Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.

Chenzhong Yin, Phoebe Imms, Nahian F Chowdhury, Nikhil N Chaudhari, Heng Ping, Haoqing Wang, Paul Bogdan, Andrei Irimia

Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089.Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089. · pubmed

Paper

Abstract

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace

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Research summary

The paper claims to quantify the pace of brain aging using deep learning techniques. This research is relevant as it explores neuroanatomic aging, which is a fundamental aspect of understanding the aging process and its implications for longevity and cognitive health.

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