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Under the framework of network-based neurodegeneration, brain functionalconnectome (FC)-based Graph Neural Networks (GNN) have emerged as a valuabletool for the diagnosis and prognosis of neurodegenerative diseases such asAlzheimer's disease (AD). However, these models are tailored for brain FC at asingle time point instead of characterizing FC trajectory. Discerning how FCevolves with disease progression, particularly at the predementia stages suchas cognitively normal individuals with amyloid deposition or individuals withmild cognitive impairment (MCI), is crucial for delineating disease spreadingpatterns and developing effective strategies to slow down or even halt diseaseadvancement. In this work, we proposed the first interpretable framework forbrain FC trajectory embedding with application to neurodegenerative diseasediagnosis and prognosis, namely Brain Tokenized Graph Transformer (BrainTokenGT). It consists of two modules: 1) Graph Invariant and Variant Embedding(GIVE) for generation of node and spatio-temporal edge embeddings, which weretokenized for downstream processing; 2) Brain Informed Graph TransformerReadout (BIGTR) which augments previous tokens with trainable type identifiersand non-trainable node identifiers and feeds them into a standard transformerencoder to readout. We conducted extensive experiments on two publiclongitudinal fMRI datasets of the AD continuum for three tasks, includingdifferentiating MCI from controls, predicting dementia conversion in MCI, andclassification of amyloid positive or negative cognitively normal individuals.Based on brain FC trajectory, the proposed Brain TokenGT approach outperformedall the other benchmark models and at the same time provided excellentinterpretability. The code is available athttps://github.com/ZijianD/Brain-TokenGT.git

Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embedding