Paper ID: 2302.05304

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

Jan Ernsting, Nils R. Winter, Ramona Leenings, Kelvin Sarink, Carlotta B. C. Barkhau, Lukas Fisch, Daniel Emden, Vincent Holstein, Jonathan Repple, Dominik Grotegerd, Susanne Meinert, NAKO Investigators, Klaus Berger, Benjamin Risse, Udo Dannlowski, Tim Hahn

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.

Submitted: Feb 10, 2023