Vagus nerve stimulation for the treatment of heart failure

A Body-Temperature-Triggered In Situ Softening Peripheral Nerve Electrode for Chronic Robust Neuromodulation

Implantable peripheral nerve electrodes are crucial for monitoring health and alleviating symptoms of chronic diseases. Advanced compliant electrodes have been developed because of their biomechanical compatibility. However, these mechanically tissue-like electrodes suffer from unmanageable operating forces, leading to high risks of nerve injury and fragile electrode-tissue interfaces. Here, a peripheral nerve electrode is developed that simultaneously fulfills the criteria of body temperature softening and tissue-like modulus (less than 0.8 MPa at 37 °C) after implantation. The central core is altered from the tri-arm crosslinker to the star-branched monomer to kill two birds (close the translation temperature to 37 °C and decrease the modulus after implantation) with one stone. Furthermore, the decreased interfacial impedance (325.1 ± 46.9 Ω at 1 kHz) and increased charge storage capacity (111.2 ± 5.8 mC cm-2) are achieved by an in situ electrografted conductive polymer on the strain-insensitive conductive network of Au nanotubes. The electrodes are readily wrapped around nerves and applied for long-term stimulation in vivo with minimal inflammation. Neuromodulation experiments demonstrate their potential clinical utility, including vagus nerve stimulation in rats to suppress seizures and alleviation of cardiac remodeling in a canine model of myocardial infarction.

Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study

Objective. Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study.Approach.Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate and the mean arterial pressure for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control.Main results.Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems.Significance.We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

Organotopic organization of the porcine mid-cervical vagus nerve

Introduction

Despite detailed characterization of fascicular organization of somatic nerves, the functional anatomy of fascicles evident in human and large mammal cervical vagus nerve is unknown. The vagus nerve is a prime target for intervention in the field of electroceuticals due to its extensive distribution to the heart, larynx, lungs, and abdominal viscera. However, current practice of the approved vagus nerve stimulation (VNS) technique is to stimulate the entire nerve. This produces indiscriminate stimulation of non-targeted effectors and undesired side effects. Selective neuromodulation is now a possibility with a spatially-selective vagal nerve cuff. However, this requires the knowledge of the fascicular organization at the level of cuff placement to inform selectivity of only the desired target organ or function.

Methods and results

We imaged function over milliseconds with fast neural electrical impedance tomography and selective stimulation, and found consistent spatially separated regions within the nerve correlating with the three fascicular groups of interest, suggesting organotopy. This was independently verified with structural imaging by tracing anatomical connections from the end organ with microCT and the development of an anatomical map of the vagus nerve. This confirmed organotopic organization.

Discussion

Here we show, for the first time, localized fascicles in the porcine cervical vagus nerve which map to cardiac, pulmonary and recurrent laryngeal function (N = 4). These findings pave the way for improved outcomes in VNS as unwanted side effects could be reduced by targeted selective stimulation of identified organ-specific fiber-containing fascicles and the extension of this technique clinically beyond the currently approved disorders to treat heart failure, chronic inflammatory disorders, and more.

Data Driven Control of Vagus Nerve Stimulation for the Cardiovascular System: An in Silico Computational Study

Vagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where an electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. Currently, vagus nerve stimulation is under investigation for the treatment of heart failure, cardiac arrhythmia and hypertension. Through several clinical trials that sought to assess vagus nerve stimulation for the treatment of heart failure, stimulation parameters were determined heuristically and the results were inconclusive, which has led to the suggestion of using a closed-loop approach to optimize the stimulation parameters. A recent investigation has demonstrated highly specific control of cardiovascular physiology by selectively activating different fibers in the vagus nerve. When multiple locations and multiple stimulation parameters are considered for optimization, the design of closed-loop control becomes considerably more challenging. To address this challenge, we investigated a data-driven control scheme for both modeling and controlling the rat cardiovascular system. Using an existing in silico physiological model of a rat heart to generate synthetic input-output data, we trained a long short-term memory network (LSTM) to map the effect of stimulation on the heart rate and blood pressure. The trained LSTM was utilized in a model predictive control framework to optimize the vagus nerve stimulation parameters for set point tracking of the heart rate and the blood pressure in closed-loop simulations. Additionally, we altered the underlying in silico physiological model to consider intra-patient variability, and diseased dynamics from increased sympathetic tone in designing closed-loop VNS strategies. Throughout the different simulation scenarios, we leveraged the design of the controller to demonstrate alternative clinical objectives. Our results show that the controller can optimize stimulation parameters to achieve set-point tracking with nominal offset while remaining computationally efficient. Furthermore, we show a controller formulation that compensates for mismatch due to intra-patient variabilty, and diseased dynamics. This study demonstrates the first application and a proof-of-concept for using a purely data-driven approach for the optimization of vagus nerve stimulation parameters in closed-loop control of the cardiovascular system.

Benefits of pharmacological and electrical cholinergic stimulation in hypertension and heart failure

Systemic arterial hypertension and heart failure are cardiovascular diseases that affect millions of individuals worldwide. They are characterized by a change in the autonomic nervous system balance, highlighted by an increase in sympathetic activity associated with a decrease in parasympathetic activity. Most therapeutic approaches seek to treat these diseases by medications that attenuate sympathetic activity. However, there is a growing number of studies demonstrating that the improvement of parasympathetic function, by means of pharmacological or electrical stimulation, can be an effective tool for the treatment of these cardiovascular diseases. Therefore, this review aims to describe the advances reported by experimental and clinical studies that addressed the potential of cholinergic stimulation to prevent autonomic and cardiovascular imbalance in hypertension and heart failure. Overall, the published data reviewed demonstrate that the use of central or peripheral acetylcholinesterase inhibitors is efficient to improve the autonomic imbalance and hemodynamic changes observed in heart failure and hypertension. Of note, the baroreflex and the vagus nerve activation have been shown to be safe and effective approaches to be used as an alternative treatment for these cardiovascular diseases. In conclusion, pharmacological and electrical stimulation of the parasympathetic nervous system has the potential to be used as a therapeutic tool for the treatment of hypertension and heart failure, deserving to be more explored in the clinical setting.