A new model for simultaneous dimensionality reduction and time-varying functional connectivity estimation
Fig 1
Two different approaches for the estimation of time-varying FC on high-dimensional fMRI data.
A. PCA is first used as a dimensionality reduction step, blindly to the purpose of estimating time-varying FC; then, some state-based model (like the hidden Markov model) is run on the first principal components (PC). B. The HMM-PCA approach, where each state is a different PCA decomposition, is run directly on the high-dimensional data; given that the computation of PCA is based on the data covariance, different PCA decompositions capture different patterns of FC. See S1 Fig for representations in the form of graphical models.