This dataset maps the relationship between vagus nerve stimulation (VNS) parameters to their immediate neural and cardiac effects in 2 pigs under anaesthesia.
Study Purpose: The vagus nerve innervates the heart (and other organs) whilst offering surgically convenient access points. This has fuelled interest in developing vagus nerve stimulation (VNS) therapies for conditions like heart failure (HF). Despite a number of promising early trials, to date there have been no successful Phase III VNS trials for HF. A key learning from failed trials is that static stimulation parameters for HF VNS lead to poor population-level outcomes and personalisation is necessary. State of the art approaches select VNS stimulation parameters by monitoring short-term physiological outcomes and adjusting parameters to find preferential responses like the “neural fulcrum” (the VNS parameter region that sees the transition from tachycardia to bradycardia). Having said that, the method requires sampling a lot of VNS parameters and could be somewhat unreliable due to the heart rate depending on various internal and external factors like time of day and caffeine intake. This dataset contributes to our understanding of the relationship between VNS parameters and the organ responses. It does so by mapping VNS parameters (at a fine resolution) to their immediate neural and cardiac effects in 2 pigs under anaesthesia.
Data Collection: This dataset contains cardiac and neural responses to VNS at high sampling rate. This allows us to develop a sample-efficient stimulation parameter optimisation algorithm. It also enables us to identify the recruitment levels of individual fibre types in the vagus. By comparing these with recorded physiological responses, we aim to derive novel insight into the neural pathways of cardiac control via VNS.
Primary Conclusion: The dataset enabled us to create a “VNS heart response” simulator. We used it to develop an optimizer that can sample-efficiently find the stimulation parameters needed to achieve a specific heart rate response. It also shows a relationship between B-fibre recruitment and heart rate responses. This is consistent with what’s known in the literature and seems like a promising future research avenue for VNS parameter optimisation for HF.
Curator's Notes
Experimental Design: The dataset contains data from 2 porcine subjects with 20+ vagus nerve stimulations each. A grid of stimulations is conducted by varying stimulation current (0.05mA to 2.5mA) and frequency (2Hz to 30Hz). Pulses are delivered through bipolar cuff electrodes. Stimulations have a 500 microsecond pulse duration and a 5 second train period. For each stimulation, a 30 second window of vagus nerve and ECG signal was recorded. Neural signals are recorded through two bipolar ring cuff electrodes positioned rostrally from the stimulation cuff.
Completeness: This dataset is complete.
Subjects & Samples: Two female pigs were used in this study.
Primary vs derivative data: Primary data is organized in folders per subject ID and contains physiology time series data stored as hdf5 file. Ther is no derivative data folder.
**Code Availability:**To help get started with this dataset, a python code is available at code/data_helper.py
. Dependencies are available at code/requirements.txt
. Refer to the README file for details.
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Wernisch, L., Edwards, T., Berthon, A., Tessier-Lariviere, O., Sarkans, E., Stoukidi, M., Fortier-Poisson, P., Pinkney, M., Thornton, M., Hanley, C., Lee, S., Jennings, J., Appleton, B., Garsed, P., Patterson, B., Buttinger, W., Gonshaw, S., Jakopec, M., Shunmugam, S., … Hewage, E. (2023). Online Bayesian Optimization of Nerve Stimulation. https://doi.org/10.1101/2023.08.30.555315