  Major depressive disorder (MDD) is a common mental disorder that typically
affects a person's mood, cognition, behavior, and physical health.
Resting-state functional magnetic resonance imaging (rs-fMRI) data are widely
used for computer-aided diagnosis of MDD. While multi-site fMRI data can
provide more data for training reliable diagnostic models, significant
cross-site data heterogeneity would result in poor model generalizability. Many
domain adaptation methods are designed to reduce the distributional differences
between sites to some extent, but usually ignore overfitting problem of the
model on the source domain. Intuitively, target data augmentation can alleviate
the overfitting problem by forcing the model to learn more generalized features
and reduce the dependence on source domain data. In this work, we propose a new
augmentation-based unsupervised cross-domain fMRI adaptation (AUFA) framework
for automatic diagnosis of MDD. The AUFA consists of 1) a graph representation
learning module for extracting rs-fMRI features with spatial attention, 2) a
domain adaptation module for feature alignment between source and target data,
3) an augmentation-based self-optimization module for alleviating model
overfitting on the source domain, and 4) a classification module. Experimental
results on 1,089 subjects suggest that AUFA outperforms several
state-of-the-art methods in MDD identification. Our approach not only reduces
data heterogeneity between different sites, but also localizes disease-related
functional connectivity abnormalities and provides interpretability for the
model.
