- This is the official PyTorch implementation of Spectral-Temporal State Space Modeling on Functional Brain Networks.
Resting-state fMRI (rs-fMRI) offers a powerful tool for analyzing functional organization in brain for neurodegenerative and neurodevelopmental disorders. Although graph-based and spatio-temporal models have shown promise, most existing approaches decouple spatial structure from temporal dynamics or rely on predefined temporal windows, limiting the capacity of spatial information to directly modulate temporal processing. To address these limitations, we propose a spectral-temporal state space framework that integrates graph spectral representations with state-space modeling through a spectral-temporal kernel, enabling window-free and frequency-based temporal modeling. Experiments on three rs-fMRI cohorts with 1,926 subjects demonstrate that our method consistently outperforms competitive baselines across diverse conditions, including Parkinson’s disease, autism spectrum disorder, and attention deficit hyperactivity disorder. In addition, we provide interpretable insights into spectral-temporal patterns of brain dysfunction, advancing the characterization and classification of neurodegenerative and neurodevelopmental disorders.
If you find our work useful for your research, please cite the our paper:
@inproceedings{sim2026spectral,
title={Spectral-Temporal State Space Modeling on Functional Brain Networks},
author={Sim, Jaeyoon and Lee, Hoseok and Park, Jihwan and Baek, Seunghun and Yu Zhang and Kim, Won Hwa},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
year={2026}
}