BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis

Welcome to the code repository of BrainAgeNeXt, a novel deep learning approach to predict brain age from T1-weighted MRI scans acquired at any magnetic field strength.

Table of Contents

• Introduction
• Installation
• Usage
• References
• License

Introduction

BrainAgeNeXt is a deep learning model designed to predict brain age with high accuracy across different MRI scanning conditions. The model builds on the MedNeXt framework [2], inspired by the ConvNeXt blocks [3].

Installation

To get started install all requirements of the MedNeXt repository. Next..

Usage

First, preprocess all images by performing skull stripping on the T1-weighted MRI scans (SynthSeg from Freesurfer is the preferred tool), followed by an affine registration to the MNI 152 standard space using ANTs.

References

Please cite the following papers if using any code from this project:

1. La Rosa, F. et al. (2024). BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis. medRxiv. https://doi.org/10.1101/2024.08.10.24311686

2. Roy, S. et, al (2023). Mednext: transformer-driven scaling of convnets for medical image segmentation. MICCAI. https://rdcu.be/dRt53

3. Liu, Z. et al. (2022). A convnet for the 2020s. arXiv. https://doi.org/10.48550/arXiv.2201.03545

License

This repository, FrancescoLR/BrainAgeNeXt, is licensed under the Apache License 2.0. This means you are free to use, modify, and distribute the code, provided that you include a copy of the license in any distributed version of the project and comply with its terms. For more details, please refer to the LICENSE file in this repository.