Functional magnetic resonance imaging (fMRI) time series data presents a unique opportunity to understand the behavior of temporal brain connectivity, and models that uncover the complex dynamic workings of this organ are of keen interest in neuroscience. We are motivated to develop accurate change point detection and network estimation techniques for high-dimensional whole-brain fMRI data. To this end, we introduce factorized binary search (FaBiSearch), a novel change point detection method in the network structure of multivariate high-dimensional time series in order to understand the large-scale characterizations and dynamics of the brain. FaBiSearch employs non-negative matrix factorization, an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. In addition, we propose a new method for network estimation for data between change points. We seek to understand the dynamic mechanism of the brain, particularly for two fMRI data sets. The first is a resting-state fMRI experiment, where subjects are scanned over three visits. The second is a task-based fMRI experiment, where subjects read Chapter 9 of Harry Potter and the Sorcerer's Stone. For the resting-state data set, we examine the test-retest behavior of dynamic functional connectivity, while for the task-based data set, we explore network dynamics during the reading and whether change points across subjects coincide with key plot twists in the story. Further, we identify hub nodes in the brain network and examine their dynamic behavior. Finally, we make all the methods discussed available in the R package fabisearch on CRAN.

翻译：功能磁共振成像（fMRI）时间序列数据为理解时变大脑连接行为提供了独特机会，揭示这一器官复杂动态运作机制的模型在神经科学领域备受关注。我们致力于开发适用于高维全脑fMRI数据的精准变点检测与网络估计技术。为此，我们提出因子化二分搜索（FaBiSearch）方法——一种面向多元高维时间序列网络结构的全新变点检测技术，旨在理解大脑的大尺度特征与动态过程。FaBiSearch采用非负矩阵分解这一无监督降维技术，并结合新型二分搜索算法来识别多个变点。此外，我们还提出了一种用于变点间数据网络估计的新方法。我们致力于理解大脑的动态机制，特别针对两个fMRI数据集展开研究：其一是静息态fMRI实验，受试者接受三次扫描；其二是任务态fMRI实验，受试者阅读《哈利·波特与魔法石》第9章。针对静息态数据集，我们考察动态功能连接的重复测试信度；针对任务态数据集，我们探索阅读过程中的网络动态，并检验受试者间变点是否与故事关键情节转折相吻合。进一步，我们识别脑网络中的枢纽节点并分析其动态特征。最后，我们将所讨论的全部方法以R语言软件包fabisearch形式发布在CRAN平台。