Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks

Tyler Ward, Abdullah-Al-Zubaer Imran
4/4/2025
cs.CV

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

Abstract

Quantifying functional connectivity (FC), a vital metric for the diagnosis of various brain disorders, traditionally relies on the use of a pre-defined brain atlas. However, using such atlases can lead to issues regarding selection bias and lack of regard for specificity. Addressing this, we propose a novel transformer-based classification network (ABFR-KAN) with effective brain function representation to aid in diagnosing autism spectrum disorder (ASD). ABFR-KAN leverages Kolmogorov-Arnold Network (KAN) blocks replacing traditional multi-layer perceptron (MLP) components. Thorough experimentation reveals the effectiveness of ABFR-KAN in improving the diagnosis of ASD under various configurations of the model architecture. Our code is available at https://github.com/tbwa233/ABFR-KAN

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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{ward2025improving,
  title  = {Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks},
  author = {Tyler Ward and Abdullah-Al-Zubaer Imran},
  year   = {2025},
  eprint = {2504.03923},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.03923v2}
}

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