ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis

Tyler Ward, Abdullah Imran
1/1/2026
cs.CV

Official code: tbwa233/ABFR-KAN (3 stars, last commit 2026-01-13).

Abstract

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.

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Code Implementations(1)

tbwa233/ABFR-KANOfficial100%

Improving brain disorder diagnosis with advanced brain function representation and Kolmogorov-Arnold Networks (MIDL 2025)

30PythonSep 17, 20249 months ago

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Cite this paper

@article{ward2026abfrkan,
  title  = {ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis},
  author = {Tyler Ward and Abdullah Imran},
  year   = {2026},
  eprint = {2601.00416},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.00416v1}
}

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