Selective chronic recording in small nerve fascicles of sciatic nerve with carbon nanotube yarns in rats.

Objective. The primary challenge faced in the field of neural rehabilitation engineering is the limited advancement in nerve interface technology, which currently fails to match the mechanical properties of small-diameter nerve fascicles. Novel developments are necessary to enable long-term, chronic recording from a multitude of small fascicles, allowing for the recovery of motor intent and sensory signals.Approach. In this study, we analyze the chronic recording capabilities of carbon nanotube yarn electrodes in the peripheral somatic nervous system. The electrodes were surgically implanted in the sciatic nerve's three individual fascicles in rats, enabling the recording of neural activity during gait. Signal-to-noise ratio (SNR) and information theory were employed to analyze the data, demonstrating the superior recording capabilities of the electrodes. Flat interface nerve electrode and thin-film longitudinal intrafascicular electrode electrodes were used as a references to assess the results from SNR and information theory analysis.Main results. The electrodes exhibited the ability to record chronic signals with SNRs reaching as high as 15 dB, providing 12 bits of information for the sciatic nerve, a significant improvement over previous methods. Furthermore, the study revealed that the SNR and information content of the neural signals remained consistent over a period of 12 weeks across three different fascicles, indicating the stability of the interface. The signals recorded from these electrodes were also analyzed for selectivity using information theory metrics, which showed an information sharing of approximately 1.4 bits across the fascicles.Significance. The ability to safely and reliably record from multiple fascicles of different nerves simultaneously over extended periods of time holds substantial implications for the field of neural and rehabilitation engineering. This advancement addresses the limitation of current nerve interface technologies and opens up new possibilities for enhancing neural rehabilitation and control.

Measurement of block thresholds in kiloHertz frequency alternating current peripheral nerve block.

BACKGROUND: Kilohertz frequency alternating currents (KHFAC) produce rapid nerve conduction block of mammalian peripheral nerve and have potential clinical applications in reducing peripheral nerve hyperactivity. The experimental investigation of KHFAC nerve block requires a robust output measure and this has proven to be the block threshold (BT), the lowest current or voltage at which the axons of interest are completely blocked. All significant literature in KHFAC nerve block, both simulations and experimental, were reviewed to determine the block threshold method that was used. The two common methods used are the High-Low method experimentally and the Binary search method for simulations. NEW METHOD: Four methods to measure the block threshold (High-Low, High-Low-High, Binary and Random) at three frequencies (10, 20 and 30 kHz) were compared through randomized repeated experiments in the in-vivo rodent sciatic nerve-gastrocnemius model. RESULTS: The literature review showed that more than 50% of publications did not measure the block threshold. The experimental results showed no statistical difference in the BT value between the four methods. COMPARISON WITH EXISTING METHOD(S): However, there were differences in the number of significant onset responses, depending on the method. The run time for the BT determination was the shortest for the High-Low method. CONCLUSIONS: It is recommended that all research in electrical nerve block, including KHFAC, should include measurement of the BT. The High-Low method is recommended for most experimental situations but the Binary method could also be a viable option, especially where onset responses are minimal.

Recovering Motor Activation with Chronic Peripheral Nerve Computer Interface.

Interfaces with the peripheral nerve provide the ability to extract motor activation and restore sensation to amputee patients. The ability to chronically extract motor activations from the peripheral nervous system remains an unsolved problem. In this study, chronic recordings with the Flat Interface Nerve Electrode (FINE) are employed to recover the activation levels of innervated muscles. The FINEs were implanted on the sciatic nerves of canines, and neural recordings were obtained as the animal walked on a treadmill. During these trials, electromyograms (EMG) from the surrounding hamstring muscles were simultaneously recorded and the neural recordings are shown to be free of interference or crosstalk from these muscles. Using a novel Bayesian algorithm, the signals from individual fascicles were recovered and then compared to the corresponding target EMG of the lower limb. High correlation coefficients (0.84 ± 0.07 and 0.61 ± 0.12) between the extracted tibial fascicle/medial gastrocnemius and peroneal fascicle/tibialis anterior muscle were obtained. Analysis calculating the information transfer rate (ITR) from the muscle to the motor predictions yielded approximately 5 and 1 bit per second (bps) for the two sources. This method can predict motor signals from neural recordings and could be used to drive a prosthesis by interfacing with residual nerves.

Chronic interfacing with the autonomic nervous system using carbon nanotube (CNT) yarn electrodes.

The ability to reliably and safely communicate chronically with small diameter (100-300 µm) autonomic nerves could have a significant impact in fundamental biomedical research and clinical applications. However, this ability has remained elusive with existing neural interface technologies. Here we show a new chronic nerve interface using highly flexible materials with axon-like dimensions. The interface was implemented with carbon nanotube (CNT) yarn electrodes to chronically record neural activity from two separate autonomic nerves: the glossopharyngeal and vagus nerves. The recorded neural signals maintain a high signal-to-noise ratio (>10 dB) in chronic implant models. We further demonstrate the ability to process the neural activity to detect hypoxic and gastric extension events from the glossopharyngeal and vagus nerves, respectively. These results establish a novel, chronic platform neural interfacing technique with the autonomic nervous system and demonstrate the possibility of regulating internal organ function, leading to new bioelectronic therapies and patient health monitoring.

Model-based Bayesian signal extraction algorithm for peripheral nerves.

OBJECTIVE: Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. APPROACH: To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, hybrid Bayesian signal extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. MAIN RESULTS: The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10-20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. SIGNIFICANCE: HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of controlling a prosthetic limb.