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Neural Prosthetics:A Review of Empirical vs. Systems Engineering Strategies 时间:2018-11-19 13:49 来源:未知作者:admin 点击: 次 Abstract:Implantable electrical interfaces with the nervous system were first enabled by cardiac pacemaker technology over 50 years ago and have since diverged into almost all of the physiological functions controlled by the nervous system. There have been a few major clinical and commercial successes, many contentious claims, and some outright failures. These tend to be reviewed within each clinical subspecialty, obscuring the many commonalities of neural control, biophysics, interface materials, electronic technologies, and medical device regulation that they share. This review cites a selection of foundational and recent journal articles and reviews for all major applications of neural prosthetic interfaces in clinical use, trials, or development. The hard-won knowledge and experience across all of these fields can now be amalgamated and distilled into more systematic processes for development of clinical products instead of the often empirical (trial and error) approaches to date. These include a frank assessment of a specific clinical problem, the state of its underlying science, the identification of feasible targets, the availability of suitable technologies, and the path to regulatory and reimbursement approval. Increasing commercial interest and investment facilitates this systematic approach, but it also motivates projects and products whose claims are dubious.
1. Introduction
Neural prosthetics are the clinical application of the science of neurophysiology and the methodology of electrophysiology. Almost all physiological functions are coordinated and controlled by electrical signals that share strikingly similar biophysical principles and cellular machinery: sensory perception, movement, cognition, emotion, digestion, excretion, endocrine function, blood circulation, cellular immunity, etc. The technology to record and manipulate these electrical signals was used first to identify and understand this physiology and then, often rapidly and even prematurely, to try to mitigate clinical dysfunction. The rush to exploit new scientific knowledge even as it remains uncertain and incomplete is understandable. Much of modern medical practice owes its existence to serendipity that subsequently motivated systematic scientific inquiry rather than the inverted and largely fictional version of the scientific method taught to students. The value of the occasional successes of Edisonian empiricism (https://en.wikipedia.org/wiki/Edisonian_approach) has often outweighed the costs of many failures and tended to undermine the credibility of scientific conservatives. Nevertheless, changing circumstances warrant a reexamination of this frontier mentality.
The accumulated scientific knowledge about most bodily functions has increased hugely, even for subsystems that are not yet the target of neural prosthetic interfaces. Finding this knowledge is both more difficult given its sheer volume and specialization and much easier given powerful search engines and online archives. Both experimental and modeling methods now make it much more feasible to mount a systematic approach to filling in the missing pieces of science and to designing and building prosthetic interfaces that are likely to work. This can be based on rigorous biophysical models of how electrical fields modulate neural activity [1–3] but only if those models have been validated experimentally.
Most of the currently approved and clinically successful neural prostheses described below utilize technology and functionality that is little changed from cardiac pacemakers of the 1970s (Figure 1). Most of the future applications of neural prostheses will require many more densely packed channels of communication into and out of the nervous system. The available armamentarium of technology makes it possible to design and build interfaces that directly address the underlying science, but they also greatly increase the complexity, time, and expense of the product development cycle.