Liu Feng, Stephen Emily P, Prerau Michael J, Purdon Patrick L
Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA.
Int IEEE EMBS Conf Neural Eng. 2019 Mar;2019:299-302. doi: 10.1109/NER.2019.8717043. Epub 2019 May 20.
Understanding how different brain areas interact to generate complex behavior is a primary goal of neuroscience research. One approach, functional connectivity analysis, aims to characterize the connectivity patterns within brain networks. In this paper, we address the problem of connectivity, i.e. determining the differences in network structure under different experimental conditions. We introduce a novel model called Sparse Multi-task Inverse Covariance Estimation (SMICE) which is capable of estimating a common connectivity network as well as discriminative networks across different tasks. We apply the method to EEG signals after solving the inverse problem of source localization, yielding networks defined on the cortical surface. We propose an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve SMICE. We apply our newly developed framework to find common and discriminative connectivity patterns for -oscillations during the Sleep Onset Process (SOP) and during Rapid Eye Movement (REM) sleep. Even though both stages exhibit a similar -oscillations, we show that the underlying networks are distinct.
理解不同脑区如何相互作用以产生复杂行为是神经科学研究的主要目标。一种方法，即功能连接性分析，旨在刻画脑网络内的连接模式。在本文中，我们解决连接性问题，即确定不同实验条件下网络结构的差异。我们引入了一种名为稀疏多任务逆协方差估计（SMICE）的新模型，它能够估计一个共同的连接网络以及跨不同任务的判别性网络。在解决源定位的逆问题后，我们将该方法应用于脑电图信号，得到在皮质表面定义的网络。我们提出一种基于交替方向乘子法（ADMM）的高效算法来求解SMICE。我们应用新开发的框架来寻找睡眠起始过程（SOP）和快速眼动（REM）睡眠期间α振荡的共同和判别性连接模式。尽管两个阶段都表现出相似的α振荡，但我们表明其潜在网络是不同的。
