Brain age prediction based on brain region volume modeling under broad network field of view.

Jianjie Zheng, Junkai Wang, Zeyin Zhang, Kuncheng Li, Huimin Zhao, Peipeng Liang

School of Psychology, Capital Normal University, Beijing, 100048, China. Electronic address: [email protected] of Psychology, Capital Normal University, Beijing, 100048, China. Electronic address: [email protected]. · pubmed

Paper

Abstract

Brain region volume from Structural Magnetic Resonance Imaging (sMRI) can directly reflect abnormal states in brain aging. While promising for clinical brain health assessment, existing volume-based brain age prediction methods fail to explore both linear and nonlinear relationships, resulting in weak representation and suboptimal estimates.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The paper claims to improve brain age prediction by modeling brain region volume relationships. This research is relevant as it addresses brain aging, which is a fundamental aspect of longevity and age-related health assessments.

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