Identifying Five Dominant Dimensions of Neurodegeneration in Brain Aging through Deep Learning: Correlations with Clinical and Genetic Measures.

Zhijian Yang, Junhao Wen, Guray Erus, Christos Davatzikos

Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadephia, PA, USA · pubmed

Paper

Abstract

Brain aging is a complex process influenced by various genetic, lifestyle, and environmental factors, as well as by age‐related and often co‐existing pathologies. MRI and AI methods have been instrumental in understanding neuroanatomical changes that occur during aging in large and diverse populations.

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Research summary

The paper addresses neurodegeneration in the context of brain aging, utilizing deep learning to identify dimensions of this process. While it contributes to understanding the mechanisms of aging and correlates with clinical and genetic measures, it primarily focuses on identifying patterns rather than addressing the root causes of aging or proposing interventions for lifespan extension. Thus, it represents solid research but has limited impact on the broader field of longevity research.

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