Decoding Vagus-Nerve Activity with Carbon Nanotube Sensors in Freely Moving Rodents

Squalamine reverses age-associated changes of firing patterns of myenteric sensory neurons and vagal fibres

Vagus nerve signaling is a key component of the gut-brain axis and regulates diverse physiological processes that decline with age. Gut to brain vagus firing patterns are regulated by myenteric intrinsic primary afferent neuron (IPAN) to vagus neurotransmission. It remains unclear how IPANs or the afferent vagus age functionally. Here we identified a distinct ageing code in gut to brain neurotransmission defined by consistent differences in firing rates, burst durations, interburst and intraburst firing intervals of IPANs and the vagus, when comparing young and aged neurons. The aminosterol squalamine changed aged neurons firing patterns to a young phenotype. In contrast to young neurons, sertraline failed to increase firing rates in the aged vagus whereas squalamine was effective. These results may have implications for improved treatments involving pharmacological and electrical stimulation of the vagus for age-related mood and other disorders. For example, oral squalamine might be substituted for or added to sertraline for the aged.

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.

A lightweight learning-based decoding algorithm for intraneural vagus nerve activity classification in pigs

OBJECTIVE

Bioelectronic medicine is an emerging field that aims at developing closed-loop neuromodulation protocols for the autonomic nervous system (ANS) to treat a wide range of disorders. When designing a closed-loop protocol for real time modulation of the ANS, the computational execution time and the memory and power demands of the decoding step are important factors to consider. In the context of cardiovascular and respiratory diseases, these requirements may partially explain why closed-loop clinical neuromodulation protocols that adapt stimulation parameters on patient's clinical characteristics are currently missing.

APPROACH

Here, we developed a lightweight learning-based decoder for the classification of cardiovascular and respiratory functional challenges from neural signals acquired through intraneural electrodes implanted in the cervical vagus nerve (VN) of 5 anaesthetized pigs. Our algorithm is based on signal temporal windowing, 9 handcrafted features, and Random Forest (RF) model for classification. Temporal windowing ranging from 50 ms to 1 sec, compatible in duration with cardio-respiratory dynamics, was applied to the data in order to mimic a pseudo real-time scenario.

MAIN RESULTS

We were able to achieve high balanced accuracy (BA) values over the whole range of temporal windowing duration. We identified 500 ms as the optimal temporal windowing duration for both BA values and computational execution time processing, achieving more than 86% for BA and a computational execution time of only ∼6.8 ms. Our algorithm outperformed in terms of balanced accuracy and computational execution time a state of the art decoding algorithm tested on the same dataset [1]. We found that RF outperformed other machine learning models such as Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptrons.

SIGNIFICANCE

Our approach could represent an important step towards the implementation of a closed-loop neuromodulation protocol relying on a single intraneural interface able to perform real-time decoding tasks and selective modulation of the VN.

Intelligent Biosignal Processing in Wearable and Implantable Sensors

Wearable technology including sensors, sensor networks, and the associated devices have opened up space in a variety of applications [...].

Closed-Loop Vagus Nerve Stimulation for the Treatment of Cardiovascular Diseases: State of the Art and Future Directions

The autonomic nervous system exerts a fine beat-to-beat regulation of cardiovascular functions and is consequently involved in the onset and progression of many cardiovascular diseases (CVDs). Selective neuromodulation of the brain-heart axis with advanced neurotechnologies is an emerging approach to corroborate CVDs treatment when classical pharmacological agents show limited effectiveness. The vagus nerve is a major component of the cardiac neuroaxis, and vagus nerve stimulation (VNS) is a promising application to restore autonomic function under various pathological conditions. VNS has led to encouraging results in animal models of CVDs, but its translation to clinical practice has not been equally successful, calling for more investigation to optimize this technique. Herein we reviewed the state of the art of VNS for CVDs and discuss avenues for therapeutic optimization. Firstly, we provided a succinct description of cardiac vagal innervation anatomy and physiology and principles of VNS. Then, we examined the main clinical applications of VNS in CVDs and the related open challenges. Finally, we presented preclinical studies that aim at overcoming VNS limitations through optimization of anatomical targets, development of novel neural interface technologies, and design of efficient VNS closed-loop protocols.