Whitmore Lucy, Beck Dani
Department of Psychology, University of Oregon, Eugene, OR, USA.
Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway.
Nat Commun. 2025 Aug 20;16(1):7771. doi: 10.1038/s41467-025-63222-7.
Advancements in computational techniques have enhanced our understanding of human brain development, particularly through high-dimensional data from magnetic resonance imaging (MRI). One notable approach is the brain-age prediction framework, which predicts biological age from neuroimaging data and calculates the brain age gap (BAG), a marker of deviation from chronological age. Most commonly applied to adult samples, this approach is now increasingly used in children and adolescents. However, several considerations must be taken into account when applying brain-age prediction in youth. In this Perspective, we outline important challenges and provide recommendations for researchers as well as future directions for the field.
计算技术的进步加深了我们对人类大脑发育的理解，特别是通过磁共振成像（MRI）的高维数据。一种值得注意的方法是脑龄预测框架，它从神经影像数据中预测生物年龄，并计算脑龄差距（BAG），这是一个偏离实际年龄的指标。这种方法最常用于成人样本，现在越来越多地应用于儿童和青少年。然而，在青少年中应用脑龄预测时必须考虑几个因素。在这篇观点文章中，我们概述了重要的挑战，并为研究人员提供了建议以及该领域的未来方向。
