Chronic recordings of hypoglossal nerve activity in a dog model of upper airway obstruction

Printable microscale interfaces for long-term peripheral nerve mapping and precision control

The nascent field of bioelectronic medicine seeks to decode and modulate peripheral nervous system signals to obtain therapeutic control of targeted end organs and effectors. Current approaches rely heavily on electrode-based devices, but size scalability, material and microfabrication challenges, limited surgical accessibility, and the biomechanically dynamic implantation environment are significant impediments to developing and deploying peripheral interfacing technologies. Here, we present a microscale implantable device - the nanoclip - for chronic interfacing with fine peripheral nerves in small animal models that begins to meet these constraints. We demonstrate the capability to make stable, high signal-to-noise ratio recordings of behaviorally-linked nerve activity over multi-week timescales. In addition, we show that multi-channel, current-steering-based stimulation within the confines of the small device can achieve multi-dimensional control of a small nerve. These results highlight the potential of new microscale design and fabrication techniques for realizing viable devices for long-term peripheral interfacing.

Neural Control of the Upper Airway: Respiratory and State-Dependent Mechanisms

Upper airway muscles subserve many essential for survival orofacial behaviors, including their important role as accessory respiratory muscles. In the face of certain predisposition of craniofacial anatomy, both tonic and phasic inspiratory activation of upper airway muscles is necessary to protect the upper airway against collapse. This protective action is adequate during wakefulness, but fails during sleep which results in recurrent episodes of hypopneas and apneas, a condition known as the obstructive sleep apnea syndrome (OSA). Although OSA is almost exclusively a human disorder, animal models help unveil the basic principles governing the impact of sleep on breathing and upper airway muscle activity. This article discusses the neuroanatomy, neurochemistry, and neurophysiology of the different neuronal systems whose activity changes with sleep-wake states, such as the noradrenergic, serotonergic, cholinergic, orexinergic, histaminergic, GABAergic and glycinergic, and their impact on central respiratory neurons and upper airway motoneurons. Observations of the interactions between sleep-wake states and upper airway muscles in healthy humans and OSA patients are related to findings from animal models with normal upper airway, and various animal models of OSA, including the chronic-intermittent hypoxia model. Using a framework of upper airway motoneurons being under concurrent influence of central respiratory, reflex and state-dependent inputs, different neurotransmitters, and neuropeptides are considered as either causing a sleep-dependent withdrawal of excitation from motoneurons or mediating an active, sleep-related inhibition of motoneurons. Information about the neurochemistry of state-dependent control of upper airway muscles accumulated to date reveals fundamental principles and may help understand and treat OSA. © 2016 American Physiological Society. Compr Physiol 6:1801-1850, 2016.

Ultra-low noise miniaturized neural amplifier with hardware averaging

OBJECTIVE

Peripheral nerves carry neural signals that could be used to control hybrid bionic systems. Cuff electrodes provide a robust and stable interface but the recorded signal amplitude is small (<3 μVrms 700 Hz-7 kHz), thereby requiring a baseline noise of less than 1 μVrms for a useful signal-to-noise ratio (SNR). Flat interface nerve electrode (FINE) contacts alone generate thermal noise of at least 0.5 μVrms therefore the amplifier should add as little noise as possible. Since mainstream neural amplifiers have a baseline noise of 2 μVrms or higher, novel designs are required.

APPROACH

Here we apply the concept of hardware averaging to nerve recordings obtained with cuff electrodes. An optimization procedure is developed to minimize noise and power simultaneously. The novel design was based on existing neural amplifiers (Intan Technologies, LLC) and is validated with signals obtained from the FINE in chronic dog experiments.

MAIN RESULTS

We showed that hardware averaging leads to a reduction in the total recording noise by a factor of 1/√N or less depending on the source resistance. Chronic recording of physiological activity with FINE using the presented design showed significant improvement on the recorded baseline noise with at least two parallel operation transconductance amplifiers leading to a 46.1% reduction at N = 8. The functionality of these recordings was quantified by the SNR improvement and shown to be significant for N = 3 or more. The present design was shown to be capable of generating <1.5 μVrms total recording baseline noise when connected to a FINE placed on the sciatic nerve of an awake animal. An algorithm was introduced to find the value of N that can minimize both the power consumption and the noise in order to design a miniaturized ultralow-noise neural amplifier.

SIGNIFICANCE

These results demonstrate the efficacy of hardware averaging on noise improvement for neural recording with cuff electrodes, and can accommodate the presence of high source impedances that are associated with the miniaturized contacts and the high channel count in electrode arrays. This technique can be adopted for other applications where miniaturized and implantable multichannel acquisition systems with ultra-low noise and low power are required.

