The brain-age gap is one of the most investigated risk markers for brain changes across disorders. While the field is progressing towards large-scale models, recently incorporating uncertainty estimates, no model to date provides the single-subject risk assessment capability essential for clinical application. In order to enable the clinical use of brain-age as a biomarker, we here combine uncertainty-aware deep Neural Networks with conformal prediction theory. This approach provides statistical guarantees with respect to single-subject uncertainty estimates and allows for the calculation of an individual's probability for accelerated brain-aging. Building on this, we show empirically in a sample of N=16,794 participants that 1. a lower or comparable error as state-of-the-art, large-scale brain-age models, 2. the statistical guarantees regarding single-subject uncertainty estimation indeed hold for every participant, and 3. that the higher individual probabilities of accelerated brain-aging derived from our model are associated with Alzheimer's Disease, Bipolar Disorder and Major Depressive Disorder.

翻译：脑龄差是跨疾病脑变化研究中被广泛探讨的风险标志物之一。尽管该领域正朝着大规模模型发展，并近期引入了不确定性估计，但目前尚无模型具备临床应用所必需的个体风险评估能力。为促进脑龄作为生物标志物的临床应用，本文创新性地将不确定性感知深度神经网络与共形预测理论相结合。该方法为个体不确定性估计提供统计保证，并允许计算个体加速脑老化的概率。在此基础上，我们在N=16,794名参与者的样本中通过实证表明：1. 相比最先进的大规模脑龄模型，本方法误差更低或相当；2. 关于个体不确定性估计的统计保证确实适用于每位参与者；3. 本模型推导出的加速脑老化个体概率升高与阿尔茨海默病、双相情感障碍及重度抑郁障碍存在关联。