Human cortical prostheses: lost in translation?

Abstract

Direct brain control of a prosthetic system is the subject of much popular and scientific news. Neural technology and science have advanced to the point that proof-of-concept systems exist for cortically-controlled prostheses in rats, monkeys, and even humans. However, realizing the dream of making such technology available to everyone is still far off. Fortunately today there is great public and scientific interest in making this happen, but it will only occur when the functional benefits of such systems outweigh the risks. In this article, the authors briefly summarize the state of the art and then highlight many issues that will directly limit clinical translation, including system durability, system performance, and patient risk. Despite the challenges, scientists and clinicians are in the desirable position of having both public and fiscal support to begin addressing these issues directly. The ultimate challenge now is to determine definitively whether these prosthetic systems will become clinical reality or forever unrealized.

Fig. 1

Concept sketch of cortical motor and communication prostheses. Neural signals are obtained from arrays of electrodes implanted in various possible cortical areas (PMd, M1, PRR, MIP). These are then processed and interpreted to generate control signals. These can be used to reconstruct an arm trajectory (motor prosthesis) or to select a target from a menu (communication prostheses). MIP = medial intraparietal area; PMd = dorsal premotor cortex; PMv = ventral premotor cortex;s PRR = parietal reach region; SMA = supplementary motor area. Figure reproduced from Shenoy et al., Increasing the performance of corticallycontrolled prostheses, in Proceedings of the 28th IEEE EMBS Annual International Conference. New York City, USA, Aug 30-Sept 3, 2006. IEEE, 2006, pp 6652–6655. Reproduced with permission, from IEEE, 2006. Copyright 2006, IEEE.

Fig. 2

Photograph of the Utah electrode array (Blackrock Microsystems, Inc.), showing a silicon micromachined electrode array (arrowhead) with 96 needle-like electrodes. This is connected via a wire bundle to a connector port (arrow) that must be anchored to the subject. This port is then used to externally connect to each electrode. The thin silver wires are for grounding (reference) purposes. Reprinted by permission from from Macmillan Publishers Ltd: Nature, 2006.

Fig. 3

Diagram showing neural recordings obtained from an unrestrained nonhuman primate over a 48-hour period after implantation of a chronic electrode array. The picture illustrates a box of recording electronics that was mounted on the subject’s head. Spike waveforms from these recordings show that waveforms changed from day to day which could not be explained by fluctuations in the signal path, indicating that the neurons seen were different. Figure reproduced from Shenoy et al., Increasing the performance of cortically-controlled prostheses, in Proceedings of the 28th IEEE EMBS Annual International Conference. New York City, USA, Aug 30-Sept 3, 2006. IEEE, 2006, pp 6652–6655. Copyright 2006, IEEE.

Fig. 4

Diagram showing an overview of our nonhuman primate high-performance communication prosthesis experiment. The real-time prosthetic cursor placement task begins by fixating and touching central targets, followed by the appearance of a peripheral target to which the subject plans (but does not execute) a reach. A period of neural data following this target onset (Tgt on) is set aside (Tskip). A period of neural data (Tint) is then analyzed to estimate the desired target; (P(m) refers to the target-m with the highest probability; here, m = 2), which could have appeared in one of 8 locations in this task, and after a brief computational decode and display rendering “overhead” period (Tdec+rend), the predicted target is encircled. Figure reproduced from Shenoy et al., Increasing the performance of cortically-controlled prostheses, in Proceedings of the 28th IEEE EMBS Annual International Conference. New York City, USA, Aug 30-Sept 3, 2006. IEEE, 2006, pp 6652–6655. Copyright 2006, IEEE.

Fig. 5

Sixteen target optimal target placement (OTP) example. Red squares are uniformly spaced targets around a circle, as an experimenter might use without OTP methods. Blue circles show an OTP solution. Figure from Cunningham et al., 2008 used with permission.

Fig. 6

Illustration of brain states involved in autonomous reaching. We spend most of our time in the idle, or baseline state (gray). From here, we switch to a different state where we plan a movement (blue). The movement can then be executed which is another brain state (red). Once completed we can then plan another move (blue arrow) or return to the idling state (long gray arrow). One can also start planning and then abort the move as indicated by the gray short return arrow. Automating a prosthetic involves determining the unique neural characteristics of these states to determine when these transitions occur. Our early computational results suggest that this can be done. See the studies by Achtman et al., 2007, and Kemere et al., 2008.

Fig. 7

A working fully implantable miniaturized implant that integrates an electrode array with amplification and telemetric circuits; shown in profile (a) and after being encased in polymer (b). Such a small implant would be necessary for long-term chronic recordings as well as to contribute to overall durability and feasibility of the system. Figure reproduced from Harrison et al., A wireless neural interface for chronic recording, in Biomedical Circuits and Systems Conference, 2008. IEEE, 2008, pp 125–128. Copyright 2008, IEEE.