Estimating whole-brain dynamics by using spectral clustering
Estimating whole-brain dynamics by using spectral clustering
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DOI：
10.1111/rssc.12169
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发表时间：
2017-04-01
影响因子：
1.6
通讯作者：
Yu, Yi
中科院分区：
文献类型：
--
作者：
Cribben, Ivor;Yu, Yi
时变网络的功能磁共振成像数据集的估计是越来越重要和感兴趣的。我们制定了一个高维的时间序列框架中的问题，并介绍了一种数据驱动的方法，即网络变点检测，检测变点的网络结构的多变量时间序列，与每个组件的时间序列表示的网络中的一个节点。将网络变化点检测应用于各种模拟数据和静息状态功能磁共振成像数据集。这种新的方法还使我们能够识别受试者内部和跨受试者的常见功能状态。最后，网络变点检测有望为大脑的大规模特征和动力学提供深入的见解。
The estimation of time varying networks for functional magnetic resonance imaging data sets is of increasing importance and interest. We formulate the problem in a high dimensional time series framework and introduce a data-driven method, namely network change points detection, which detects change points in the network structure of a multivariate time series, with each component of the time series represented by a node in the network. Network change points detection is applied to various simulated data and a resting state functional magnetic resonance imaging data set. This new methodology also allows us to identify common functional states within and across subjects. Finally, network change points detection promises to offer a deep insight into the large-scale characterizations and dynamics of the brain.