  In this work, we focus on the challenging task, neuro-disease classification,
using functional magnetic resonance imaging (fMRI). In population graph-based
disease analysis, graph convolutional neural networks (GCNs) have achieved
remarkable success. However, these achievements are inseparable from abundant
labeled data and sensitive to spurious signals. To improve fMRI representation
learning and classification under a label-efficient setting, we propose a novel
and theory-driven self-supervised learning (SSL) framework on GCNs, namely
Graph CCA for Temporal self-supervised learning on fMRI analysis GATE.
Concretely, it is demanding to design a suitable and effective SSL strategy to
extract formation and robust features for fMRI. To this end, we investigate
several new graph augmentation strategies from fMRI dynamic functional
connectives (FC) for SSL training. Further, we leverage canonical-correlation
analysis (CCA) on different temporal embeddings and present the theoretical
implications. Consequently, this yields a novel two-step GCN learning procedure
comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning
on a small labeled fMRI dataset for a classification task. Our method is tested
on two independent fMRI datasets, demonstrating superior performance on autism
and dementia diagnosis.

Machine Learning;Image and Video Processing