O'Neill George C, Bauer Markus, Woolrich Mark W, Morris Peter G, Barnes Gareth R, Brookes Matthew J
Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom.
School of Psychology, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom.
Neuroimage. 2015 Jul 15;115:85-95. doi: 10.1016/j.neuroimage.2015.04.030. Epub 2015 Apr 18.
Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with evidence suggesting that they are integral to healthy brain function and perturbed in pathology. Despite rapid progress in this area, the temporal dynamics governing the functional connectivities that underlie RSN structure remain poorly understood. Here, we present a framework to help further our understanding of RSN dynamics. We describe a methodology which exploits the direct nature and high temporal resolution of magnetoencephalography (MEG). This technique, which builds on previous work, extends from solving fundamental confounds in MEG (source leakage) to multivariate modelling of transient connectivity. The resulting processing pipeline facilitates direct (electrophysiological) measurement of dynamic functional networks. Our results show that, when functional connectivity is assessed in small time windows, the canonical sensorimotor network can be decomposed into a number of transiently synchronising sub-networks, recruitment of which depends on current mental state. These rapidly changing sub-networks are spatially focal with, for example, bilateral primary sensory and motor areas resolved into two separate sub-networks. The likely interpretation is that the larger canonical sensorimotor network most often seen in neuroimaging studies reflects only a temporal aggregate of these transient sub-networks. Our approach opens new frontiers to study RSN dynamics, showing that MEG is capable of revealing the spatial, temporal and spectral signature of the human connectome in health and disease.
静息态网络（RSNs）在人类系统神经科学中至关重要，有证据表明它们是健康大脑功能不可或缺的部分，且在病理状态下会受到干扰。尽管该领域取得了快速进展，但对于构成RSN结构基础的功能连接的时间动态变化仍知之甚少。在此，我们提出一个框架以助于进一步理解RSN动态变化。我们描述了一种利用脑磁图（MEG）的直接特性和高时间分辨率的方法。该技术基于先前的工作，从解决MEG中的基本混淆问题（源泄漏）扩展到对瞬态连接的多变量建模。由此产生的处理流程有助于对动态功能网络进行直接（电生理）测量。我们的结果表明，当在小时间窗口内评估功能连接时，典型的感觉运动网络可分解为多个瞬态同步的子网，其募集取决于当前的心理状态。这些快速变化的子网在空间上具有焦点性，例如，双侧初级感觉和运动区域被解析为两个独立的子网。可能的解释是，在神经影像学研究中最常看到的较大的典型感觉运动网络仅反映了这些瞬态子网的时间总和。我们的方法为研究RSN动态变化开辟了新的前沿领域，表明MEG能够揭示健康和疾病状态下人类连接组的空间、时间和频谱特征。
