Co-Activation Pattern Analysis Based on Hidden Semi-Markov Model for Brain Spatiotemporal Dynamics

Analyzing the spontaneous activity of the human brain using dynamic approaches can reveal functional organizations. The co-activation pattern (CAP) analysis of signals from different brain regions is used to characterize brain neural networks that may serve specialized functions. However, CAP is based on spatial information but ignores temporal reproducible transition patterns, and lacks robustness to low signal-to-noise rate (SNR) data. To address these issues, this study proposes a new CAP framework based on hidden semi-Markov model (HSMM) called HSMM-CAP analysis, which can be performed to investigate spatiotemporal CAPs (stCAPs) of the brain. HSMM-CAP uses empirical spatial distributions of stCAPs as emission models, and assumes that the state sequence of stCAPs follows a semi-Markov process. Based on the assumptions of sparsity, heterogeneity, and semi-Markov property of stCAPs, the HSMM-CAP-K-means method is constructed to infer the state sequence and transition parameters of stCAPs. In addition, HSMM-CAP provides the inverse relationship between the number of states and sparsity. Simulation studies verify the performance of HSMM-CAP at different levels of SNR. The spatiotemporal dynamics of stCAPs are also revealed by the proposed method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for revealing the brain spatiotemporal dynamics of resting-state fMRI data.