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Related Experiment Video

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HESREN: A Derivative-Informed Reservoir Framework for Detecting Transient Neural Events and Windowless Estimation of

Ugur Kadak1,2

  • 1Gazi University, Ankara, Turkey. ugurkadak@gmail.com.

Neuroinformatics
|June 10, 2026
PubMed
Summary

A new framework called HESREN improves dynamic functional connectivity (dFC) analysis in fMRI by enabling windowless estimation and detecting transient neural events. This method offers higher temporal resolution and better detection of brain activity changes compared to conventional approaches.

Keywords:
Dynamic functional connectivityEcho state networksHermite-type neural operatorsReservoir computingTransient event detectionfMRI time series analysis

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Machine Learning

Background:

  • Dynamic functional connectivity (dFC) analysis in fMRI is crucial for understanding brain function.
  • Conventional sliding-window methods face limitations in temporal resolution and detecting rare transient events.
  • Existing methods struggle to balance statistical reliability with the need for high temporal precision.

Purpose of the Study:

  • To introduce HESREN (Hermite-Enhanced Software Reservoir Network), a novel framework for windowless dFC estimation and transient neural event detection.
  • To overcome the trade-off between temporal resolution and statistical reliability in fMRI analysis.
  • To enhance the detection and characterization of transient neural events in brain activity.

Main Methods:

  • HESREN integrates echo state networks with derivative-informed Hermite-type neural operators.
  • A leaky-integrator reservoir projects fMRI time series into high-dimensional spaces, incorporating temporal derivatives for enhanced feature vectors.
  • Strict temporal partitioning and teacher-student distillation ensure accurate, reproducible, and windowless dFC estimation and transient event detection.

Main Results:

  • HESREN demonstrated significant improvements over conventional sliding-window methods and other dFC alternatives (HMM, TCN, LSTM).
  • The framework achieved high performance in transient event detection (AUC [Formula: see text], AP [Formula: see text]) and provided finer temporal resolution (3-[Formula: see text] improvement).
  • Network-level analysis revealed earlier detection of transient events and enhanced network coupling, consistent with brain network modularity.

Conclusions:

  • HESREN overcomes fundamental limitations of sliding-window dFC analysis, offering a computationally efficient and mathematically principled framework.
  • The method enables precise capture of transient neural reconfigurations in fMRI data.
  • Its modular architecture supports adaptation for diverse neuroimaging applications, including real-time clinical monitoring.