Decoding Task-Based Functional Connectivity: Exploring Kolmogorov-Arnold Networks for Task-Evoked fMRI Classification

Motivation: Limited research exists on leveraging functional connectivity for task condition classification, and current deep learning models lack interpretability from a neuroscience perspective. Goal(s): Evaluate the interpretability of Kolmogorov-Arnold Networks for task condition classification. Approach: Kolmogorov-Arnold Networks was trained to classify task conditions using functional connectivity across seven tasks. Contribution analysis was conducted on learnable B-spline activation functions from correctly classified samples to report the important connections and regions. Results: The analysis results from the trained Kolmogorov-Arnold Networks are more interpretable with respect to the literature on the functional roles of specific brain regions. Impact: This study demonstrates that the Kolmogorov-Arnold Network offers strong interpretability for fMRI task-condition classification, aligning with literature on the functional roles of specific brain regions. This advances task-based fMRI analysis and supports the development of explainable neural networks in neuroscience.