  Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect
spontaneous neural activities in brain and is widely used for brain disorder
analysis.Previous studies propose to extract fMRI representations through
diverse machine/deep learning methods for subsequent analysis. But the learned
features typically lack biological interpretability, which limits their
clinical utility. From the view of graph theory, the brain exhibits a
remarkable modular structure in spontaneous brain functional networks, with
each module comprised of functionally interconnected brain regions-of-interest
(ROIs). However, most existing learning-based methods for fMRI analysis fail to
adequately utilize such brain modularity prior. In this paper, we propose a
Brain Modularity-constrained dynamic Representation learning (BMR) framework
for interpretable fMRI analysis, consisting of three major components: (1)
dynamic graph construction, (2) dynamic graph learning via a novel
modularity-constrained graph neural network(MGNN), (3) prediction and biomarker
detection for interpretable fMRI analysis. Especially, three core
neurocognitive modules (i.e., salience network, central executive network, and
default mode network) are explicitly incorporated into the MGNN, encouraging
the nodes/ROIs within the same module to share similar representations. To
further enhance discriminative ability of learned features, we also encourage
the MGNN to preserve the network topology of input graphs via a graph topology
reconstruction constraint. Experimental results on 534 subjects with rs-fMRI
scans from two datasets validate the effectiveness of the proposed method. The
identified discriminative brain ROIs and functional connectivities can be
regarded as potential fMRI biomarkers to aid in clinical diagnosis.
