Community-Aware Transformer for Autism Prediction in fMRI Connectome

• URL: http://arxiv.org/abs/2307.10181v1
• Date: Sat, 24 Jun 2023 23:52:57 GMT
• Title: Community-Aware Transformer for Autism Prediction in fMRI Connectome
• Authors: Anushree Bannadabhavi and Soojin Lee and Wenlong Deng and Xiaoxiao Li
• Abstract summary: Com-BrainTF is a hierarchical local-global transformer architecture that learns intra and inter-community aware node embeddings for ASD prediction task. Our model outperforms state-of-the-art (SOTA) architecture on ABIDE dataset and has high interpretability, evident from the attention module.
• Score: 12.433556885503243
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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