ds-FCRN: three-dimensional dual-stream fully convolutional residual networks and transformer-based global-local feature learning for brain age prediction.

Yutong Wu, Chen Zhang, Xiangge Ma, Xinyu Zhu, Lan Lin, Miao Tian

Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, 100124, China. · pubmed

Paper

Abstract

The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks. The gray matter (GM) density maps obtained from T1 MRI data of 16,377 healthy participants aged 45 to 82 years from the UKB database were included in this study (mean age,

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Research summary

The paper claims to develop a deep learning model for accurately predicting brain age using MRI data. This research is relevant as it addresses brain aging, which is a fundamental aspect of longevity and age-related cognitive decline.

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