Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by varied social cognitive challenges and repetitive behavioral patterns. Identifying reliable brain imaging-based biomarkers for ASD has been a persistent challenge due to the spectrum's diverse symptomatology. Existing baselines in the field have made significant strides in this direction, yet there remains room for improvement in both performance and interpretability. We propose \emph{HyperGALE}, which builds upon the hypergraph by incorporating learned hyperedges and gated attention mechanisms. This approach has led to substantial improvements in the model's ability to interpret complex brain graph data, offering deeper insights into ASD biomarker characterization. Evaluated on the extensive ABIDE II dataset, \emph{HyperGALE} not only improves interpretability but also demonstrates statistically significant enhancements in key performance metrics compared to both previous baselines and the foundational hypergraph model. The advancement \emph{HyperGALE} brings to ASD research highlights the potential of sophisticated graph-based techniques in neurodevelopmental studies. The source code and implementation instructions are available at GitHub:https://github.com/mehular0ra/HyperGALE.

翻译：自闭症谱系障碍（ASD）是一种以多样化社会认知挑战和重复性行为模式为特征的神经发育性疾病。由于该谱系的症状多样性，识别基于脑影像的可靠ASD生物标志物一直是一项持久挑战。现有基线方法在该领域取得了显著进展，但在性能与可解释性方面仍有提升空间。我们提出的HyperGALE模型在超图基础上引入可学习超边与门控注意力机制，显著增强了模型对复杂脑图数据的解析能力，为ASD生物标志物表征提供了更深入的见解。在广泛使用的ABIDE II数据集上的评估表明，HyperGALE不仅提升了可解释性，相较于先前基线方法及基础超图模型，在关键性能指标上还表现出统计学显著改善。HyperGALE为ASD研究带来的进展凸显了先进图技术在神经发育研究中的潜力。源代码与实现指南已发布于GitHub:https://github.com/mehular0ra/HyperGALE。