  Autism spectrum disorder(ASD) is a lifelong neurodevelopmental condition that
affects social communication and behavior. Investigating functional magnetic
resonance imaging (fMRI)-based brain functional connectome can aid in the
understanding and diagnosis of ASD, leading to more effective treatments. The
brain is modeled as a network of brain Regions of Interest (ROIs), and ROIs
form communities and knowledge of these communities is crucial for ASD
diagnosis. On the one hand, Transformer-based models have proven to be highly
effective across several tasks, including fMRI connectome analysis to learn
useful representations of ROIs. On the other hand, existing transformer-based
models treat all ROIs equally and overlook the impact of community-specific
associations when learning node embeddings. To fill this gap, we propose a
novel method, Com-BrainTF, a hierarchical local-global transformer architecture
that learns intra and inter-community aware node embeddings for ASD prediction
task. Furthermore, we avoid over-parameterization by sharing the local
transformer parameters for different communities but optimize unique learnable
prompt tokens for each community. Our model outperforms state-of-the-art (SOTA)
architecture on ABIDE dataset and has high interpretability, evident from the
attention module. Our code is available at
https://github.com/ubc-tea/Com-BrainTF.
