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Elife. 2018 Jan 8;7. pii: e28927. doi: 10.7554/eLife.28927.

Inferring multi-scale neural mechanisms with brain network modelling.

Schirner M1,2,3, McIntosh AR4, Jirsa V5, Deco G6,7,8,9, Ritter P1,2,3,10.

Author information

1
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Berlin, Germany.
2
Berlin Institute of Health (BIH), Berlin, Germany.
3
Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany.
4
Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Canada.
5
Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine, Marseille, France.
6
Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
7
Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
8
Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
9
School of Psychological Sciences, Monash University, Melbourne, Australia.
10
Berlin School of Mind and Brain & MindBrainBody Institute, Humboldt University, Berlin, Germany.

Abstract

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.

KEYWORDS:

Brain modeling; EEG; alpha rhythm; computational biology; connectomics; fMRI; human; neuroscience; resting-state networks; systems biology

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