Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys

Bonizzato, Marco; Hottin, Rose Guay; Côté, Sandrine L.; Massai, Elena; Choinière, Léo; Macar, Uzay; Laferrière, Samuel; Sirpal, Parikshat; Quessy, Stephan; Lajoie, Guillaume; Martinez, Marina; Dancause, Numa

doi:10.1016/j.xcrm.2023.101008

Cited by 14 publications

(49 citation statements)

References 72 publications

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“…Several simulation studies on neuromodulation models, that enable a precise comparison with some ground truth, show its promising performance when optimizing parameters (Grado et al 2018, Mao et al 2023, Aiello et al 2023. Evaluations using simulated data have consistently showcased BO's superior performance compared to random and grid searches-a point we highlight in this study as well-or greedy strategies (Bonizzato et al 2023). We emphasize that this study's primary objective was not to pioneer a completely new approach for optimizing nerve stimulations.…”

Section: Performance Of Oboesmentioning

confidence: 72%

“…BO is a computational technique that seeks to identify optimal parameters with a minimum number of exploratory steps. BO algorithms have recently emerged as promising optimization tools for stimulations of the nervous system in several studies: for optimizing epidural spinal cord stimulation in rats (Desautels et al 2015), for intracortical stimulation in monkeys (Laferriere et al 2020), for distal limb movement in monkeys (Losanno et al 2021, Bonizzato et al 2023, treatment of epilepsy (Park et al 2020, Stieve et al 2023), and improved memory (Ashmaig et al 2018) in deep brain stimulation. Simulation studies, utilizing models based on neuromodulation data, demonstrate the potential of BO in comparison to other optimization methods (Grado et al 2018, Mao et al 2023, Aiello et al 2023.…”

Section: Introductionmentioning

confidence: 99%

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Online Bayesian optimization of vagus nerve stimulation

Wernisch,

Edwards,

Berthon

et al. 2024

J. Neural Eng.

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Objective. In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation 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. 

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Section: Performance Of Oboesmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Online Bayesian optimization of vagus nerve stimulation

Wernisch,

Edwards,

Berthon

et al. 2024

J. Neural Eng.

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“…Approximately 6 months after the array implantation, ICMS motor mapping using short trains delivered through three arrays was done during a few experimental sessions. These data were collected for another set of experiments that have been previously published ( Bonizzato et al, 2023 ). During these sessions, monkeys sat calmly while treats were intermittently offered between blocks of stimulations.…”

Section: Methodsmentioning

confidence: 99%

Differential Modulation of Local Field Potentials in the Primary and Premotor Cortices during Ipsilateral and Contralateral Reach to Grasp in Macaque Monkeys

Falaki,

Quessy,

Dancause

2024

J. Neurosci.

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Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1), and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤ 13 Hz) and gamma frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, delta oscillations were greater during contralateral reach and grasp, and beta activity increased during contralateral grasp. In contrast, beta oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.Significance StatementWe compared local field potentials (LFPs) from the primary motor cortex (M1), the dorsal and ventral premotor cortices (PMd and PMv) while monkeys performed reach and grasp with the contralateral or ipsilateral hand. In general, hand-related differences were greater in M1 than in premotor areas. During both contralateral and ipsilateral trials, LFPs were more similar when comparing the two premotor areas than comparing M1 to PMd or PMv. However, the pattern of modulations in each area had unique features. The combination of these signals is likely essential to support the flexibility and complexity of unilateral hand movements. Our results help to understand the neural substrate that allows cortical areas to concurrently contribute to different aspects of movement planning and production.

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“…The ability to manipulate brain activity directly through external stimuli of various physical natures is a major driver in advancing innovative therapeutic strategies. In fact, neurostimulation techniques are widely used as neuroscientific investigation tools, in the development of neuroprostheses, and very successfully in the treatment of a broad variety of neurological conditions, such as Parkinson’s Disease ( Cole et al, 2022 ), epilepsy ( Krishna et al, 2016 ; Li and Cook, 2018 ; Dell et al, 2019 ; Davis and Gaitanis, 2020 ; Ryvlin et al, 2021 ), distinct cognitive ( Bonizzato et al, 2023 ; Kolmos et al, 2023 ), and psychiatric dysfunctions ( Nuttin et al, 2014 ), to promote functional recovery in brain injury patients ( Bao et al, 2020 ), including stroke ( Kolmos et al, 2023 ). On the other hand and despite its established status, the effectiveness of neurostimulation varies considerably among patients and across clinical trials of different applications.…”

Section: Introductionmentioning

confidence: 99%

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Carè,

Chiappalone,

Cota

2024

Front. Neurosci.

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Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

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Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys

Cited by 14 publications

References 72 publications

Online Bayesian optimization of vagus nerve stimulation

Online Bayesian optimization of vagus nerve stimulation

Differential Modulation of Local Field Potentials in the Primary and Premotor Cortices during Ipsilateral and Contralateral Reach to Grasp in Macaque Monkeys

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

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