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Bayesian scalar-on-image regression with spatial interactions for modeling Alzheimer's disease.

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  • 1Operations Management, Quantitative Methods and Information Systems Area, Indian Institute of Management Udaipur, Udaipur, Rajasthan 313001, India.

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Summary

This study introduces a new Bayesian model to predict cognitive impairment in Alzheimer's disease (AD) by analyzing brain imaging and risk factors. The model improves prediction accuracy and identifies key brain regions associated with cognitive decline in AD patients.

Keywords:
Bayesian inferenceclusteringhigh-dimensionalneuroimaging analysisscalar-on-image regressionspike and slab

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Medical Imaging

Background:

  • Predictive modeling for cognitive impairment in Alzheimer's disease (AD) using neuroimaging shows progress.
  • Existing models often overlook heterogeneity from interactions between brain imaging features and demographic, clinical, or genetic risk factors.
  • Ignoring this heterogeneity can lead to inaccurate predictions and biased estimations in AD research.

Purpose of the Study:

  • To develop a novel statistical framework that incorporates spatially varying interactions between brain imaging data and supplementary risk factors for improved prediction of cognitive impairment in AD.
  • To address the limitations of current predictive models by accounting for complex interactions and heterogeneity in AD.
  • To identify specific brain regions and their interactions with risk factors that are significantly associated with cognitive abilities in AD.

Main Methods:

  • A Bayesian hierarchical model within a scalar-on-function framework using multi-resolution wavelet decomposition.
  • Incorporation of spatially varying interactions between brain imaging features and demographic, clinical, and genetic risk factors.
  • Application of a spike and slab mixture prior with latent class distribution for simultaneous sparsity and clustering to handle high dimensionality.
  • Development of an efficient Markov chain Monte Carlo algorithm for posterior computation.

Main Results:

  • The proposed model significantly improved the prediction of cognitive impairment in AD across multiple longitudinal visits compared to existing methods.
  • The model successfully identified key brain regions in AD that show significant associations with cognitive abilities.
  • Demonstrated the importance of considering interactions between brain imaging and risk factors for accurate AD prediction.

Conclusions:

  • The novel Bayesian approach effectively models heterogeneity and interactions, leading to enhanced prediction of cognitive impairment in Alzheimer's disease.
  • This method provides a more nuanced understanding of AD pathophysiology by highlighting region-specific interactions with risk factors.
  • The findings suggest a promising direction for developing more personalized and accurate diagnostic and prognostic tools for AD.