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 neurogimaging 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 provide 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 signal and also applied to EEG data related to visual tasks.

中文翻译：

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有效连通网络断点的快速检测方法


人们对识别大脑网络潜在状态的变化越来越感兴趣。大规模神经影像数据的可用性强烈需要开发快速、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推测潜在的机制。本文提出了一种基于向量自回归（格兰杰因果）模型的超长时间序列神经影像数据中检测断点的快速方法。它使用基于正则化目标函数的多步骤策略，可快速识别候选断点，然后通过聚类步骤来选择最终的断点集，并通过对底层格兰杰因果网络的误报控制进行后续估计。后者提供了对导致断点存在的网络连接的关键变化的见解。所提出的方法在长度、维度、断点数量、信号强度不同的合成数据上进行说明，并且也应用于与视觉任务相关的脑电图数据。