Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity
Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity
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DOI：
10.1016/j.neuroimage.2013.12.066
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发表时间：
2014-05-01
期刊：
影响因子：
5.7
通讯作者：
Barnes, Gareth R.
中科院分区：
文献类型：
--
作者：
Brookes, Matthew J.;O'Neill, George C.;Barnes, Gareth R.
神经影像学中的功能连接的主题正在迅速扩展，现在许多研究都集中在空间分离的大脑区域之间的耦合。这些研究表明，大脑中存在相对少量的大规模网络，并且这些网络的健康功能在许多临床人群中被破坏。迄今为止，绝大多数探索连通性的研究都采用了计算几分钟内以及特定预定义大脑位置之间的时间平均相关性的技术。然而，越来越多的证据表明，功能连接在时间上是不稳定的。此外，电生理测量表明，连接性取决于神经振荡的频带。也可以想象，网络表现出一定程度的空间不均匀性，即我们观察到的大规模网络可能是由多个瞬时同步的子网络的时间平均值产生的，每个子网络都有自己的空间特征。这意味着下一代神经成像工具计算功能连接必须考虑空间不均匀性，光谱不均匀性和时间非平稳性。在这里，我们提出了一种方法来实现这一点，通过应用窗口典型相关分析（CCA）源空间投影MEG数据。我们描述了生成的时间-频率连接图，显示大脑区域之间的耦合的时间和频谱分布。此外，CCA体素提供了一种手段，以评估在短时间-频率窗口内的空间非均匀性。这种技术的可行性证明在模拟和静息态脑磁图实验中，我们阐明了多个不同的时空谱模式之间的协变的左，右感觉运动区。(C)2014爱思唯尔公司All rights reserved.
The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe the generation of time-frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial non-uniformity within short time-frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatio-temporal-spectral modes of covariation between the left and right sensorimotor areas. (C) 2014 Elsevier Inc. All rights reserved.