Brain age modelling and prediction

This module supports training of a model to predict age from a set of IDPs or other measures. The model can then be used to determine a subject's 'brain age' from a set of the same measured, and hence a 'brain delta' that expresses how much older or younger their brain appears compared to their actual age.

Based on method described in Smith et al (Neuroimage 200 (2019) 528–539)

https://doi.org/10.1016/j.neuroimage.2019.06.017

Multiple models are defined in this paper including a simple model that has been shown to be biased when the model retains age dependence within the prediction. A corrected 'unbiased' model can be used to address this issue. In addition quadratic age dependence can be modelled and an alternate approach in which age is regarded as a predictor of IDPs rather than the other way round can be used.

Reproduction of simulation results can be found in the examples/ directory

User guide

Conceptually, brain age prediction is in two parts - first we train a model using known true ages and a set of features (typically IDPs or other metrics) from some group of subjects. Once trained, we can then use this model to predict the ages of a set of subjects given their true ages and values of the same features used to train the model.

Of course, in practice these two steps are often combined, we train the model and then use it to predict ages of the same set of subjects, and may then be interested in the comparing the predicted 'brain age' with the actual ages of the subjects.

However the set of subjects used for training and prediction does not have to be the same, this is used in the cross-validation analysis in the Smith paper (code to do this is also in the examples folder)

Command line interface

The command line interface is deltab

A typical usage would involve the following inputs:

• A file (e.g. TRUE_AGES.txt) containing a listing of true ages for subjects
• A second file (e.g. IDPS.txt) in TSV or CSV format containing the values of the features to use in training the model. The features in this file are the columns, each subject is a row

We can then run the prediction as:

deltab  --train-ages TRUE_AGES.txt --train-features IDPS.txt \
        --predict-model unbiased_quadratic --predict-output BRAIN_AGE.txt --predict age

Training options

--feature-nans : If set to median (default), any NaN values in the features for a subject are replace with the median value of this feature. If remove, subjects with any NaN value in the features are removed. Note that you will then want to use --true-ages-output to ensure that you have corresponding true ages to compare with excluding the removed subjects

--feature-proportion : Typically PCA reduction is performed on the feature set and not all features are retained for the prediction. This option specifies a proportion between 0 and 1 of features to be retained.

--feature-num : An alternative to feature-proportion, this option specifies an exact number of features to retain in PCA

--feature-var : Another alternative to feature-proportion, this retains features to explain the specified proportion of total variance

--kaiser-guttmann : Yet another alternative to feature-proportion, this uses the Kaiser-Guttmann criterion to select the number of features to retain

Prediction options

--predict-model : defines the model to use in prediction. For full details see Smith 2019:

• simple is a naive regression model that is shown in Smith 2019 to be biased when age dependence is present in the features
• unbiased removes age dependence from the features to eliminate the age bias in the simple model
• unbiased_quadratic also models quadratic dependence on true age
• alternate is an alternative approach in which age is viewed as a predictor of features rather than the other way round
• alternate_quadratic adds quadratic age dependence to the alternate model

--predict : Specifies what to output, age to output predicted age or delta to output the difference between true age and predicted age

--predict-output : Name of file to write predicted age/delta to

--true-ages-output : Name of file to write true ages used in prediction. Normally this would be identical to the true age input but if --feature-nans is set to remove, it will contain the ages only of the subjects that were included

--predict-ages : If the subjects to be predicted are not the same as those used to train the model, this file should contain the true ages of the prediction subjects

--predict-features : Similarly, when making predictions for a different set of subjects to those used in training, this file should be CSV or TSV in format and contain the feature values for the prediction subjects.

Other options

--save : If specified, the details of the trained model will be saved to a file that can be re-loaded and used for subsequent predictions

--load : If specified, skips the training step and instead loads a previously saved model

--overwrite : Overwrite existing output

--debug : Include debug output messages

Programmatic interface

See the examples folder for code samples showing how to use the brain age module in a Python program