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.

翻译：功能连接分析是计算机辅助脑部疾病诊断的重要工具，传统上依赖于基于图谱的脑区分割。然而，此类分割方法可能带来选择偏差和忽视被试特异性等问题。为此，我们提出ABFR-KAN，这是一种基于Transformer的分类网络，它结合了新颖的高级脑功能表示组件与Kolmogorov-Arnold网络的强大能力，以减轻结构偏差、提升解剖学一致性并增强功能连接估计的可靠性。在ABIDE I数据集上进行的大量实验，包括跨站点评估以及针对不同模型主干和KAN配置的消融研究，均表明ABFR-KAN在自闭症谱系障碍分类任务上持续优于现有最先进的基线方法。我们的代码发布于https://github.com/tbwa233/ABFR-KAN。