Functional magnetic resonance imaging (fMRI) time series data presents a unique opportunity to understand temporal brain connectivity, and models that uncover the complex dynamic workings of this organ are of keen interest in neuroscience. Change point models can capture and reflect the dynamic nature of brain connectivity, however methods that translate well into a high-dimensional context (where p>>n) are scarce. 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. FaBiSearch uses 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 show that FaBiSearch outperforms another state-of-the-art method on simulated data sets and we apply FaBiSearch to a resting-state and to a task-based fMRI data set. In particular, for the task-based data set, we explore network dynamics during the reading of Chapter 9 in Harry Potter and the Sorcerer's Stone and find that change points across subjects coincide with key plot twists. Further, we find that the density of networks was positively related to the frequency of speech between characters in the story. Finally, we make all the methods discussed available in the R package fabisearch on CRAN.

翻译：功能性磁共振成像（fMRI）时间序列数据为了解大脑时间连接性提供了独特机会，而揭示这一器官复杂动态工作过程的模型在神经科学中备受关注。变点模型能够捕捉并反映大脑连接性的动态特性，然而，能有效适用于高维情境（其中p>>n）的方法却十分匮乏。为此，我们提出了因式分解二分搜索（FaBiSearch），这是一种针对多元高维时间序列网络结构的新型变点检测方法。FaBiSearch利用非负矩阵分解（一种无监督降维技术）和新的二分搜索算法来识别多个变点。此外，我们还提出了一种新方法，用于估计变点之间的数据网络结构。我们证明，在模拟数据集上，FaBiSearch优于另一种最先进的方法，并将其应用于静息态和基于任务的功能磁共振成像数据集。特别是在基于任务的数据集中，我们探究了阅读《哈利·波特与魔法石》第九章期间的网络动态，发现不同受试者间的变点与关键情节转折相吻合。进一步，我们发现网络密度与故事中角色间对话频率呈正相关。最后，我们将所讨论的所有方法以R包fabisearch的形式发布在CRAN平台上。