Brain age regression using deep learning

Predict age from brain grey matter (regression) using Deep Learning. Aging is associated with grey matter (GM) atrophy. Each year, an adult lose 0.1% of GM. We will try to learn a predictor of the chronological age (true age) using GM measurements on a population of healthy control participants.

Such a predictor provides the expected brain age of a subject. Deviation from this expected brain age indicates acceleration or slowdown of the aging process which may be associated with a pathological neurobiological process or protective factor of aging.

Dataset

There are 357 samples in the training set and 90 samples in the test set.

Input data

Voxel-based_morphometry VBM using cat12 software which provides:

• Regions Of Interest (rois) of Grey Matter (GM) scaled for the Total Intracranial Volume (TIV): [train|test]_rois.csv 284 features.

• VBM GM 3D maps or images (vbm3d) of voxels in the MNI space: [train|test]_vbm.npz contains 3D images of shapes (121, 145, 121). This npz contains also the 3D mask and the affine transformation to MNI referential. Masking the brain provides flat 331 695 input features (masked voxels) for each participant.

By default problem.get_[train|test]_data() return the concatenation of 284 ROIs of Grey Matter (GM) features with 331 695 features (voxels) within a brain mask. Those two blocks are higly redundant. To select only rois features do:

X[:, :284]

To select only vbm features do:

X[:, 284:]

Target

The target can be found in [test|train]_participants.csv files, selecting the age column for regression problem.

Evaluation metrics

sklearn metrics

The main Evaluation metrics is the Root-mean-square deviation RMSE. We will also look at the R-squared R2.

Links

• RAMP-workflow’s documentation
• RAMP-workflow’s github
• RAMP Kits

Installation

This starting kit requires Python and the following dependencies:

• numpy
• scipy
• pandas
• scikit-learn
• matplolib
• seaborn
• jupyter
• torch
• ramp-workflow

You can install the dependencies with the following command-line:

pip install -U -r requirements.txt

If you are using conda, we provide an environment.yml file for similar usage.

conda env create -n brain_age -f environment.yml

Then, you can activate/deasactivate the conda environment using:

conda activate brain_age
conda deactivate

1. Download the data

python download_data.py

The train/test data will be available in the data directory.

2. Execute the jupyter notebook

jupyter notebook brain_age_starting_kit.ipynb

Play with this notebook to create your new model.

3. Test Submission

The submissions need to be located in the submissions folder. For instance to create a linear_regression_rois submission, start by copying the starting kit

cp -r submissions/submissions/strating_kit submissions/submissions/linear_regression_rois.

Tune the estimator in thesubmissions/submissions/linear_regression_rois/estimator.py file. This file must contain a function get_estimator() that returns a scikit learn Pipeline.

Then, test your submission locally:

ramp-test --submission linear_regression_rois

4. Submission on N4H RAMP

Connect to your N4H RAMP account, select the brain_age_deep event, and submit your estimator in the sandbox section. x Note that no training will be performed on the server side. You thus need to join the weights of the model in a file called weights.pth.