This repository contains scripts to enable readers to run the trained models presented in Wood et al., Accurate brain-age models for routine clinical MRI examinations, 2022, NeuroImage. The models were trained on > 23,000 axial T2-weighted head MRI scans from two large UK hospitals, and demonstrate accurate, generalisable brain-age prediction:

The code requires the data to be in Nifti file format (.nii or .nii.gz extension) and makes use of the Project Monai library. This repository is compatible with python 3.6. See requirements.txt for all prerequisites; you can also install them using the following command:

pip install -r requirements.txt

Running our models is straightforward. All that is needed is a .csv file with the following two columns:

• 'ID' (which is a unique identifier for each participant/scanning session e.g., 'pat119' etc.).

• 'file_name' (which gives the absolute path to the Nifti file for each participant).

Optionally, users can also provide a third column named 'Age', which gives the chronological age of each participant in years, in order to generate performance metrics (e.g., mean absolute error (MAE)) and scatter plots).

Brain-age prediction can then be performed using the following command:

python run.py --project_name NAME --csv_file /PATH/TO/CSV/FILE

This will save a .csv file within the local cloned repository (./NAME_output.csv) with the brain-predicted ages for each subject. If a scatter-plot is required, run.py should be called with the additional argument --return_metrics

By default, our model will run on a cpu. If a GPU is available, run.py should be called with the additional argument --gpu (in this case, run time is <2 seconds per scan):

python run.py --project_name NAME --csv_file /PATH/TO/CSV/FILE --gpu

By default, run.py assumes axial T2-weighted scans are provided. If instead axial diffusion-weighted scans are provided, then run.py should be called with the additional argument --sequence dwi to use our DWI model:

python run.py --project_name NAME --csv_file /PATH/TO/CSV/FILE --sequence dwi

Please note that our model only provides meaningful brian-age predictions for scans that are oriented in the 'LPS' coordinate system (i.e., right to Left, anterior to Posterior, inferior to Superior). For this reason, run.py automatically reorients scans to this coordinate system.

To reproduce the results on the open-access IXI dataset, first download (and unzip) the axial T2-weighted scans and associated .csv file here. Brain-age prediction can then be performed using the following command:

python run.py --ixi --project_name IXI --csv_file /home/dw19/Downloads/IXI_file.xls --ixi_nii_dir /PATH/TO/IXI/NII/FILES --return_metrics

This will save a .csv file with brain-predicted ages for each IXI participant, along with the following scatter plot:

Note that /PATH/TO/IXI/NII/FILES must be the path to the extracted files after unzipping 'IXI-T2.tar', and should contain files such as 'IXI002-Guys-0828-T2.nii.gz','IXI012-HH-1211-T2.nii.gz' etc.

We will be releasing our 'skull-stripped' model which takes as input axial T2-weighted scans which have had non-brain-tissue removed. We will also be releaseing our volumetric T1-weighted models (raw and skull-stripped).

If you found this repository useful, please consider citing our work:

  title={Accurate brain-age models for routine clinical MRI examinations},
  author={Wood, David A and Kafiabadi, Sina and Al Busaidi, Ayisha and Guilhem, Emily and Montvila, Antanas and Lynch, Jeremy and Townend, Matthew and Agarwal, Siddharth and Mazumder, Asif and Barker, Gareth J and others},
  journal={NeuroImage},
  pages={118871},
  year={2022},
  publisher={Elsevier}
}```