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An Explainable Framework for the Relationship between Dementia and Metabolism Patterns.
C Vázquez-García1, F J Martínez-Murcia1, F Segovia Román1
1Department of Signal Processing and Biomedical Applications, University of Granada, Granada 18071, Spain.
This study introduces a semi-supervised Variational Autoencoder (VAE) framework to analyze neuroimaging data for neurodegenerative diseases. The model aligns latent spaces with cognitive scores, revealing key brain region metabolism changes in Alzheimer's disease progression.
Area of Science:
- Neuroscience
- Medical Imaging
- Machine Learning
Background:
- High-dimensional neuroimaging data poses challenges for studying neurodegenerative diseases due to complex relationships.
- Variational Autoencoders (VAEs) offer a method to reduce data dimensionality and extract relevant features.
Purpose of the Study:
- To develop a semi-supervised VAE framework for neuroimaging analysis in neurodegenerative diseases.
- To align latent representations with clinical or biomarker measures of disease progression, such as cognitive scores.
Main Methods:
- Implemented a semi-supervised VAE with a flexible similarity regularization term.
- Utilized PET scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
- Guided the first latent dimension to correlate with a cognitive score.
Main Results:
- Generated average reconstructions reflecting cognitive impairment levels.
- Identified reduced metabolism in the hippocampus and key Resting State Networks (Default Mode, Central Executive).
- Latent variables captured confounds like inter-subject variability and site effects.
Conclusions:
- The VAE framework effectively extracts Alzheimer's disease-related patterns aligned with biomarkers.
- The approach provides an interpretable and adaptable tool for studying neurodegenerative disease progression.
- Demonstrated utility in linking neuroimaging features to cognitive decline.
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