  Despite the remarkable success achieved by graph convolutional networks for
functional brain activity analysis, the heterogeneity of functional patterns
and the scarcity of imaging data still pose challenges in many tasks.
Transferring knowledge from a source domain with abundant training data to a
target domain is effective for improving representation learning on scarce
training data. However, traditional transfer learning methods often fail to
generalize the pre-trained knowledge to the target task due to domain
discrepancy. Self-supervised learning on graphs can increase the
generalizability of graph features since self-supervision concentrates on
inherent graph properties that are not limited to a particular supervised task.
We propose a novel knowledge transfer strategy by integrating meta-learning
with self-supervised learning to deal with the heterogeneity and scarcity of
fMRI data. Specifically, we perform a self-supervised task on the source domain
and apply meta-learning, which strongly improves the generalizability of the
model using the bi-level optimization, to transfer the self-supervised
knowledge to the target domain. Through experiments on a neurological disorder
classification task, we demonstrate that the proposed strategy significantly
improves target task performance by increasing the generalizability and
transferability of graph-based knowledge.
