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Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

Triebkorn, P.; Zimmermann, J.; Stefanovski, L.; Roy, D.; Solodkin, A.; Jirsa, V.; Deco, G.; Breakspear, M.; McIntosh, A. R.; Ritter, P.

2020-03-26 neuroscience
10.1101/2020.03.26.009795 bioRxiv
Show abstract

Brain network modeling studies are often limited with respect to the number of data features fitted, although capturing multiple empirical features is important to validate the models overall biological plausibility. Here we construct personalized models from multimodal data of 50 healthy individuals (18-80 years) with The Virtual Brain and demonstrate that an individuals brain has its own converging optimal working point in the parameter space that predicts multiple empirical features in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We further show that bimodality in the alpha band power - as an explored novel feature - arises as a function of global coupling and exhibits inter-regional differences depending on the degree. Reliable inter-individual differences with respect to these optimal working points were found that seem to be driven by the individual structural rather than by the functional connectivity. Our results provide the groundwork for future multimodal brain modeling studies.

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