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| 001 | 1044797 | ||
| 005 | 20251007202032.0 | ||
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| 100 | 1 | _ | |a Hoheisel, Linnea |0 P:(DE-Juel1)190833 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns |
| 260 | _ | _ | |a Lausanne |c 2025 |b Frontiers Research Foundation |
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| 520 | _ | _ | |a 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) Ji, an inhibitory coupling parameter; (iii) JN, the excitatory NMDA synaptic coupling parameter; and (iv) wp, 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 JN negatively with a range of static and dynamic FC features (|r| > 0.2, pFDR < 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. |
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| 773 | _ | _ | |a 10.3389/fncom.2025.1525785 |g Vol. 19, p. 1525785 |0 PERI:(DE-600)2452964-3 |p 1525785 |t Frontiers in computational neuroscience |v 19 |y 2025 |x 1662-5188 |
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