P287 Pulsatile and direct current stimulation in functional network model of decision making
Cynthia Steinhardt*1,2,Paul Adkisson3, Gene Fridman3
1 Simons Society of Fellows, Junior Fellow, New York, New York 10010
2 Center for Theoretical Neuroscience, Zuckerman Brain Science Institute, Columbia University, New York, New York 10027
3 Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland 21287
*Email: cs4248@columbia.edu
Introduction
Pulsatile stimulation has been the primary method for neural implants in sensory restoration and neuropathology treatment (e.g., Parkinson’s, epilepsy) since the first neural implant [1]. Recently, non-invasive transcranial direct/alternating current (DC/AC) stimulation has gained interest, offering broader accessibility without surgery. However, to be viable, effective non-invasive alternatives must match or exceed the efficacy of implants in modulating neural circuits. Pulsatile and DC stimulation effects in complex networks have not been directly compared due to the need for detailed biophysical models. We address this gap.
Methods
Our prior work showed that pulsatile stimulation alters firing patterns in single neurons in complex ways depending on pulse parameters and spontaneous activity [3]. Similarly, we modeled and characterized the effects of DC stimulation on single neurons [4]. Here, we extend these models, modifying linear-integrate-and-fire (LIF) models to include approximations of these effects so that we can accurately simulate local stimulation in a 1000-neuron network. We simulate pulsatile and DC stimulation at equivalent local dosing levels and at behaviorally equivalent levels and compare network effects in a winner-take-all decision-making circuit for motion detection.
Results
The network processes moving dots and determines whether the majority are moving left or right. We identified pulse rates for suprathreshold pulses that match DC stimulation’s effects on the firing rate in the left-motion detection part of the network. At this level, pulsatile stimulation induced a stronger, faster bias toward leftward decisions. When matched for behavioral bias, pulsatile stimulation resisted feedback inhibition and had conflicting effects with recurrent feedback. DC stimulation, in contrast, propagated through the network more strongly due to recurrent excitation but was more affected by feedback inhibition [5].
Discussion
This study provides the first direct comparison of how pulsatile and DC stimulation influence network activity up to the behavioral-level, using accurate approximations of electrical stimulation. We show that these two forms of stimulation interact differently with network dynamics, suggesting different therapeutic applications. Additionally, we present open-access tools for modeling, which could enhance patient-specific disease models. These tools allow for mechanistic insights beyond the LIF and threshold models currently used.
Acknowledgements
Acknowledgments
We thank the Simons Society of Fellows (965377), Gatsby Charitable Trust (GAT3708), Kavli Foundation, and NIH (R01NS110893) for support.
References
References
1.Loeb, G. E. (2018). Neural Prosthetics.Appl Bionics Biomech,2018, 1435030.
2.Giordano, J., et al. (2017). Mechanisms of tDCS.Dose-Response,15(1), 1559325816685467.
3.Steinhardt, C. R., et al. (2024). Pulsatile stimulation disrupts firing.Nat Commun,15(1), 5861.
4.Steinhardt, C. R., & Fridman, G. Y. (2021). DC effects on afferents.iScience,24(3).
5.Adkisson, P. W., et al. (2024). Galvanic vs. pulsatile effects.J Neural Eng,21(2), 026021.
