The analysis of functional connectivity (FC) networks in resting-state functional magnetic resonance imaging (rs-fMRI) has recently evolved to a dynamic FC approach, where the functional networks are presumed to vary throughout a scanning session. Central challenges in dFC analysis involve partitioning rs-fMRI into segments of static FC and achieving high replicability while controlling for false positives. In this work we propose Rank-Adapative Covariance Changepoint detection (RACC), a changepoint detection method to address these challenges. RACC utilizes a binary segmentation procedure with novel test statistics able to detect changes in covariances driven by low-rank latent factors, which are useful for understanding changes occurring within and between functional networks. A permutation scheme is used to address the high dimensionality of the data and to provide false positive control. RACC improves upon existing rs-fMRI changepoint detection methods by explicitly controlling Type 1 error and improving sensitivity in estimating dFC at the whole-brain level. We conducted extensive simulation studies across a variety of data generating scenarios, and applied RACC to a rs-fMRI dataset of subjects with schizophrenia spectrum disorder and healthy controls to highlight our findings.

翻译：静息态功能磁共振成像（rs-fMRI）中功能连接（FC）网络的分析近期已发展为动态FC方法，该方法假定功能网络在扫描过程中随时间变化。动态FC分析的核心挑战包括将rs-fMRI分割为静态FC片段，以及在控制假阳性率的同时实现高可重复性。本文提出秩自适应协方差变点检测（RACC），一种应对这些挑战的变点检测方法。RACC采用二元分割流程，结合新型检验统计量，能够检测由低秩潜在因子驱动的协方差变化，这对于理解功能网络内部及网络间的变化至关重要。采用置换检验策略应对数据的高维性并提供假阳性控制。RACC通过明确控制第一类错误并提升全脑水平动态FC估计的敏感性，改进了现有rs-fMRI变点检测方法。我们针对多种数据生成场景开展了广泛的仿真研究，并将RACC应用于包含精神分裂症谱系障碍患者与健康对照者的rs-fMRI数据集以突出研究成果。