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A Bayesian group lasso classification for ADNI volumetrics data.

Atreyee Majumder1, Tapabrata Maiti1, Subha Datta2

  • 1Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA.

Statistical Methods in Medical Research
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian group lasso method to accurately classify Alzheimer's disease (AD) patients using brain imaging data. The novel approach achieved 80% accuracy by identifying key brain regions affected by AD.

Keywords:
Alzheimer’s Disease Neuroimaging Initiative data classificationBayesian group lassogroup lassologistic regressionslabspike

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Machine Learning

Background:

  • Alzheimer's Disease Neuroimaging Initiative (ADNI) provides valuable data for cognitive impairment research.
  • Accurate classification of Alzheimer's disease (AD) is crucial for early intervention.
  • Existing methods may struggle with high-dimensional brain volumetrics data.

Purpose of the Study:

  • To develop a statistically robust classification procedure for AD using brain image volumetrics.
  • To apply a Bayesian group lasso method for logistic regression in analyzing ADNI data.
  • To identify predictive brain subregions associated with Alzheimer's disease.

Main Methods:

  • Utilized a Bayesian group lasso method with a spike and slab prior for logistic regression.
  • Employed group lasso penalty to select groups of brain subregion attributes.
  • Validated the method through simulation studies in high- and low-dimensional scenarios.

Main Results:

  • The proposed method achieved an 80% accuracy rate in classifying Alzheimer's disease patients.
  • Successfully identified 29 statistically significant brain subregions associated with AD.
  • Demonstrated the ability to select true predictive parameters even with a large number of variables.

Conclusions:

  • The Bayesian group lasso method offers an effective tool for analyzing brain volumetrics in AD research.
  • The identified brain subregions are consistent with known AD pathology.
  • This approach facilitates the selection of optimal, statistically significant models for disease classification.