# An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling

arXiv: https://arxiv.org/abs/2107.07977 · HTML: https://mlsanity.com/paper/2107.07977/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker](https://mlsanity.com/paper/1612.02572/) (2016)
- [Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression](https://mlsanity.com/paper/1912.06686/) (2019)

## Built on by (most cited first, 3 of 3)

- [Using Sequential Statistical Tests for Efficient Hyperparameter Tuning](https://mlsanity.com/paper/2112.12438/) (2021)
- [From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling](https://mlsanity.com/paper/2302.05304/) (2023)
- [Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning](https://mlsanity.com/paper/2605.28990/) (2026)

## Abstract (arXiv)

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research. However, Machine Learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared due to data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression (MCCQR) Neural Network trained on N=10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared to existing models across ten recruitment centers and in three independent validation samples (N=4,004). In two examples, we demonstrate that it prevents spurious associations and increases power to detect accelerated brain-aging. We make the pre-trained model publicly available.