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

Author

Listed:
  • Eugen Pircalabelu
  • Gerda Claeskens
  • Sara Jahfari
  • Lourens Waldorp

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.

Suggested Citation

  • Eugen Pircalabelu & Gerda Claeskens & Sara Jahfari & Lourens Waldorp, 2015. "A focused information criterion for graphical models in fMRI connectivity with high-dimensional data," Working Papers of Department of Decision Sciences and Information Management, Leuven 510578, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
    • Handle: RePEc:ete:kbiper:510578
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