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

Jan 1, 2023·
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
· 0 min read
Abstract
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.
Type
Publication
arXiv