  Machine learning analysis of longitudinal neuroimaging data is typically
based on supervised learning, which requires a large number of ground-truth
labels to be informative. As ground-truth labels are often missing or expensive
to obtain in neuroscience, we avoid them in our analysis by combing factor
disentanglement with self-supervised learning to identify changes and
consistencies across the multiple MRIs acquired of each individual over time.
Specifically, we propose a new definition of disentanglement by formulating a
multivariate mapping between factors (e.g., brain age) associated with an MRI
and a latent image representation. Then, factors that evolve across
acquisitions of longitudinal sequences are disentangled from that mapping by
self-supervised learning in such a way that changes in a single factor induce
change along one direction in the representation space. We implement this
model, named Longitudinal Self-Supervised Learning (LSSL), via a standard
autoencoding structure with a cosine loss to disentangle brain age from the
image representation. We apply LSSL to two longitudinal neuroimaging studies to
highlight its strength in extracting the brain-age information from MRI and
revealing informative characteristics associated with neurodegenerative and
neuropsychological disorders. Moreover, the representations learned by LSSL
facilitate supervised classification by recording faster convergence and higher
(or similar) prediction accuracy compared to several other representation
learning techniques.
