Vidaurre Diego, Quinn Andrew J, Baker Adam P, Dupret David, Tejero-Cantero Alvaro, Woolrich Mark W
Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, UK.
MRC Brain Network Dynamics Unit, Department of Pharmacology, University of Oxford, UK.
Neuroimage. 2016 Feb 1;126:81-95. doi: 10.1016/j.neuroimage.2015.11.047. Epub 2015 Nov 26.
The brain is capable of producing coordinated fast changing neural dynamics across multiple brain regions in order to adapt to rapidly changing environments. However, it is non-trivial to identify multiregion dynamics at fast sub-second time-scales in electrophysiological data. We propose a method that, with no knowledge of any task timings, can simultaneously identify and describe fast transient multiregion dynamics in terms of their temporal, spectral and spatial properties. The approach models brain activity using a discrete set of sequential states, with each state distinguished by its own multiregion spectral properties. This can identify potentially very short-lived visits to a brain state, at the same time as inferring the state's properties, by pooling over many repeated visits to that state. We show how this can be used to compute state-specific measures such as power spectra and coherence. We demonstrate that this can be used to identify short-lived transient brain states with distinct power and functional connectivity (e.g., coherence) properties in an MEG data set collected during a volitional motor task.
大脑能够在多个脑区产生协调的快速变化的神经动力学，以适应快速变化的环境。然而，在电生理数据中识别亚秒级快速时间尺度上的多区域动力学并非易事。我们提出了一种方法，该方法无需了解任何任务时间，就能根据其时间、频谱和空间特性同时识别和描述快速瞬态多区域动力学。该方法使用一组离散的顺序状态对大脑活动进行建模，每个状态由其自身的多区域频谱特性区分。通过汇总对该状态的多次重复访问，这可以在推断状态特性的同时，识别对大脑状态的潜在非常短暂的访问。我们展示了如何使用此方法来计算特定状态的测量值，如功率谱和相干性。我们证明，在一项自主运动任务期间收集的脑磁图（MEG）数据集中，此方法可用于识别具有不同功率和功能连接性（如相干性）特性的短暂瞬态脑状态。
