  Neuroimaging-driven prediction of brain age, defined as the predicted
biological age of a subject using only brain imaging data, is an exciting
avenue of research. In this work we seek to build models of brain age based on
functional connectivity while prioritizing model interpretability and
understanding. This way, the models serve to both provide accurate estimates of
brain age as well as allow us to investigate changes in functional connectivity
which occur during the ageing process. The methods proposed in this work
consist of a two-step procedure: first, linear latent variable models, such as
PCA and its extensions, are employed to learn reproducible functional
connectivity networks present across a cohort of subjects. The activity within
each network is subsequently employed as a feature in a linear regression model
to predict brain age. The proposed framework is employed on the data from the
CamCAN repository and the inferred brain age models are further demonstrated to
generalize using data from two open-access repositories: the Human Connectome
Project and the ATR Wide-Age-Range.
