STNAGNN: Spatiotemporal Node Attention Graph Neural Network for Task-based fMRI Analysis

• URL: http://arxiv.org/abs/2406.12065v1
• Date: Mon, 17 Jun 2024 20:08:05 GMT
• Title: STNAGNN: Spatiotemporal Node Attention Graph Neural Network for Task-based fMRI Analysis
• Authors: Jiyao Wang, Nicha C. Dvornek, Peiyu Duan, Lawrence H. Staib, Pamela Ventola, James S. Duncan,
• Abstract summary: We show that using task-driven data structures is effective for autism analysis. We propose a GNN-based task-based architecture and validate its performance in an autism task.
• Score: 9.35032090865023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Task-based fMRI uses actions or stimuli to trigger task-specific brain responses and measures them using BOLD contrast. Despite the significant task-induced spatiotemporal brain activation fluctuations, most studies on task-based fMRI ignore the task context information aligned with fMRI and consider task-based fMRI a coherent sequence. In this paper, we show that using the task structures as data-driven guidance is effective for spatiotemporal analysis. We propose STNAGNN, a GNN-based spatiotemporal architecture, and validate its performance in an autism classification task. The trained model is also interpreted for identifying autism-related spatiotemporal brain biomarkers.

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