ORIGINAL RESEARCH article
Front. Comput. Neurosci.
Volume 19 - 2025 | doi: 10.3389/fncom.2025.1525785
Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns
Provisionally accepted- 1Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Jülich, North Rhine-Westphalia, Germany
- 2Department of Psychiatry and Psychotherapy, University Hospital of Cologne and Faculty of Medicine, University of Cologne, Cologne, Germany
- 3Department of Neurology, University Hospital of Cologne and Faculty of Medicine, University of Cologne, Cologne, Germany
- 4Institute of Zoology, University of Cologne, Cologne, Germany
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Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow us to investigate the relationship of both static FC (sFC) and dynamic FC (dFC) with neurobiology non-invasively. In this study, we modelled the brain activity of 200 healthy individuals based on empirical resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. Simulations were conducted using a group-averaged structural connectome and four parameters guiding global integration and local excitation-inhibition balance: i) G, a global coupling scaling parameter; ii) J i , an inhibitory coupling parameter; iii) J N , the excitatory NMDA synaptic coupling parameter; and iv) w p , the excitatory population recurrence weight. For each individual, we optimised the parameters to replicate empirical sFC and temporal correlation (TC). We analysed associations between brain-wide sFC and TC features with optimal model parameters and fits with a univariate correlation approach and multivariate prediction models. In addition, we used a group-average perturbation approach to investigate the effect of coupling in each region on overall network connectivity. Our models could replicate empirical sFC and TC but not the FC variance or node cohesion (NC). Both fits and parameters exhibited strong associations with brain connectivity. G correlated positively and J N negatively with a range of static and dynamic FC features (|r| > 0.2, p FDR < 0.05). TC fit correlated negatively, and sFC fit positively with static and dynamic FC features. TC features were predictive of TC fit, sFC features of sFC fit (R² > 0.5). Perturbation analysis revealed that the sFC fit was most impacted by coupling changes in the left paracentral gyrus (Δr = 0.07), TC fit by alterations in the left pars triangularis (Δr = 0.24). Our findings indicate that neurobiological characteristics are associated with individual variability in sFC and dFC, and that sFC and dFC are shaped by small sets of distinct regions. By modelling both sFC and dFC, we provide new evidence of the role of neurophysiological characteristics in establishing brain network configurations.
Keywords: Computational modelling, resting-state functional MRI, Dynamic functional connectivity (dFC), brain networks, Whole-brain connectivity
Received: 10 Nov 2024; Accepted: 15 Jun 2025.
Copyright: © 2025 Hoheisel, Hacker, Fink, Daun and Kambeitz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Linnea Hoheisel, Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Jülich, North Rhine-Westphalia, Germany
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