Task induced modulation of neural oscillations in electrophysiological brain networks
Task induced modulation of neural oscillations in electrophysiological brain networks
复制标题
DOI：
10.1016/j.neuroimage.2012.08.012
复制
发表时间：
2012-12-01
期刊：
影响因子：
5.7
通讯作者：
Morris, P. G.
中科院分区：
文献类型：
--
作者：
Brookes, M. J.;Liddle, E. B.;Morris, P. G.
近年来，系统神经科学中最重要的发现之一是识别大规模分布式脑网络。这些网络支持健康的大脑功能，并在许多神经系统疾病（如精神分裂症）中受到干扰。因此，他们的研究是神经科学研究的一个重要和不断发展的焦点。大多数网络研究都是使用功能性磁共振成像（fMRI）进行的，它依赖于神经活动引起的血氧变化。然而，最近，少数研究已经开始阐明的电起源的功能磁共振成像网络的脑磁图（MEG）数据中的空间分离的脑区的神经振荡信号之间的相关性，在这里，我们推进这一研究领域。我们介绍了两种方法的扩展，以前的独立成分分析（伊卡）方法的脑磁网络特性：1）我们展示了如何获得泛谱网络，联合收割机独立成分计算在各个频带内。2)我们展示了如何测量每个网络的时间演化与毫秒的时间分辨率。我们将我们的方法应用于在3个独立的认知任务中记录的28个实验会话中的类似10小时的MEG数据，表明可以识别许多网络，并且在时间，任务，主题和记录会话中具有鲁棒性。此外，我们表明，在这些网络中的神经振荡调制的记忆负荷和任务的相关性。这项研究进一步推动了最近关于电动脑网络的发现，并为未来的临床研究铺平了道路，这些研究认为异常连接是核心症状的基础。(C)2012 Elsevier Inc. All rights reserved.
In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalograPhy (MEG) data Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to similar to 10 h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms. (C) 2012 Elsevier Inc. All rights reserved.