Deep learning to quantify the pace of brain aging in relation to neurocognitive changes
Neurocognitive
Brain Aging
Longitudinal Study
Aging brain
DOI:
10.1073/pnas.2413442122
Publication Date:
2025-02-24T20:09:01Z
AUTHORS (941)
ABSTRACT
Brain age (BA), distinct from chronological (CA), can be estimated MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative since birth. Thus, it conveys poorly recent or contemporaneous trends, which better quantified by the (temporal) pace P of brain aging. Many approaches map , rely on quantifying DNA methylation whole-blood cells, blood–brain barrier separates neural cells. We introduce three-dimensional convolutional network (3D-CNN) estimate noninvasively longitudinal MRI. Our model (LM) trained 2,055 CN adults, validated 1,304 and further applied an independent cohort 104 adults 140 patients with Alzheimer’s disease (AD). In its test set, LM computes mean absolute error (MAE) 0.16 y (7% error). This significantly outperforms most accurate model, whose MAE 1.85 has 83% error. By synergizing interpretable CNN saliency approach, we anatomic variations regional rates differ according sex, decade life, neurocognitive status. estimates are associated changes cognitive functioning across domains. underscores LM’s ability way captures relationship between research complements existing strategies for AD risk assessment individuals’ adverse change age.
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