Progress in Neuroengineering for brain repair: New challenges and open issues

Abstract

Background: In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome.

Results: In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise.

Conclusions: Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.

Keywords: Artificial intelligence; bioethics; brain disorder; closed loop; neuroprosthetics.

Figure 1.

Patent analysis in neuroengineering. (a) Number of published patents per year from 1970 to 2017. (b) Map showing the countries where the patents were filed. The United States leads with 3073 patents followed by Germany with 147, Netherlands with 131, Switzerland with 127 and China with 82. The remaining nations are below 65. (c) Number of published patents per year (from 1998 to 2017) by the 15 most active applicants. Patent data were collected using the Patent Inspiration database (http://www.patentinspiration.com/) based on the DOCDB database from the European Patent Office (EPO). DOCDB database contains bibliographic data from over 102 countries. Bibliographic data include titles, abstracts, applicants, inventors, citations, literature citations, code classifications and family info. The database is updated on a weekly basis. Patents were searched from January 1970 to October 2017 using the following Boolean search strategy: (‘neurostimulation’ OR ‘neural stimulation’ OR ‘brain stimulation’) in title or abstract.

Figure 2.

I/O control systems for neuroengineering. (a) Representation of the elements involved in a neurotechnological tool: an artificial device (D block) and a portion of the CNS (B block). (b) Open-loop architecture. The device is programmed to output a stimulus (device output – O_D), which also represents the input to the brain (device input – I_D); in turn, the brain generates an output response (brain output – O_B). The open-loop device cannot read brain electrical activity (black trace) and thus operates independent of it. (c) Control algorithms for closed-loop architecture. In closed-loop fashion, the brain output response (O_B) is fed back to the device, thus serving as an input to the device (brain input – I_B) and determining the device output (O_D). The reactive I/O system is capable of reading an input signal, but cannot interpret its meaning. The system’s output is predefined by the human, based on theoretical assumptions or on empirical trial-and-error refinement, and the feedback from the brain acts as a simple trigger. The responsive I/O system is provided with a number of choices, but their conditional application is predefined by the human based on previously acquired knowledge. The system interprets the feedback from the brain and selects the stimulus based on its content. The adaptive I/O system can independently choose the best output provided a varying input. The system learns the best output strategy through the feedback provided by the performance evaluator. In this way, the system may evolve and deliver a different output upon subsequent presentation of the same input based on the learned strategy and on its past experience.

Figure 3.

Neuroengineering devices for brain repair. (a) Brain modulator for DBS. The device is implanted into deep brain structures and may be based on open- or closed-loop architecture. (b) A BMI conveys the electrical activity of the recorded brain area to the robotic end effector. In this rendition, the established system is made up of the input brain area, the interface apparatus and the robotic hand. In this case, an open-loop strategy is implemented: visual feedback aids in movement planning and the required adjustments, which, in turn, influence the function of the interface apparatus. (c) A brain prosthesis is an artificial device implanted in the brain to replace brain activity or to reconnect disconnected brain areas.