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brainageR

Software for generating a brain-predicted age value from a raw T1-weighted MRI scan. This uses a Gaussian Processes regression, implemented in R, using the kernlab package.

The software takes raw T1-weighted MRI scans, then uses SPM12 for segmentation and normalisation. A slightly customised version FSL slicesdir is then used to generate a directory of PNGs and corresponding index.html file for quality controlling in a web browser. Finally, the normalised images and loaded into R using the RNfiti package, vectorised and grey matter, white matter and CSF vectors masked (using 0.3 in the average image from the brainageR-specific template, derived from n=200 scans, n=20 from each of the n=10 scanners) and combined. In version 2.0 Principal Components Analysis was run (using R's prcomp), and the top 80% of variance retained. This meant 435 PCs were included. The rotation matrix of the PCA is applied to any new data adn these 435 variables are then used to predict an age value with the trained model with kernlab. The entailed using a GPR with RBF kernel and default hyperparameters.

In 2022 a dockerised version of brainageR v2.1 was developed, using Octave instead of Matlab to run SPM12. If you prefer to use that instead of this repo, please see here: https://github.com/fprados/brainageR_dockerfile

Brain-age Model

The brainageR model for v2.1 was trained on n = 3377 healthy individuals (mean age = 40.6 years, SD = 21.4, age range 18-92 years) from seven publicly-available datasets, and tested on n = 857 (mean age = 40.1 years, SD = 21.8, age range 18-90 years). All participants included were healthy according to local study data. For example, OASIS participants were only included if they had a CDR score < 0.5. All data was visually quality control to ensure quality and accuracy of image processing. Demographics were error-checked, and exclusions made if age values were unavailable.

The model performance on the held-out test data (with random assignment to training and test) is as follows: Pearson's correlation between chronological age and brain-predicted age: r = 0.973, mean absolute error = 3.933 years, R^2 = 0.946. While a bias has been reported in terms of a correlation between chronological age and the brain-age difference, in this GPR model the correlation in the test set was r = -0.012. Hence, the model DOES NOT automatically correct predictions for a statistical dependency on chronological age. It is still recommend to use age as a covariate in future analysis that use brain-prediced age difference (brain-PAD) as the outcome measure.

For a plot of brain-predicted age and age please see figshare here

The model has been tested using an entirely independent data, CamCAN, which was not used for training. These data included n=611 people aged 18-90 years. Performance here was r = 0.947, MAE = 4.90 years. Interestingly, there was still a significant relationship between the brain-predicted age difference (brain-PAD, AKA gap, AKA delta) and chronological age, r = -0.379. This reiterates the importance of accounting for chronological age in subsequent analyses.

For a plot of brain-predicted age and age in CamCAN please see figshare here

Citations

While there is no specific journal article associated with the software, we have published several works using brainageR. So, if you use this software, please consider citing one or more of the following papers:

I am also hosting this package on Zenodo and it has it's own DOI and on OSF.

Since kernlab does most of the heavy lifting, please consider citing this excellent package: https://cran.r-project.org/web/packages/kernlab/citation.html

Prerequisites

Optional (but recommended) software

Usage

brainageR software version 2.0 24 Sep 2019

Required arguments: 
	-f: input Nifti file
	-o: output csv filename

Optional arguments:
	-d: debug mode - turns off clean-up
	-h: displays this help message

For example:
brainageR -f subj01_T1.nii -o subj01_brain_predicted.age.csv

Installation

First, create a directory to work in, i.e., brainageR, and cd into it. This will be your brainageR_dir.

mkdir brainageR
cd brainageR

Next, clone this repository into the folder software using following command:

git clone https://github.com/james-cole/brainageR.git software

Currently, this Github repo is missing three crucial files (pca_center.rds,pca_rotation.rds,pca_scale.rds). These are files created by running PCA on the training data, and are necessary for applying to new data. These files are close to 2GB and over the limit for non-premium Github LFS. You can get these from the v2.1 Releases page. It's also available on Zenodo or OSF. Download those three files and put them into the software directory. The directory should now look like:

brainageR/
└── software/
    ├── templates/
    ├── GPR_model_gm_wm_csf.RData
    ├── LICENSE
    ├── README.md
    ├── all_BANC_2019.csv
    ├── brainageR
    ├── brains.train_labels.csv
    ├── collate_brain_ages.sh
    ├── generate_submit_scripts.sh
    ├── predict_new_data_gm_wm_csf.R
    ├── sge_submit_template.sh
    ├── slicesdir.brainageR
    ├── slurm_submit_template.sh
    ├── spm_preprocess_brainageR.m
    ├── submit_template.sh
    ├── pca_center.rds
    ├── pca_rotation.rds
    └── pca_scale.rds

Once you have the software files, you need to edit the brainageR script located in brainageR/software/ to set the brainageR_dir. Further, add the full pathway to your local installation of SPM12, your MATLAB binary, and your FSL directory. This is what is currently in there, so please edit accordingly:

brainageR_dir=/home/jcole/brain_age/BRAIN_AGE_T1/brainageR/
spm_dir=/apps/matlab_toolboxes/spm12/
matlab_path=/Applications/MATLAB_R2017b.app/bin/matlab
FSLDIR=/usr/local/fsl/

For ease, you might then want to add the software directory to your path environmental variable.

Notes

The software works on a single T1-weighted MRI scan in uncompressed Nifti format (e.g., subj01_T1.nii). It can run locally or using an HPC cluster environment. To submit to an HPC queue manager (e.g., SGE, SLURM) you can use one the supplied templates (e.g., submit_template.sh). Simply edit this script to fit your local environment, which will depend on how your sysadmin has configured MATLAB and R to run on your grid. You can then use the generate_submit_scripts.sh utility to create multiple versions of your tailored submit script, one per nifti file. Then use a for loop to submit these multiple scripts to the queue manager.

Since the software is designed to run on single Nifti files, an output file is created for each Nifti. You can use the collate_brain_ages.sh utility to combine multiple output.csv files from within a single director.

Example usage for single Nifti: brainageR -f subj01_T1.nii -o subj01_brain.predicted_age.csv

Example usage for multiple Niftis in one directory:

ls T1_dir/

subj01_T1.nii
subj02_T1.nii
subj03_T1.nii

cd T1_dir/

ls *nii > file_list.txt

generate_submit_scripts file_list.txt /apps/brainageR/software/sge_submit_template.sh

ls *sh

subj01_T1_submit_template.sh
subj02_T1_submit_template.sh
subj03_T1_submit_template.sh

for i in *submit_script.sh; do
	qsub $i
done