Researchers developed an AI framework using resting-state fMRI to assess Alzheimer's risk along a continuum from SuperAgers to AD patients.
Key Details
- 1The Alzheimer’s Resilience-Vulnerability Connectome (ARC) uses resting-state fMRI data and machine learning.
- 2Model trained on 180 SuperAgers and 110 Alzheimer's patients, all age 75+.
- 3Validated on an independent cohort (n=143) and further tested on 818 cognitively unimpaired people and another independent cohort (n=497).
- 4ARC determines individual positions along the cognitive aging continuum between SuperAgers and AD diagnosis.
- 5Brain function patterns similar to AD predicted faster cognitive decline and more amyloid-β accumulation over 5–15 years.
Why It Matters
Accurate early risk stratification for Alzheimer’s disease could improve patient management and preventive strategies. The use of fMRI and machine learning highlights radiology’s role in innovating neurodegenerative disease prediction.
Source
EurekAlert
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