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Related Experiment Video

Updated: Jun 4, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

A model selection method for nonlinear system identification based FMRI effective connectivity analysis.

Xingfeng Li1, Damien Coyle, Liam Maguire

  • 1INSERM, UPMC Université Paris 06, UMR_S 678, Laboratoire d’Imagerie Fonctionnelle, Paris Cedex 13, France. x.li@ulster.ac.uk

IEEE Transactions on Medical Imaging
|February 22, 2011
PubMed
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This study introduces a novel model selection algorithm for functional magnetic resonance imaging (fMRI) effective connectivity analysis. The proposed method efficiently identifies significant nonlinear or linear relationships without pre-defined models, offering a faster alternative to existing techniques.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Systems Biology

Background:

  • Functional magnetic resonance imaging (fMRI) is crucial for understanding brain activity.
  • Effective connectivity analysis aims to map directed interactions between brain regions.
  • Existing methods often require pre-defined models, limiting their flexibility.

Purpose of the Study:

  • To propose a model selection algorithm for nonlinear system identification in fMRI effective connectivity.
  • To develop a method that does not require a pre-defined model structure.
  • To compare the performance of Akaike's information criterion corrected (AICc) and leave-one-out (LOO) cross-validation for model selection.

Main Methods:

  • A novel model selection algorithm based on least angle regression (LARS) for identifying significant linear or nonlinear covariates.

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  • Estimation of covariate coefficients using the LARS method.
  • Comparison of AICc and LOO cross-validation for selecting the best model.

    • Main Results:

    • The LARS model selection method demonstrated faster computation compared to dynamic causal modeling (DCM).
    • The proposed method achieved compact and parsimonious nonlinear models.
    • Leave-one-out (LOO) cross-validation resulted in lower residual sum of squares than AICc for nonlinear model selection.
    • The LARS method was successfully applied to analyze dorsal and ventral visual pathway networks using real fMRI datasets with various experimental designs.

    Conclusions:

    • The LARS-based model selection algorithm provides an efficient and flexible approach for fMRI effective connectivity analysis.
    • The proposed method can identify complex nonlinear relationships without prior structural assumptions.
    • LOO cross-validation is a more effective model selection strategy than AICc in this nonlinear context.