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  1. Home
  3. Smas: Structural Mri-based Ad Score Using Bayesian Supervised Vae.
  1. Home
  3. Smas: Structural Mri-based Ad Score Using Bayesian Supervised Vae.

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Basics of Multivariate Analysis in Neuroimaging Data
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SMAS: Structural MRI-based AD Score using Bayesian supervised VAE.

A Nemali1, J Bernal1, R Yakupov1

  • 1Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University (OVGU), Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.

Computers in Biology and Medicine
|August 16, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new Structural MRI-based Alzheimer's Disease Score (SMAS) effectively quantifies brain atrophy. This deep learning biomarker shows strong associations with cognitive decline and aids early Alzheimer's disease detection and tracking.

Keywords:
Alzheimer’s diseaseBayesian Supervised Variational AutoencoderBayesian inferenceBrain morphology indicesCognitive decline

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Biomarker Development

Background:

  • Alzheimer's Disease (AD) poses a significant challenge, necessitating advanced diagnostic tools.
  • Current methods for assessing AD-related brain changes can be limited in sensitivity and interpretability.
  • Structural MRI is a key modality for observing neurodegeneration in AD.

Purpose of the Study:

  • To introduce and validate the Structural MRI-based Alzheimer's Disease Score (SMAS), a novel deep learning index.
  • To assess SMAS's association with cognitive function, age, and brain morphometry.
  • To evaluate SMAS's utility in early AD detection and longitudinal monitoring.

Main Methods:

  • Development of a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE) to create the SMAS index.
  • Utilized baseline structural MRI data from the DELCODE cohort.
  • Longitudinal validation in independent DELCODE and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts.

    • Main Results:

    • SMAS demonstrated strong correlations with cognitive performance, age, hippocampal, and gray matter volumes across cohorts.
    • The SMAS index showed high accuracy in distinguishing healthy individuals from those with AD (AUC up to 0.971).
    • SMAS outperformed existing biomarkers (SPARE-AD, hippocampal volume) in longitudinal tracking over 36 months.

    Conclusions:

    • SMAS is a sensitive and interpretable biomarker reflecting AD-related brain atrophy.
    • SMAS shows significant potential for early AD detection and monitoring disease progression.
    • The relevance map analysis highlights SMAS's focus on key AD-affected brain regions.