HyperGALE: ASD Classification via Hypergraph Gated Attention with
  Learnable Hyperedges
Mehul Arora Chirag Shantilal Jain Lalith Bharadwaj Baru Kamalaker Dadi Bapi Raju Surampudi
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.