Schirner Michael, McIntosh Anthony Randal, Jirsa Viktor, Deco Gustavo, Ritter Petra
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Berlin, Germany.
Berlin Institute of Health (BIH), Berlin, Germany.
Elife. 2018 Jan 8;7:e28927. doi: 10.7554/eLife.28927.
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.
非侵入性脑活动测量背后的神经生理过程尚未完全被理解。在此，我们开发了一种基于连接组的脑网络模型，该模型将个体的结构和功能数据与神经群体动力学相结合，以支持多尺度神经生理推断。模拟群体通过结构连接性相互关联，并且新颖的是，由脑电图（EEG）源活动驱动。模拟不仅预测了受试者在20分钟活动期间的个体静息态功能磁共振成像（fMRI）时间序列和空间网络拓扑结构，更重要的是，它们还揭示了精确的神经生理机制，这些机制构成并连接了来自不同尺度和模态的六个实证观察结果：（1）静息态fMRI振荡，（2）功能连接网络，（3）兴奋 - 抑制平衡，（4,5）α节律、脉冲发放与fMRI在短时间和长时间尺度上的反比关系，以及（6）fMRI幂律缩放。这些发现强调了这种新的建模框架在神经生理知识的一般推断和整合方面的潜力，以补充实证研究。
