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

Eugen Pircalabelu, Gerda Claeskens, Sara Jahfari and Lourens Waldorp

No 510578, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

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 datasets and on a resting state functional magnetic resonance imaging (fMRI) dataset where often the number of nodes in the estimated graph is equal to, or larger than the number of samples.

Keywords: fMRI connectivity; Focused information criterion; Model selection; Gaussian graphical model; Penalization; High-dimensional data (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

Published in FEB Research Report KBI_1524

Downloads: (external link)
https://lirias.kuleuven.be/bitstream/123456789/510578/1/KBI_1524.pdf A focused information criterion for graphical models in fMRI connectivity with high-dimensional data (application/pdf)
intranet

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ete:kbiper:510578

Access Statistics for this paper

More papers in Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven
Bibliographic data for series maintained by library EBIB ().