Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausible electrophysiological and imaging features

Abstract

Using The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters:conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain. Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.Brain network models allow us to investigate how realistic neural behaviour arises as a function of variations in specific neural parameters. Large-sc...

neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

“…When overlaying the FCD fit with the FC fit in Fig. 5b, we can also see that the region of overall good fits becomes narrower with sharp drops of the KS distance at around Ke_gl = 250 while the FC correlation remains fairly high at values above 0.5, which is also similar to previous reports [64,65].…”

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

“…The demonstration of a sizeable contribution of cerebro-cerebellar connectivity to global brain dynamics suggests that TVB has the potential to reveal the impact of specific subnetworks on global brain dynamics and to understand the interaction of multiple brain areas ( Aerts et al, 2018 ; Zimmermann et al, 2018 ; An et al, 2019 ). A further improvement to TVB performance could be achieved by using multimodal datasets merging MRI, EEG, MEG, positron emission tomography (PET) data ( Schirner et al, 2018 ; Stefanovski et al, 2019 ; Triebkorn et al, 2020 ).…”

Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state

“…On the one hand, this allowed us to focus on general trends and behaviors, but on the other, it leaves no room for examining how individual differences may affect certain findings. In future work, it will be interesting to explore how various results generalize to both higher-resolution and higher-quality brain data, and to understand how variability in brain structure across different human subjects [78,122,123] or even across different species [124] may relate to differences in how perturbations of neural activity are expressed in system dynamics.…”