Multiscale dynamic mean field (MDMF) model to relate resting state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis in health and disease.

The spontaneous neuronal activity of human brain shows highly structured spatio-temporal pattern, designated as resting state network. Previous neuro-computational studies have established the connection of spontaneous resting state activity with neuronal ensembles (using dynamic mean field approach) and showed the impact of local inhibitory-excitatory balance on this patterned spontaneous resting state activity of brain. But, the role of neurotransmitter dynamics on large-scale network organization remains unexplored. Empirically, this is beyond the scope of current neuroimaging techniques to monitor the neurotransmitter changes and capture BOLD dynamics simultaneously. We argue that large-scale computational models offer an attractive platform to test out the possible neural mechanisms that operate at multiple scales of physiological organization namely, brain metabolism and changes in synaptic concentrations of GABA (gamma-aminobutyric acid)/glutamate. We introduce a multiscale dynamic mean field (MDMF) model, that captures the synaptic gating dynamics as a function of neurotransmitter kinetics, we demonstrate how local homeostasis of excitatory-inhibitory neurotransmitter concentrations (GABA/glutamate) could modulate the large-scale resting state network dynamics of brain. Simulations were carried out across glutamate (0.1 to 15 mmol) and GABA (0.1 to 15 mmol) concentration regimes that span the parameter space reported from healthy and diseased brains. We have found for optimal configurations of glutamate concentration ranging from 5.6 to 15 mmol and GABA concentration from 0.9 to 15 mmol model predicted functional connectivity matrix show closest match with empirical functional connectivity matrix of normal resting brain. Graph theoretical measures of segregation (modularity, clustering coefficient and local efficiency) and integration (global efficiency and characteristic path length) in network information processing reported earlier in healthy and diseased brains were related to optimal GABA and glutamate concentrations and pathological values reported in epilepsy and schizophrenia respectively. In conclusion, the MDMF model could relate the various scales of observations from neurotransmitter concentrations to dynamics of synaptic gating to large-scale resting state network topology in healthy and in disordered states of the brain. Key words: GABA, glutamate, resting-state functional connectivity, structural connectivity, network measures, epilepsy and schizophrenia.