A new artificial intelligence model measures how fast a patient's brain is aging and could be a powerful new tool for understanding, preventing and treating cognitive decline and dementia, according to USC researchers.
Unlike traditional cross-sectional approaches, which estimate brain age from one scan at a single time point, this longitudinal method compares baseline and follow-up MRI scans from the same individual. As a result, it more accurately pinpoints neuroanatomic changes tied to accelerated or decelerated aging.
The 3D-CNN also generates interpretable “saliency maps,” which indicate the specific brain regions that are most important for determining the pace of aging.
When applied to a group of 104 cognitively healthy adults and 140 Alzheimer's disease patients, the new model's calculations of brain aging speed closely correlated with changes in cognitive function tests given at both time points.
The alignment of these measures with cognitive test results indicates that the framework may serve as an early biomarker of neurocognitive decline.
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