Phasic motor activity of respiratory and non-respiratory muscles in REM sleep

OBJECTIVES

In this study, we quantified the profiles of phasic activity in respiratory muscles (diaphragm, genioglossus and external intercostal) and non-respiratory muscles (neck and extensor digitorum) across REM sleep. We hypothesized that if there is a unique pontine structure that controls all REM sleep phasic events, the profiles of the phasic twitches of different muscle groups should be identical. Furthermore, we described how respiratory parameters (e.g., frequency, amplitude, and effort) vary across REM sleep to determine if phasic processes affect breathing.

METHODS

Electrodes were implanted in Wistar rats to record brain activity and muscle activity of neck, extensor digitorum, diaphragm, external intercostal, and genioglossal muscles. Ten rats were studied to obtain 313 REM periods over 73 recording days. Data were analyzed offline and REM sleep activity profiles were built for each muscle. In 6 animals, respiratory frequency, effort, amplitude, and inspiratory peak were also analyzed during 192 REM sleep periods.

RESULTS

Respiratory muscle phasic activity increased in the second part of the REM period. For example, genioglossal activity increased in the second part of the REM period by 63.8% compared to the average level during NREM sleep. This profile was consistent between animals and REM periods (η(2)=0.58). This increased activity seen in respiratory muscles appeared as irregular bursts and trains of activity that could affect rythmo-genesis. Indeed, the increased integrated activity seen in the second part of the REM period in the diaphragm was associated with an increase in the number (28.3%) and amplitude (30%) of breaths. Non-respiratory muscle phasic activity in REM sleep did not have a profile like the phasic activity of respiratory muscles. Time in REM sleep did not have an effect on nuchal activity (P=0.59).

CONCLUSION

We conclude that the concept of a common pontine center controlling all REM phasic events is not supported by our data. There is a drive in REM sleep that affects specifically respiratory muscles. The characteristic increase in respiratory frequency during REM sleep is induced by this drive.

Localization and recovery of peripheral neural sources with beamforming algorithms

The peripheral nervous system carries sensory and motor information that could be useful as command signals for function restoration in areas such as neural prosthetics and functional electrical stimulation (FES). Nerve cuff electrodes provide a robust and safe technique for recording nerve signals. However, a method to separate and recover signals from individual fascicles is necessary. Prior knowledge of the electrode geometry was used to develop an algorithm which assumes neither signal independence nor detailed knowledge of the nerve's geometry/conductivity, and is applicable to any wide-band near-field situation. When used to recover fascicular activities from simulated nerve cuff recordings in a realistic human femoral nerve model, this beamforming algorithm separates signals as close as 1.5 mm with cross-correlation coefficient, R > 0.9 (10% noise). Ten simultaneous signals could be recovered from individual fascicles with only a 20% decrease in R compared to a single signal. At high noise levels (40%), sources were localized to 180 +/- 170 microm in the 12 x 3 mm cuff. Localizing sources and using the resulting positions in the recovery algorithm yielded R = 0.66 +/- 0.10 in 10% noise for five simultaneous muscle-activation signals from synergistic fascicles. These recovered signals should allow natural, robust, closed-loop control of multiple degree-of-freedom prosthetic devices and FES systems.

The recording properties of a multi-contact nerve electrode as predicted by a finite element model of the canine hypoglossal nerve

Most functional electrical stimulation (FES) systems rely only on unidirectional (i.e., efferent) activation of the target organ to yield therapeutic outcomes. For applications involving multi-fasciculated nerves, however, artificial sensors have exhibited limited results. As such, the flat-interface-nerve-electrode (FINE) is presented as a means of obtaining an effective closed-loop control system. To investigate the ability of this electrode to achieve selective recordings at physiological signal-to-noise ratio (SNR), a finite element model (JFEM) of a beagle hypoglossal nerve with an implanted FINE was constructed. Action potentials (AP) were generated at various SNR levels and the performance of the electrode was assessed with a selectivity index (0 < or = SI < or = 1; ability of the electrode to distinguish two active sources). Computer simulations yielded a selective range (0.05 < or = SI < or = 0.76) that was (1) related to the inter-fiber distance and (2) used to predict the minimum inter-fiber distance (0.23 mm < or = d < or = 1.42 mm) required for selective recording. The results of this study suggest that the FINE can record neural activity from a multi-fasciculated nerve and, more importantly, distinguish neural activity from pairs of fascicles at physiologic SNR.