Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics

Hilgo Bruining,Klaus Linkenkaer-Hansen,Erika L Juarez-Martinez,Huibert D Mansvelder,J Matias Palva,Bob Oranje,Eva Dallares,Jan Sprengers,Arthur-Ervin Avramiea,Richard Hardstone,Sonja Simpraga,Satu Palva,Simon J Houtman,Simon-Shlomo Poil

doi:10.1038/s41598-020-65500-4

Abstract

Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network’s activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.

Highlights

The theory of critical brain dynamics links E/I balance to the scale-free statistical character of network activity[18,19,20,21,22,23,24,25]
We applied detrended fluctuation analysis (DFA) to the amplitude envelope of the alpha oscillations to obtain the DFA exponent, β, which is a measure of long-range temporal correlations (LRTC) and widely used as an index of critical oscillation dynamics[21,27]
We have introduced a measure of functional E/I ratio from network activity that is sensitive to both changes in synaptic functioning and network connectivity and which is applicable to non-invasive human EEG recordings[3,8]

Summary

Introduction

The theory of critical brain dynamics links E/I balance to the scale-free statistical character of network activity[18,19,20,21,22,23,24,25]. Neuronal networks exhibit scale-free spatial and long-range temporal correlations of activity patterns when they operate near the critical point, poised between a low-activity sub-critical phase—which occurs when there is excessive net inhibition—and a relentlessly active super-critical phase associated with excessive net excitation[18,26]. It is plausible that combining two properties of neuronal network activity—the spectral power and long-range temporal correlations—can lead to an estimate of E/I ratio.

Methods

Results

Conclusion