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Abstract Nanomaterial advancements have driven progress in central and peripheral nervous system applications such as tissue regeneration and brain–machine interfacing. Ideally, neural interfaces with native tissue shall seamlessly integrate, a process that is often mediated by the interfacial material properties. Surface topography and material chemistry are significant extracellular stimuli that can influence neural cell behavior to facilitate tissue integration and augment therapeutic outcomes. This review characterizes topographical modifications, including micropillars, microchannels, surface roughness, and porosity, implemented on regenerative scaffolding and brain–machine interfaces. Their impact on neural cell response is summarized through neurogenic outcome and mechanistic analysis. The effects of surface chemistry on neural cell signaling with common interfacing compounds like carbon‐based nanomaterials, conductive polymers, and biologically inspired matrices are also reviewed. Finally, the impact of these extracellular mediated neural cues on intracellular signaling cascades is discussed to provide perspective on the manipulation of neuron and neuroglia cell microenvironments to drive therapeutic outcomes.
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Optimal and Adaptive Stimulation Design
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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- Award ID(s):
- 1845348
- PAR ID:
- 10645007
- Editor(s):
- Thakor, Nitish V
- Publisher / Repository:
- Springer Singapore
- Date Published:
- Edition / Version:
- 3.0
- Page Range / eLocation ID:
- 1 to 64
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Book Chapter:
- https://doi.org/10.1007/978-981-15-2848-4_60-1
