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

From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling