P217 The Generalized Activating Function: Accelerating Axonal Dynamics Modeling for Spinal Cord Stimulation Optimization
Javier García Ordóñez*1,2, Taylor Newton1, Abdallah Alashqar3,4, Andreas Rowald3,4, Esra Neufeld1, & Niels Kuster1,5
1 IT’IS Foundation, Zürich, Switzerland 2 Zürich MedTech AG, Zürich, Switzerland 3 Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürenberg, Erlangen, Germany 4 Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürenberg, Erlangen, Germany 5 Swiss Federal Institute of Technology (ETH Zurich), Zürich, Switzerland
*Email: ordonez@itis.swiss
Introduction
The classical Activating Function (AF) provides a fast, linear estimator for membrane polarization as a predictor for stimulation by extracellular electric potential exposure [1]. While computationally efficient, the classical AF fails to account for membrane leakage currents, diffusive interactions between adjacent axonal segments, complex fiber models (multi-cable with periaxonal and paranodal compartments), and the influence of stimulation waveform, limiting its accuracy and usefulness in complex neurostimulation scenarios.
Methods The Generalized Activating Function (GAF) is a biophysics-based predictor that overcomes these limitations while preserving computational efficiency. The GAF extends the classical framework by convolving the extracellular potential with a Green's function kernel to account for the dynamics of membrane polarization, including axial currents and membrane leakage. A fast Fourier transform is used for the convolutions, producing spike predictions more than 1000× faster than conventional compartmental modeling. The GAF’s formulation accurately predicts dynamic responses in complex fiber models, such as the McIntyre-Richardson-Grill myelinated fiber model [2]. Results We first verified the GAF by reproducing benchmark experimental and computational data (e.g., strength–duration curves and diameter–dependent rheobase values for different fiber types). Next, we applied the GAF to a clinically validated, realistic model of spinal cord stimulation (SCS)[3]. The GAF’s spike predictions matched those of full electrophysiological simulations, with compute times reduced from hours to seconds. Finally, we leveraged the GAF’s speed and efficiency to explore the design of superior stimulation waveforms and electrode configurations that enhance the selectivity and energy efficiency of SCS. GAF-guided pulse shape optimization discovered charge-balanced waveforms that double recruitment efficacy or reduced power consumption five-fold relative to commonly applied stimulation waveforms. Discussion These results demonstrate that the GAF dramatically accelerates neurostimulation modeling without significant loss of accuracy, thereby facilitating large-scale explorations of stimulation parameters and the identification of personalized neuromodulation strategies. By bridging the gap between computational modeling and clinical practice, the GAF paves the way for optimized, patient-specific neurostimulation therapies.
Acknowledgements No acknowledgements. References ● https://doi.org/10.1152/jn.00353.2001 ● https://doi.org/10.1109/TBME.1986.325670
