ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis
This work addresses issues in functional brain analysis for computer-aided diagnosis, offering a novel method to enhance classification accuracy for autism spectrum disorder, though it appears incremental as it builds on existing transformer and KAN frameworks.
The paper tackles the problem of functional connectivity analysis for brain disorder diagnosis by proposing ABFR-KAN, a transformer-based network that incorporates Kolmogorov-Arnold Networks to mitigate structural bias and improve reliability, achieving state-of-the-art performance in autism spectrum disorder classification on the ABIDE I dataset.
Functional connectivity (FC) analysis, a valuable tool for computer-aided brain disorder diagnosis, traditionally relies on atlas-based parcellation. However, issues relating to selection bias and a lack of regard for subject specificity can arise as a result of such parcellations. Addressing this, we propose ABFR-KAN, a transformer-based classification network that incorporates novel advanced brain function representation components with the power of Kolmogorov-Arnold Networks (KANs) to mitigate structural bias, improve anatomical conformity, and enhance the reliability of FC estimation. Extensive experiments on the ABIDE I dataset, including cross-site evaluation and ablation studies across varying model backbones and KAN configurations, demonstrate that ABFR-KAN consistently outperforms state-of-the-art baselines for autism spectrum distorder (ASD) classification. Our code is available at https://github.com/tbwa233/ABFR-KAN.