一种有效连通网络断点的快速检测方法
A Fast Detection Method of Break Points in Effective Connectivity Networks
摘要：
人们对识别大脑网络底层状态的变化越来越感兴趣。大规模神经成像数据的可用性产生了强烈的需求，即开发快速、可扩展的方法来及时检测和定位这些变化，并识别其驱动因素，从而使神经科学家能够对潜在的机制进行假设。提出了一种基于向量自回归（Granger因果）模型的超长时间序列神经影像数据断点快速检测方法。它使用基于正则化目标函数的多步策略，该策略导致快速识别候选断点，然后通过聚类步骤选择最终的断点集，并随后估计底层Granger因果网络的误报控制。后者提供了对网络连接中导致断点存在的关键变化的见解。所提出的方法说明合成数据的长度，维数，断点的数量，信号的强度不同，也适用于EEG数据相关的视觉任务。
英文摘要：
There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop fast, scalable methods for detecting and localizing in time such changes and also identify their drivers, thus enabling neuroscientists to hypothesize about potential mechanisms. This paper presents a fast method for detecting break points in exceedingly long time series neuroimaging data, based on vector autoregressive (Granger causal) models. It uses a multi-step strategy based on a regularized objective function that leads to fast identification of candidate break points, followed by clustering steps to select the final set of break points and subsequent estimation with false positives control of the underlying Granger causal networks. The latter provides insights into key changes in network connectivity that led to the presence of break points. The proposed methodology is illustrated on synthetic data varying in their length, dimensionality, number of break points, strength of the signal, and also applied to EEG data related to visual tasks.
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