Fig. 3.

Results of change-point detection with different values of $K$ and local inference. a CDE of the multivariate Gaussian data with SNR = 5 dB using different models ($K$ = 6, 5, 4, and 3). The sliding window size for converting from time series to correlation matrices sequence is $W$ = 20, whereas (for smoothing) the sliding window size for converting from PPDI to CDE is $W_s$ = 10. The vertical dashed lines are the locations of the true change-points ($t$ = 20, 50, 80, 100, 130, and 160). The multi-color scatterplots in the figures are the CDEs of individual (virtual) subjects and the black curve is the group-level CDE (averaged CDE over 100 subjects). The red dots are the local maxima and the blue dots are the local minima. b Local fitting with different models (from $K$ = 3 to 18) for synthetic data (SNR = 5 dB). Different colors represent the PPDI values of different states with the true number of communities $K^{true}$. c The estimation of community constituents for SNR = 5 dB at each discrete state: $t$ = 36, 66, 91, 116, 146 for brain states 1 to 5, respectively. The estimations of the latent label vectors (Estimation) and the label vectors (True) that determine the covariance matrix in the generative model are shown as bar graphs. The strength and variation of the connectivity within and between communities are represented by the block mean and variance matrices within each panel. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)