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

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 governs 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 multi-scale dynamic mean field model (MDMF) – 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 modelled as population of MDMF and are connected by realistic connection topologies estimated from Diffusion Tensor Imaging data. First, MDMF successfully predicts resting-state functionalconnectivity. 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 MDMF model.

High segregation and diminished global integration in large-scale brain functional networks enhances the perceptual binding of cross-modal stimuli

“…However, our study departs significantly from these earlier theoretical frameworks in providing a more neurobiologically grounded mechanism of multisensory integration based on the existence of balanced states of higher segregation and decreased global integration (Lord et al, 2017) employed by nodes of the PBN. In light of our current results and previous modeling studies (Abeysuriya et al, 2018;Naskar et al, 2021;Vattikonda et al, 2016), we propose that balanced states of functional integration and segregation may emerge from the underlying excitatory-inhibitory (E-I) balance in local neuronal populations of involved brain areas and may even be related to neurotransmitter homeostasis (Naskar et al, 2021;Shine et al, 2021). While spontaneous brain activity such as during resting state has been typically associated with the E-I balance (Abeysuriya et al, 2018;Naskar et al, 2021;Vattikonda et al, 2016), the potential of altered E-I balance-in guiding the formation of perceptual experience from interactions between inclement sensory signals with stored associative memories (Barron et al, 2017), as a catalyst for volitional control effectuating perceptual stability (Kondo et al, 2018), in guiding decision making strategies during multi-attribute choice (Pettine et al, 2021); all have been proposed recently.…”

Aperiodic and Periodic Components of Ongoing Oscillatory Brain Dynamics Link Distinct Functional Aspects of Cognition across Adult Lifespan

“…At a more fundamental level, 1/f scale reflects the self-similar temporal properties of the self-organized critical states. Although the aim of this study is not to resolve this debate, however, we argue that 1/f activity could arise from potentially number of factors, e.g., altered tissue properties or self-organized criticality and transient stability with aging and change in underlying excitation-inhibition (E/I) balance ( Bédard et al, 2006 ; Voytek et al, 2015 ; Gao et al, 2017 ; Naik et al, 2017 ; Naskar et al, 2021 ).…”

Excitatory-Inhibitory Homeostasis and Diaschisis: Tying the Local and Global Scales in the Post-stroke Cortex

“…Building on these foundations, further research demonstrated that the same homeostatic mechanisms rendered cortical networks more robust to changes in parameters such as transduction delays and global coupling strength ( Abeysuriya et al, 2018 ). Interestingly, in a recent study, using an updated model of coupled excitatory and inhibitory populations with a term that quantifies the concentration of excitatory and inhibitory neurotransmitters at a local level, it was found that there are optimal concentrations of glutamate and GABA that maximize fit of simulated and empirical FC ( Naskar et al, 2021 ). Not only that, but these suggested optimal concentrations also corresponded to a working point of activity where the brain is in a state of heightened metastability.…”