  As a mental disorder progresses, it may affect brain structure, but brain
function expressed in brain dynamics is affected much earlier. Capturing the
moment when brain dynamics express the disorder is crucial for early diagnosis.
The traditional approach to this problem via training classifiers either
proceeds from handcrafted features or requires large datasets to combat the
$m>>n$ problem when a high dimensional fMRI volume only has a single label that
carries learning signal. Large datasets may not be available for a study of
each disorder, or rare disorder types or sub-populations may not warrant for
them. In this paper, we demonstrate a self-supervised pre-training method that
enables us to pre-train directly on fMRI dynamics of healthy control subjects
and transfer the learning to much smaller datasets of schizophrenia. Not only
we enable classification of disorder directly based on fMRI dynamics in small
data but also significantly speed up the learning when possible. This is
encouraging evidence of informative transfer learning across datasets and
diagnostic categories.
