  Introspection of deep supervised predictive models trained on functional and
structural brain imaging may uncover novel markers of Alzheimer's disease (AD).
However, supervised training is prone to learning from spurious features
(shortcut learning) impairing its value in the discovery process. Deep
unsupervised and, recently, contrastive self-supervised approaches, not biased
to classification, are better candidates for the task. Their multimodal options
specifically offer additional regularization via modality interactions. In this
paper, we introduce a way to exhaustively consider multimodal architectures for
contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.
We show that this multimodal fusion results in representations that improve the
results of the downstream classification for both modalities. We investigate
the fused self-supervised features projected into the brain space and introduce
a numerically stable way to do so.
