ArticleNetwork neuroscience (Cambridge, Mass.)2021
Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 48 citations in OpenAlex.
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- Modelling brain metabolism with interacting nonautonomous phase oscillators.Frontiers in network physiology · 2026Article
- Local homeostasis preserves global neural dynamics compensating for structural loss during human lifespan aging.Communications biology · 2025Article
- Metastable dynamics emerge from local excitatory-inhibitory homeostasis in the cortex at rest.Network neuroscience (Cambridge, Mass.) · 2025Article
- Excitatory/Inhibitory balance in autism spectrum disorders: Integrating genetic, neurotransmitter and computational perspectives.AIMS neuroscience · 2025Review
- Excitatory-inhibitory homeostasis and bifurcation control in the Wilson-Cowan model of cortical dynamics.PLoS computational biology · 2025Article
- Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms.Translational psychiatry · 2024Review
- Metastability indexes global changes in the dynamic working point of the brain following brain stimulation.Frontiers in neurorobotics · 2024Article
- Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF.Network neuroscience (Cambridge, Mass.) · 2024Article
- A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics.PLoS computational biology · 2023Article
- From neurotransmitters to networks: Transcending organisational hierarchies with molecular-informed functional imaging.Neuroscience and biobehavioral reviews · 2023Review
- Multiscale effects of excitatory-inhibitory homeostasis in lesioned cortical networks: A computational study.PLoS computational biology · 2023Article
- Slope of the power spectral density flattens at low frequencies (<150 Hz) with healthy aging but also steepens at higher frequency (>200 Hz) in human electroencephalogram.Cerebral cortex communications · 2023Article
- Whole-brain dynamical modelling for classification of Parkinson's disease.Brain communications · 2023Article
- A perturbative approach to study information communication in brain networks.Network neuroscience (Cambridge, Mass.) · 2022Article
- Whole-Brain Network Models: From Physics to Bedside.Frontiers in computational neuroscience · 2022Review
- Excitatory-Inhibitory Homeostasis and Diaschisis: Tying the Local and Global Scales in the Post-stroke Cortex.Frontiers in systems neuroscience · 2021Review
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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