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An explainable framework for the relationship between dementia and metabolism patterns.

C Vázquez-García1, F J Martínez-Murcia1, F Segovia1

  • 1Department of Signal Processing and Biomedical Applications, University of Granada, Granada 18071, Spain.

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This summary is machine-generated.

This study introduces a novel semi-supervised Variational Autoencoder (VAE) framework to analyze neuroimaging data for neurodegenerative diseases. The model effectively disentangles disease biomarkers from confounding factors, aiding in understanding Alzheimer's Disease progression.

Keywords:
ADNIAlzheimerComputational neurosciencePETVariational Autoencoder

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • High-dimensional neuroimaging data presents challenges in assessing neurodegenerative diseases due to complex, non-linear relationships.
  • Traditional methods struggle to disentangle these intricate patterns, hindering accurate clinical assessment.
  • Variational Autoencoders (VAEs) offer a promising approach for dimensionality reduction in neuroimaging, encoding scans into meaningful latent spaces.

Purpose of the Study:

  • To develop and validate a semi-supervised VAE framework for analyzing neuroimaging data in neurodegenerative diseases.
  • To align latent variables with clinical and biomarker measures of dementia progression, enabling interpretable disease modeling.
  • To effectively disentangle disease-specific neuroimaging biomarkers from confounding factors like age and scanner variability.

Main Methods:

  • Implementation of a semi-supervised VAE with a flexible similarity regularization term.
  • Utilizing Positron Emission Tomography (PET) scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
  • Guiding the model to capture Alzheimer's Disease (AD) patterns by correlating latent dimensions with cognitive scores and age.

Main Results:

  • The VAE framework successfully captured neurodegenerative patterns, aligning latent dimensions with cognitive decline and age.
  • Average reconstructions visualized varying levels of cognitive impairment, highlighting disease progression.
  • Voxel-wise General Linear Model (GLM) analysis confirmed reduced metabolism in key brain regions (hippocampus) and Resting State Networks (DMN, CEN).
  • Remaining latent variables identified and encoded confounding factors like inter-subject variability and site noise.

Conclusions:

  • The proposed semi-supervised VAE framework effectively disentangles neuroimaging biomarkers from confounding factors and age.
  • This adaptable tool provides an interpretable method for modeling and visualizing neurodegenerative disease progression.
  • The findings support the use of VAEs for advanced analysis of complex neuroimaging data in clinical research.