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

arXiv: https://arxiv.org/abs/2302.05304 · HTML: https://mlsanity.com/paper/2302.05304/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [MAPIE: an open-source library for distribution-free uncertainty quantification](https://mlsanity.com/paper/2207.12274/) (2022)
- [Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data](https://mlsanity.com/paper/1808.06452/) (2018)
- [PHOTONAI -- A Python API for Rapid Machine Learning Model Development](https://mlsanity.com/paper/2002.05426/) (2020)
- [An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling](https://mlsanity.com/paper/2107.07977/) (2021)

## Built on by (most cited first, 1 of 1)

- [Uncertainty quantification in metric spaces](https://mlsanity.com/paper/2405.05110/) (2024)

## Abstract (arXiv)

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.