# A voxel-level approach to brain age prediction: A method to assess regional brain aging

arXiv: https://arxiv.org/abs/2310.11385 · HTML: https://mlsanity.com/paper/2310.11385/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [How Does Batch Normalization Help Optimization?](https://mlsanity.com/paper/1805.11604/) (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)
- [Global-Local Transformer for Brain Age Estimation](https://mlsanity.com/paper/2109.01663/) (2021)
- [Deep learning for neuroimaging: a validation study](https://mlsanity.com/paper/1312.5847/) (2013)
- [Patch-based Brain Age Estimation from MR Images](https://mlsanity.com/paper/2008.12965/) (2020)
- [Voxel-level Importance Maps for Interpretable Brain Age Estimation](https://mlsanity.com/paper/2108.05388/) (2021)

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

- [Deeply Supervised Multi-Task Autoencoder for Biological Brain Age estimation using three dimensional T1-weighted magnetic resonance imaging](https://mlsanity.com/paper/2508.01565/) (2025)
- [MRI-Based Brain Age Estimation with Supervised Contrastive Learning of Continuous Representation](https://mlsanity.com/paper/2511.22102/) (2025)
- [SurfAge-Net: A Hierarchical Surface-Based Network for Interpretable Fine-Grained Brain Age Prediction](https://mlsanity.com/paper/2602.06994/) (2026)

## Abstract (arXiv)

Brain aging is a regional phenomenon, a facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized brain age estimates that can provide granular insights into the regional aging processes. This is essential to understand the differences in aging trajectories in healthy versus diseased subjects. In this work, a deep learning-based multitask model is proposed for voxel-level brain age prediction from T1-weighted magnetic resonance images. The proposed model outperforms the models existing in the literature and yields valuable clinical insights when applied to both healthy and diseased populations. Regional analysis is performed on the voxel-level brain age predictions to understand aging trajectories of known anatomical regions in the brain and show that there exist disparities in regional aging trajectories of healthy subjects compared to ones with underlying neurological disorders such as Dementia and more specifically, Alzheimer's disease. Our code is available at https://github.com/nehagianchandani/Voxel-level-brain-age-prediction.