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This paper considers the problem of identifying multivariate autoregressive(AR) sparse plus low-rank graphical models. Based on the corresponding problemformulation recently presented, we use the alternating direction method ofmultipliers (ADMM) to efficiently solve it and scale it to sizes encountered inneuroimaging applications. We apply this decomposition on synthetic and realneuroimaging datasets with a specific focus on the information encoded in thelow-rank structure of our model. In particular, we illustrate that thisinformation captures the spatio-temporal structure of the original data,generalizing classical component analysis approaches.

Sparse plus low-rank autoregressive identification in neuroimaging time series