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Jorin Mamen

jorin.mamen@mila.quebec
Coordonnateur de projets, Recherche avec l'industrie, Projets structurants

Publications

Using neural biomarkers to personalize dosing of vagus nerve stimulation
Antonin Berthon
Lorenz Wernisch
Myrta Stoukidi
Michael Thornton
Olivier Tessier-Larivière
Pascal Fortier-Poisson
Jorin Mamen
Max Pinkney
Susannah Lee
Elvijs Sarkans
Luca Annecchino
Ben Appleton
Philip Garsed
Bret Patterson
Samuel Gonshaw
Matjaž Jakopec
Sudhakaran Shunmugam
Tristan Edwards
Aleksi Tukiainen
Joel Jennings … (voir 3 de plus)
Guillaume Lajoie
Emil Hewage
Oliver Armitage
2024-06-17
Bioelectronic Medicine (publié)
doi.org
Online Bayesian optimization of vagus nerve stimulation.
Lorenz Wernisch
Tristan Edwards
Antonin Berthon
Olivier Tessier-Larivière
Elvijs Sarkans
Myrta Stoukidi
Pascal Fortier-Poisson
Max Pinkney
Michael Thornton
Catherine Hanley
Susannah Lee
Joel Jennings
Ben Appleton
Philip Garsed
Bret Patterson
Buttinger Will
Samuel Gonshaw
Matjaž Jakopec
Sudhakaran Shunmugam
Jorin Mamen … (voir 4 de plus)
Aleksi Tukiainen
Guillaume Lajoie
Oliver Armitage
Emil Hewage
OBJECTIVE In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimu… (voir plus)lation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects. Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate, making it an ideal candidate for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to vagus nerve stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target heart rates and optimizing neural B-fiber activations despite high intersubject variability. Significance. An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing. .
2024-03-13
Journal of Neural Engineering (publié)
doi.org
Online Bayesian Optimization of Nerve Stimulation
Lorenz Wernisch
Tristan Edwards
Antonin Berthon
Olivier Tessier-Larivière
Elvijs Sarkans
Myrta Stoukidi
Pascal Fortier-Poisson
Max Pinkney
Michael Thornton
Catherine Hanley
Susannah Lee
Joel Jennings
Ben Appleton
Philip Garsed
Bret Patterson
Buttinger Will
Samuel Gonshaw
Matjaž Jakopec
Sudhakaran Shunmugam
Jorin Mamen … (voir 4 de plus)
Aleksi Tukiainen
Guillaume Lajoie
Oliver Armitage
Emil Hewage
2023-09-01
bioRxiv (prépublication)
doi.org