Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis.

Gustavo Deco,Amit Naskar,Arpan Banerjee,Anirudh Vattikonda,Dipanjan Roy

doi:10.1162/netn_a_00197

Abstract

Previous computational models have related spontaneous resting-state brain activity with local excitatory–inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E–I balance govern resting-state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions, relate to functional brain activity is of critical importance. We propose a multiscale dynamic mean field (MDMF) model—a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modeled as population of MDMF and are connected by realistic connection topologies estimated from diffusion tensor imaging data. First, MDMF successfully predicts resting-state functional connectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level-dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from an MDMF model.

Highlights

Resting-state spontaneous activity (Mitra, Snyder, Hacker, & Raichle, 2014; Raichle, & Mintun, 2006; Rogers, Morgan, Newton, & Gore, 2007; Vincent et al, 2007) is presently well established as a substrate to understand normative brain patterns and intrinsic functional organization of macroscopic brain network dynamics
Our results show that optimal range of glutamate and gamma-aminobutyric acid (GABA) neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level-dependent signals
We propose a multiscale dynamic mean field (MDMF) model that incorporates biophysically realistic kinetic parameters of receptor binding in a dynamic mean field model and captures brain dynamics from the “whole brain.”

Summary

Introduction

Resting-state spontaneous activity (Mitra, Snyder, Hacker, & Raichle, 2014; Raichle, & Mintun, 2006; Rogers, Morgan, Newton, & Gore, 2007; Vincent et al, 2007) is presently well established as a substrate to understand normative brain patterns and intrinsic functional organization of macroscopic brain network dynamics. Numerous studies have attempted to understand the underlying mechanisms that govern the spatiotemporal dynamics of large-scale resting-state networks (Deco & Jirsa, 2012; Deco, Ponce-Alvarez, et al, 2014; Demirtas et al, 2017; see Cabral, Kringelbach, & Deco, 2017, for a review). All these studies predominantly utilized a class of dynamic mean field (DMF) models known as wholebrain computational models that have emerged as an important tool to link the healthy and pathological brain network dynamics with underlying change in excitation–inhibition (E–I) balance (Abeysuriya et al, 2018; Deco, Ponce-Alvarez, et al, 2014; Vattikonda, Surampudi, Banerjee, Deco, & Roy, 2016). Physiologically realistic whole-brain network models are favorable candidates to overcome and manipulate experimentally inaccessible parameter spaces of the system (Breakspear, 2017)

Methods

Results

Discussion

Conclusion