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Generating MRI-Derived CSF Proxy-Markers to Predict and Visualize Alzheimer's Disease Progression.

Anees Abrol1, Vince D Calhoun1, 1

  • 1Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.

Human Brain Mapping
|October 28, 2025
PubMed
Summary

Detecting Alzheimer's disease (AD) early is key for drug trials. New AI models use MRI scans to predict AD risk, avoiding invasive tests and improving patient screening.

Keywords:
Alzheimer's disease progressionCSF proxy‐markersCSF surrogate markersamyloid plaquesdeep learningearly diagnosissMRItau tangles

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Biomarker Discovery

Background:

  • Early Alzheimer's disease (AD) detection is vital for clinical trials and patient benefit.
  • Current methods often involve invasive procedures like lumbar punctures or radiopharmaceutical injections.
  • Noninvasive neuroimaging offers a safer alternative for detecting AD biomarkers and understanding disease progression.

Purpose of the Study:

  • To develop a noninvasive method for predicting Alzheimer's disease (AD) risk using structural MRI (sMRI).
  • To train neural networks to generate sMRI-based representations as proxies for cerebrospinal fluid (CSF) biomarker status.
  • To assess the clinical utility of this approach for AD screening, diagnosis, and progression prediction.

Main Methods:

  • Utilizing neural networks to analyze structural MRI (sMRI) data.
  • Generating latent sMRI representations as proxies for cerebrospinal fluid (CSF) biomarker status.
  • Applying these models for classification, prediction, and risk staging in Alzheimer's disease.

Main Results:

  • Neural networks successfully generated predictive sMRI representations for AD biomarkers.
  • Key brain regions including the amygdala, hippocampus, and cingulate gyrus showed high prognostic value for AD risk.
  • The approach demonstrated potential for screening, diagnosis, and predicting AD progression.

Conclusions:

  • AI-driven analysis of sMRI can serve as a noninvasive proxy for AD biomarkers.
  • This method aids in early AD detection, patient stratification for clinical trials, and predicting disease progression.
  • Noninvasive neuroimaging offers a promising avenue for improving Alzheimer's disease management and research.