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A focused information criterion for graphical models in fMRI connectivity with high-dimensional data. (English) Zbl 1397.62466
Summary: Connectivity in the brain is the most promising approach to explain human behavior. Here we develop a focused information criterion for graphical models to determine brain connectivity tailored to specific research questions. All efforts are concentrated on high-dimensional settings where the number of nodes in the graph is larger than the number of samples. The graphical models may include autoregressive times series components, they can relate graphs from different subjects or pool data via random effects. The proposed method selects a graph with a small estimated mean squared error for a user-specified focus. The performance of the proposed method is assessed on simulated data sets and on a resting state functional magnetic resonance imaging (fMRI) data set where often the number of nodes in the estimated graph is equal to or larger than the number of samples.

MSC:
62P10 Applications of statistics to biology and medical sciences; meta analysis
62B10 Statistical aspects of information-theoretic topics
62H12 Estimation in multivariate analysis
05C90 Applications of graph theory
92C55 Biomedical imaging and signal processing
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