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