The Annals of Applied Statistics

A focused information criterion for graphical models in fMRI connectivity with high-dimensional data

Eugen Pircalabelu, Gerda Claeskens, Sara Jahfari, and Lourens J. Waldorp

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Appl. Stat., Volume 9, Number 4 (2015), 2179-2214.

Dates
Received: July 2014
Revised: July 2015
First available in Project Euclid: 28 January 2016

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1453994197

Digital Object Identifier
doi:10.1214/15-AOAS882

Mathematical Reviews number (MathSciNet)
MR3456371

Zentralblatt MATH identifier
06560827

Keywords
fMRI connectivity focused information criterion model selection Gaussian graphical model penalization high-dimensional data

Citation

Pircalabelu, Eugen; Claeskens, Gerda; Jahfari, Sara; Waldorp, Lourens J. A focused information criterion for graphical models in fMRI connectivity with high-dimensional data. Ann. Appl. Stat. 9 (2015), no. 4, 2179--2214. doi:10.1214/15-AOAS882. https://projecteuclid.org/euclid.aoas/1453994197


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