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

Author

Listed:
  • Pircalabelu, Eugen

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Claeskens, Gerda
  • Jahfari, Sara
  • Waldorp, Lourens J.

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

  • Pircalabelu, Eugen & Claeskens, Gerda & Jahfari, Sara & Waldorp, Lourens J., 2015. "A focused information criterion for graphical models in fMRI connectivity with high-dimensional data," LIDAM Reprints ISBA 2015045, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2015045
    DOI: https://doi.org/10.1214/15-aoas882
    Note: In: The Annals of Applied Statistics, vol. 9, no.4, p. 2179-2214 (2015)
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    Cited by:

    1. Pircalabelu, Eugen & Claeskens, Gerda, 2021. "Linear manifold modeling and graph estimation based on multivariate functional data with different coarseness scales," LIDAM Discussion Papers ISBA 2021032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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