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Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and

Xingfeng Li1, Yuan Zhang2

  • 1Department of Surgery & Cancer, Hammersmith Campus, Imperial College London, Du Cane Road, London W12 0HS, UK.

Tomography (Ann Arbor, Mich.)
|October 25, 2024
PubMed
Summary

This study introduces a new method using LASSO model selection to identify brain network structures and causal influences from fMRI data, overcoming limitations of previous approaches.

Keywords:
brain imagingeffective connectivityfMRIleast absolute shrinkage and selection operator (LASSO)model selectionsystem identificationvisual cortex

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Area of Science:

  • Neuroimaging
  • Systems Neuroscience
  • Computational Neuroscience

Background:

  • Functional magnetic resonance imaging (fMRI) is crucial for studying brain connectivity.
  • Existing methods often focus on influence magnitude, neglecting network structure identification.
  • Directly modeling brain networks can lead to overfitting issues.

Purpose of the Study:

  • To develop a novel method for identifying brain network structures and causal influences from fMRI data.
  • To address the overfitting problem in nonlinear system identification for brain networks.
  • To accurately estimate both network architecture and connection strengths.

Main Methods:

  • Employed a nonlinear system identification approach with a polynomial kernel.
  • Applied the Least Absolute Shrinkage and Selection Operator (LASSO) model selection for network and coefficient identification.
  • Validated the method on the human visual cortex using phase-encoded fMRI data and retinotopic mapping.

Main Results:

  • Successfully identified network structures and associated causalities between brain regions.
  • Demonstrated the ability of LASSO to select relevant connections and estimate their strengths.
  • The method accurately mapped the eight-connection visual system network.

Conclusions:

  • System identification combined with LASSO offers a robust solution for fMRI effective connectivity analysis.
  • This approach enhances the understanding of brain network dynamics.
  • It provides a powerful tool for investigating causal relationships in the brain.