Abstract
Large-scale biophysical circuit models provide mechanistic insights into the micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. We developed a spatially heterogeneous large-scale dynamical circuit model that allowed for variation in local synaptic properties across the human cortex. Here we show that parameterizing local circuit properties with both anatomical and functional gradients generates more realistic static and dynamic resting-state functional connectivity (FC). Furthermore, empirical and simulated FC dynamics demonstrates remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. Time-varying regional fMRI amplitude may track multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggests that sensory-motor regions are a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matches the principal gradient of gene expression that encompasses certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.
Original language | English |
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Article number | 6373 |
Pages (from-to) | 6373 |
Journal | Nature Communications |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - 4 Nov 2021 |
Bibliographical note
© 2021. The Author(s).Funding
This work was supported by the Singapore National Research Foundation (NRF) Fellowship (Class of 2017), the NUS Yong Loo Lin School of Medicine (NUHSRO/2020/ 124/TMR/LOA), the Singapore National Medical Research Council (NMRC) LCG (OFLCG19May-0035), and the United States National Institutes of Health (R01MH120080). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not reflect the views of the Singapore NRF or the Singapore NMRC. Our computational work was partially performed on resources of the National Supercomputing Centre, Singapore (https://www.nscc.sg). Data were in part provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
Funders | Funder number |
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NUS Yong Loo Lin School of Medicine | NUHSRO/2020/ 124/TMR/LOA |
National Institutes of Health | 1U54MH091657 |
National Institute of Mental Health | R01MH120080 |
NIH Blueprint for Neuroscience Research | |
McDonnell Center for Systems Neuroscience | |
National Medical Research Council | OFLCG19May-0035 |
National Research Foundation Singapore | Class of 2017 |
Keywords
- Brain/physiology
- Brain Mapping/methods
- Computer Simulation
- Connectome/methods
- Databases, Factual
- Humans
- Magnetic Resonance Imaging/methods
- Models, Neurological
- Neural Pathways/physiology
- Rest/physiology
- Sensorimotor Cortex/physiology