TL;DR

This study uses intracranial recordings and a complexity-entropy analysis to distinguish sleep stages, revealing distinct brain dynamics and demonstrating the effectiveness of a computational model and machine learning classification.

Contribution

It introduces a novel application of the complexity-entropy plane to intracranial data for sleep stage characterization and validates a whole-brain model that reproduces empirical findings.

Findings

Distinct regions in the complexity-entropy plane for each sleep stage

Whole-brain model replicates empirical complexity-entropy patterns

High accuracy classification of sleep stages using SVM

Abstract

Characterizing the brain dynamics during different cortical states can reveal valuable information about its patterns across various cognitive processes. In particular, studying the differences between awake and sleep stages can shed light on the understanding of brain processes essential for physical and mental well-being, such as memory consolidation, information processing, and fatigue recovery. Alterations in these patterns may indicate disorders and pathologies such as obstructive sleep apnea, narcolepsy, as well as Alzheimer's and Parkinson's diseases. Here, we analyze time series obtained from intracranial recordings of 106 patients, covering four sleep stages: Wake, N2, N3, and REM. Intracranial electroencephalography (iEEG), which can include electrocorticography (ECoG) and depth recordings, represents the state-of-the-art measurements of brain activity, offering unparalleled spatial and temporal resolution for investigating neural dynamics. We characterize the signals using Bandt and Pompe symbolic methodology to calculate the Weighted Permutation Entropy (WPE) and the Statistical Complexity Measure (SCM) based on the Jensen and Shannon disequilibrium. By mapping the data onto the complexity-entropy plane, we observe that each stage occupies a distinct region, revealing its own dynamic signature. We show that our empirical results can be reproduced by a whole-brain computational model, in which each cortical region is described by a mean-field formulation based on networks of Adaptive Exponential Integrate-and-Fire (AdEx) neurons, adjusting the adaptation parameter to simulate the different sleep stages. Finally, we show that a classification approach using Support Vector Machine (SVM) provides high accuracy in distinguishing between cortical states.