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Optimal and Adaptive Stimulation Design

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Handbook of Neuroengineering

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Abstract

Successful stimulation therapies of the central nervous system for chronic neurological disorders have been based so far on electric pulses that have equal amplitude and are delivered at constant intervals. Recent advancements, however, have shown that irregular and time-varying sequences of pulses can be equally effective in treating chronic disease conditions. This suggests that both pulse waveform and temporal arrangement are important factors in determining the therapeutic merit of a stimulation protocol and can be used to address the trade-off between therapeutic effectiveness, amount of charge delivered per unit of time, and efficiency of neural stimulators. Accordingly, a wide range of computational approaches have been developed to optimize this trade-off, and novel nonregular pulse trains have been designed. Optimization, adaptive control, and machine learning have been rapidly integrated into the design process of stimulation therapies, leading to highly efficient solutions but also dramatically increasing the complexity of the design process. This chapter will review the most significant advancements in optimization-based design for neural stimulation, along with the computational challenges, methodological innovations, and the most promising clinical applications for the treatment of the central nervous system.

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Abbreviations

AF:

Axon fibers

CNS:

Central nervous system

CR:

Coordinated reset

CV:

Coefficient of variation

DBS:

Deep brain stimulation

DoC:

Degree of centrality

DP:

Dynamic Programming

FDA:

US Food and Drug Administration

IF:

Integrate-and-fire

IPF:

Instantaneous pulse frequency

LIF:

Leaky integrate-and-fire

MIMO:

Multi-input multi-output

PD:

Parkinson’s disease

PMA:

Pre-market approval

PRC:

Phase response curve

PW:

Pulse width

STN:

Subthalamic nucleus

VAT:

Volume of activated tissue

VNS:

Vagus nerve stimulation

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Acknowledgments

This work was partly supported by the CT Institute for the Brain and Cognitive Sciences IBRAiN Fellowship (to X.Z.) and the US National Science Foundation CAREER Award 1845348 (to S.S.).

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  1. Biomedical Engineering Department, University of Connecticut, Storrs, CT, USA

    Xu Zhang & Sabato Santaniello

  2. CT Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, USA

    Xu Zhang & Sabato Santaniello

  3. Brain Research Imaging Center, University of Connecticut, Storrs, CT, USA

    Xu Zhang & Sabato Santaniello

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  1. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Nuan Chen

  2. SINAPSE Laboratory, Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Nuan Chen

  3. The N.1 Institute for Health, National University of Singapore, Singapore, Singapore

    Nuan Chen

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Zhang, X., Santaniello, S. (2022). Optimal and Adaptive Stimulation Design. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2848-4_60-1

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