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

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• dc.contributor.author Naskar, Amit
• dc.contributor.author Vattikonda, Anirudh
• dc.contributor.author Deco, Gustavo
• dc.contributor.author Roy, Dipanjan
• dc.contributor.author Banerjee, Arpan
• dc.date.accessioned 2023-06-20T07:29:46Z
• dc.date.available 2023-06-20T07:29:46Z
• dc.date.issued 2021
• dc.description.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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Naskar A, Vattikonda A, Deco G, Roy D, Banerjee A. Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis. Netw Neurosci. 2021;5(3):757–82. DOI: 10.1162/netn_a_00197
• dc.identifier.doi http://dx.doi.org/10.1162/netn_a_00197
• dc.identifier.issn 2472-1751
• dc.identifier.uri http://hdl.handle.net/10230/57271
• dc.language.iso eng
• dc.publisher MIT Press
• dc.relation.ispartof Network Neuroscience. 2021;5(3):757-82.
• dc.rights © 2021 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword MDMF
• dc.subject.keyword Neurotransmitters
• dc.subject.keyword Metastability
• dc.subject.keyword Resting-state functional and structural connectivity
• dc.subject.keyword Network measures
• dc.subject.keyword Neurological disorders
• dc.title Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion