Benchmark II ixk51

Figure 1: Image modified from (Navarro et al. 2005)

Importance of problem: A selective, high fidelity neural signal can serve as an indicator of neuronal activity, which can be used in a multitude of control and therapy applications

A stable chronic interface between the nervous system and man-made electronics allows us to “listen” to neuronal activity. It is impossible to fully appreciate all the potential applications of peripheral nerve recording since some, if not most of them, have yet to be invented; however, there are several particular applications which demonstrate a clear need for advances in this technology.

The prosthetic limb industry has been around for centuries in one form or another, yet the idea of using “smart” prosthetics, ones that mimic actual human motion, is relatively new. In order to ensure that a prosthetic device is responsive to the user’s movement wishes, there must be a command signal to drive the motor(s) in the artificial joint. This signal may be obtained from extra-corporeal sources such as sensor/switch systems, as well as from the body’s natural cortical, muscle-based, or peripheral nerve activities. Using external sensors and switches, while plausible, poses problems such as donning, doffing, calibration, and lack of space. Of the aforementioned biologically inspired options, detecting activity from the nerve that previously innervated the lost limb is the most intuitive interface method. It taps into the “control signal” of the muscles themselves, such that neural information that previously went to the patient’s missing limb is now fully accessible. An ideal (yet currently unavailable) neural interface would pick out signals from individual neurons, which would enable prosthetic devices to fully mimic human motion using an artificial limb.

Peripheral neural recording has been shown to be potentially useful in several applications involving controlling FES devices (Popovic et al. 1993), more specifically electrical stimulation to treat obstructive sleep apnea (OSA) (Sahin, Durand, and Haxhiu 1999), foot drop (M. K. Haugland and Sinkjaer 1995), hand grasp (M. Haugland et al. 1997) and others (see (Hoffer et al. 1996) for a review of applications).

Barrier to overcome: There is currently no optimized minimally invasive electrode system for the purpose of selectively recording ENG (electroneurograms).

In FES and prosthetic systems with a large number of degrees of freedom (DOF), controlling each DOF is crucial for functionality. In such a case, it becomes necessary to control each DOF with its own signal, which could be accomplished by implanting a separate electrode for each DOF, which is overly invasive due to the need for multiple implantations, overabundance of wires, and overall complexity of the system. One of the greatest challenges of modern neural recording systems is developing a single electrode that could discriminate between the firing patterns of different fascicles, obviating the need for multiple implantations.

The ability of an electrode to discriminate between signals from various sources is defined as its selectivity, depicted on the x-axis in Figure 1. Unsurprisingly, selectivity increases proportionally with invasivity since electrodes that are physically closer to neurons are better able to pick up their activity but are also more invasive. Invasivity in this context is defined as the measure by which neural tissue is negatively affected due to implantation. Man-made devices that closely interact with neurons are therefore more invasive than those that are physically further away.

The challenge has been to optimize the interface in such a way that selectivity is maximized while invasivity (and therefore damage) is minimized (labeled “Optimal” in Figure 1). Currently, no electrode exists that claims to cause no damage to neurons and at the same time records with superior selectivity, though several electrodes can be placed at different points along the selectivity-invasivity line (Figure 1) (((Navarro et al. 2005))(Yoo and Durand 2005)(Micera et al. 2011)(Yoshida, Hennings, and Kammer 2006)).

Importance in the field: The novel electrode design may challenge the paradigm that selectivity and invasivity are directly related.

This project will address the challenge of creating a selective yet relatively non-invasive interface (aka closer to the “Optimal” electrode on the Selectivity-Invasivity plot in Figure 1) by proposing an interfascicular electrode, a novel interface which has been sparingly used for stimulation (Tyler and Durand 1997)(Nielsen, Sevcencu, and Struijk 2011) and has not been documented for recording applications. This study will quantify the short-term and long-term performance of interfascicular electrodes by assessing three clinically relevant metrics: selectivity, signal-to-noise ratio (SNR), and neuronal damage. If successful, this electrode could serve as a viable recording interface between the peripheral nervous system and man-made devices. The development of this technology could ultimately improve the control mechanism for patients using FES systems or prosthetic devices, which would ultimately lead to a lower level of functional impairment and an improved standard of living. As more devices are made that can utilize neural signals, using interfascicular electrodes may become a standard practice for those applications as well. 

References

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Haugland, M. K, and T. Sinkjaer. 1995. “Cutaneous whole nerve recordings used for correction of footdrop inhemiplegic man.” IEEE Transactions on Rehabilitation Engineering 3 (4) (December): 307-317. doi:10.1109/86.481970.

Haugland, Morten, Andreas Lickel, Ron Riso, Margareth M Adamczyk, Michael Keith, Inger Lauge Jensen, Jens Haase, and Thomas Sinkjær. 1997. “Restoration of Lateral Hand Grasp Using Natural Sensors.” Artificial Organs 21 (3) (March 1): 250-253. doi:10.1111/j.1525-1594.1997.tb04661.x.

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Navarro, Xavier, Thilo B Krueger, Natalia Lago, Silvestro Micera, Thomas Stieglitz, and Paolo Dario. 2005. “A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems.” Journal of the Peripheral Nervous System: JPNS 10 (3) (September): 229-258. doi:10.1111/j.1085-9489.2005.10303.x.

Nielsen, T, C Sevcencu, and J Struijk. 2011. “Fascicle-Selectivity of an Intra-Neural Stimulation Electrode in the Rabbit Sciatic Nerve.” IEEE Transactions on Bio-Medical Engineering (September 26). doi:10.1109/TBME.2011.2169671. http://www.ncbi.nlm.nih.gov/pubmed/21954195.

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Sahin, M., D. M. Durand, and M. A. Haxhiu. 1999. “Chronic recordings of hypoglossal nerve activity in a dog model of upper airway obstruction.” J Appl Physiol 87 (6): 2197-206.

Schneider, Rick, Joanna Przybyl, Uwe Pliquett, Michael Hermann, Markus Wehner, Uta-Carolin Pietsch, Fritjoff König, Johann Hauss, Sven Jonas, and Steffen Leinung. 2010. “A new vagal anchor electrode for real-time monitoring of the recurrent laryngeal nerve.” American Journal of Surgery 199 (4) (April): 507-514. doi:10.1016/j.amjsurg.2009.04.036.

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Yoo, P. B., and D. M. Durand. 2005. “Selective recording of the canine hypoglossal nerve using a multicontact flat interface nerve electrode.” IEEE Trans Biomed Eng 52 (8): 1461-9. doi:10.1109/TBME.2005.851482.

Yoshida, K., K. Hennings, and S. Kammer. 2006. Acute Performance of the Thin-Film Longitudinal Intra-Fascicular Electrode. In Biomedical Robotics and Biomechatronics, 2006. BioRob 2006. The First IEEE/RAS-EMBS International Conference on, 296-300.