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Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

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Alzheimer's Imaging Consortium.

Vladimir S Fonov1, Pedro Rosa-Neto1,2, D Louis Collins1,3

  • 1McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 23, 2025
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Summary
This summary is machine-generated.

This study predicts Alzheimer's disease (AD) progression risk by analyzing brain structure volumes against healthy aging models. The method effectively identifies individuals at high risk for AD conversion using neuroimaging data.

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Area of Science:

  • Neuroimaging
  • Alzheimer's Disease Research
  • Computational Neuroscience

Background:

  • Predicting dementia risk is crucial for early Alzheimer's disease (AD) management.
  • Brain structure volumes deviate from normal trajectories in individuals with AD.
  • Analyzing deviations from expected age and sex-based volumetric measurements can predict AD progression risk.

Purpose of the Study:

  • To predict individual Alzheimer's disease progression risk.
  • To analyze deviations from age and sex-based volumetric measurements of brain structures.
  • To identify individuals at high risk for AD conversion.

Main Methods:

  • Utilized T1w MRI scans from multiple large-scale databases.
  • Modeled healthy aging trajectories for 70 brain regions using Bayesian multilevel models and cubic b-splines.
  • Employed a linear Survival Support Vector Machine with permutation importance to predict time-to-event for AD conversion.

Main Results:

  • Achieved a concordance index of 0.82(0.04) in predicting AD conversion risk.
  • Identified baseline diagnosis (Cognitively Normal or Mild Cognitive Impairment) as a key predictor.
  • Highlighted the importance of baseline volumes in the Amygdala, Inferior lateral ventricles, Superior temporal gyrus, and Superior frontal gyrus.

Conclusions:

  • Developed a library of healthy aging trajectories for 70 anatomical structures.
  • Demonstrated the effectiveness of the method in identifying subjects at high risk of progressing to Alzheimer's Dementia.
  • The approach offers a novel way to assess AD progression risk using neuroimaging data.