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Bayesian pathway analysis over brain network mediators for survival data.

Xinyuan Tian1, Fan Li1,2,3, Li Shen4

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT 06511, United States.

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Summary
This summary is machine-generated.

This study introduces a novel Bayesian approach to analyze brain connectivity, genetic exposure, and disease onset. The method maximizes information extraction, offering new insights into neurodegenerative disease mechanisms.

Keywords:
accelerated failure time modelbrain connectivityimaging geneticsmediation analysisnatural indirect effectshrinkage and regularization

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Medical Imaging

Background:

  • Noninvasive imaging enables whole brain connectivity network construction.
  • Current analysis methods for brain connectivity often result in significant information loss.
  • Understanding the interplay between genetic factors, brain networks, and disease onset is crucial.

Purpose of the Study:

  • To propose a Bayesian approach for modeling the pathway between genetic exposure, brain connectivity, and time to disease onset.
  • To quantify the mediating role of brain networks in this pathway.
  • To maximize information extraction from complex brain connectivity data.

Main Methods:

  • Development of a structural model accommodating biological architectures of brain connectivity.
  • Inclusion of a symmetric matrix-variate accelerated failure time model for disease onset.
  • Incorporation of a symmetric matrix response regression for the network-variate mediator.
  • Application of within-graph sparsity and between-graph shrinkage for identifying informative network configurations.

Main Results:

  • Simulations confirm the proposed method's advantages over existing alternatives.
  • Application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study yields neurobiologically plausible insights.
  • The method effectively identifies informative network structures and mitigates interference from noisy components.

Conclusions:

  • The proposed Bayesian approach offers a powerful tool for analyzing complex relationships in neuroimaging and disease progression.
  • This method enhances information extraction from brain connectivity data compared to traditional approaches.
  • Findings from the ADNI study suggest potential for informing future intervention strategies for neurodegenerative diseases.