  Contrastive self-supervised learning has recently benefited fMRI
classification with inductive biases. Its weak label reliance prevents
overfitting on small medical datasets and tackles the high intraclass
variances. Nonetheless, existing contrastive methods generate resemblant pairs
only on pixel-level features of 3D medical images, while the functional
connectivity that reveals critical cognitive information is under-explored.
Additionally, existing methods predict labels on individual contrastive
representation without recognizing neighbouring information in the patient
group, whereas interpatient contrast can act as a similarity measure suitable
for population-based classification. We hereby proposed contrastive functional
connectivity graph learning for population-based fMRI classification.
Representations on the functional connectivity graphs are "repelled" for
heterogeneous patient pairs meanwhile homogeneous pairs "attract" each other.
Then a dynamic population graph that strengthens the connections between
similar patients is updated for classification. Experiments on a multi-site
dataset ADHD200 validate the superiority of the proposed method on various
metrics. We initially visualize the population relationships and exploit
potential subtypes.
