  Learning-based methods have recently enabled performance leaps in analysis of
high-dimensional functional MRI (fMRI) time series. Deep learning models that
receive as input functional connectivity (FC) features among brain regions have
been commonly adopted in the literature. However, many models focus on
temporally static FC features across a scan, reducing sensitivity to dynamic
features of brain activity. Here, we describe a plug-in graph neural network
that can be flexibly integrated into a main learning-based fMRI model to boost
its temporal sensitivity. Receiving brain regions as nodes and
blood-oxygen-level-dependent (BOLD) signals as node inputs, the proposed
GraphCorr method leverages a node embedder module based on a transformer
encoder to capture temporally-windowed latent representations of BOLD signals.
GraphCorr also leverages a lag filter module to account for delayed
interactions across nodes by computing cross-correlation of windowed BOLD
signals across a range of time lags. Information captured by the two modules is
fused via a message passing algorithm executed on the graph, and enhanced node
features are then computed at the output. These enhanced features are used to
drive a subsequent learning-based model to analyze fMRI time series with
elevated sensitivity. Comprehensive demonstrations on two public datasets
indicate improved classification performance and interpretability for several
state-of-the-art graphical and convolutional methods that employ
GraphCorr-derived feature representations of fMRI time series as their input.
