Rxivist logo

Inferring multi-scale neural mechanisms with brain network modelling

By Michael Schirner, Anthony Randal McIntosh, Viktor K. Jirsa, Gustavo Deco, Petra Ritter

Posted 28 Jun 2017
bioRxiv DOI: 10.1101/157263 (published DOI: 10.7554/elife.28927)

The neurophysiological processes underlying non-invasive brain activity measurements are not well understood. Here, we developed a novel 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) slow resting-state fMRI oscillations, (2) spatial topologies of functional connectivity networks, (3) excitation-inhibition balance, (4, 5) pulsed inhibition 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.

Download data

  • Downloaded 421 times
  • Download rankings, all-time:
    • Site-wide: 31,573 out of 77,363
    • In bioinformatics: 4,072 out of 7,469
  • Year to date:
    • Site-wide: 63,834 out of 77,363
  • Since beginning of last month:
    • Site-wide: 70,394 out of 77,363

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News