Inferring multi-scale neural mechanisms with brain network modelling

• DOI: 10.7554/elife.28927
• Publication Date: 2018-01-08T13:01:10Z
• Authors: Michael Schirner; Anthony Randal Mc...; Viktor Jirsa; Gustavo Deco; Petra Ritter
• Keywords: Brain modeling; Adult; Male; 500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie; 570; alpha rhythm; QH301-705.5; Science; Models, Neurological; 610; 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit; Young Adult; 03 medical and health sciences; 0302 clinical medicine; Humans; Computer Simulation; EEG; Biology (General); connectomics; Aged; fMRI; Q; [SCCO.NEUR] Cognitive science/Neuroscience; resting-state networks; R; Brain; Electroencephalography; Middle Aged; Magnetic Resonance Imaging; 3. Good health; Medicine; Female; Nerve Net; Computational and Systems Biology

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.