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@ARTICLE{Hoheisel:1044797,
      author       = {Hoheisel, Linnea and Hacker, Hannah and Fink, Gereon R and
                      Daun, Silvia and Kambeitz, Joseph},
      title        = {{C}omputational modelling reveals neurobiological
                      contributions to static and dynamic functional connectivity
                      patterns},
      journal      = {Frontiers in computational neuroscience},
      volume       = {19},
      issn         = {1662-5188},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2025-03353},
      pages        = {1525785},
      year         = {2025},
      abstract     = {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.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / DFG
                      project G:(GEPRIS)491111487 - Open-Access-Publikationskosten
                      / 2025 - 2027 / Forschungszentrum Jülich (OAPKFZJ)
                      (491111487)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.3389/fncom.2025.1525785},
      url          = {https://juser.fz-juelich.de/record/1044797},
}