Abstract
Functional connectivity analysis is widely used for computer-aided brain disorder diagnosis, but conventional atlas- based approaches can introduce structural bias and overlook subject-specific brain function. Addressing this, we propose Advanced Brain Function Representation with Kolmogorov-Arnold Networks (ABFR-KAN), a transformer- based framework that combines anatomically adaptive brain function representation components with the power of Kolmogorov–Arnold Networks (KANs) to mitigate structural bias, improve anatomical conformity, and improve the reliability of FC estimation. We evaluate the proposed method on resting-stage functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) I and II datasets (281 subjects and 211 subjects, respectively). Extensive experiments, including 5-fold cross validation, 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 disorder (ASD) classification. These results highlight the potential of combining advanced brain function representation strategies with KAN-based architectures for robust neuroimaging analysis. Our code is available at https://github.com/tbwa233/ABFR-KAN