Portrait de Numa Dancause

Numa Dancause

Membre académique associé
labodancause@gmail.com
Professeur titulaire, Université de Montréal
Sujets de recherche
Interface neuronale
Neuroprothèses
Neurosciences
Neurosciences computationnelles
Neurotechnologie
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Publications

Motor Recovery After a Hemispherectomy: Review of Mechanisms and the Potential of Neuromodulation to Enhance Motor Outcomes
David Bergeron
Dorothy Barthélemy
Aristides Hadjinicolaou
Marco Bonizzato
Marina Martinez
Numa Dancause
Alexander G. Weil
In children with severe, refractory hemispheric epilepsy syndromes, the removal or disconnection of the diseased cortex on one hemisphere fr… (voir plus)om the rest of the brain (hemispherectomy) is a last-resort treatment to cure epilepsy. The removal or disconnection of the motor cortex expectedly leads to contralateral hemiparesis. Partial recovery of the leg or proximal arm may occur over time from the plasticity of alternate motor pathways, but finer hand movements generally do not recover. The advent of neuroprostheses delivering invasive or non-invasive stimulation at different levels of the motor pathways holds promise to enhance motor recovery after a neurologic injury. In this manuscript, we review the mechanisms of motor recovery after a hemispherectomy and discuss how emerging neuromodulation options could be used to improve function. We conclude that the most suitable neuromodulation options for short-term clinical trials are vagal nerve stimulation paired with rehabilitation, and tonic spinal cord stimulation (transcutaneous or with implanted electrodes). We also identify promising neuromodulation options that would require further preclinical investigation in animal models: subcortical deep brain stimulation (motor thalamus, contralateral dentate nucleus), brain-spine interfacing, and motor cortex stimulation. Altogether, this manuscript lays the theoretical foundations for the investigation of neuromodulation therapies to improve the motor outcomes of patients who underwent a hemispherectomy for refractory epilepsy.
2026-02-12
Journal of Child Neurology (publié)
doi.org
Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats
Leo Choiniere
Rose Guay-Hottin
Rémi Picard
Guillaume Lajoie
Marco Bonizzato
Numa Dancause
2024-02-29
STAR Protocols (publié)
doi.org
Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys
Marco Bonizzato
Rose Guay Hottin
Sandrine L. Côté
Elena Massai
Leo Choiniere
Uzay Macar
Samuel Laferrière
Parikshat Sirpal
Stephan Quessy
Guillaume Lajoie
Marina Martinez
Numa Dancause
Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged ne… (voir plus)urostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming other search strategies after testing only a fraction of the possible combinations. Through a series of real-time multi-dimensional neurostimulation experiments, we demonstrate optimization across diverse biological targets (brain, spinal cord), animal models (rats, non-human primates), in healthy subjects, and in neuroprosthetic intervention after injury, for both immediate and continual learning over multiple sessions. GP-BO can embed and improve “prior” expert/clinical knowledge to dramatically enhance its performance. These results advocate for broader establishment of learning agents as structural elements of neuroprosthetic design, enabling personalization and maximization of therapeutic effectiveness.
2023-03-31
Cell Reports Medicine (publié)
doi.org
Learning to evoke complex motor outputs with spatiotemporal neurostimulation using a hierarchical and adaptive optimization algorithm.
Samuel Laferrière
Marco Bonizzato
Numa Dancause
Guillaume Lajoie
The development of neurostimulation techniques to evoke motor patterns is an active area of research. It serves as a crucial experimental to… (voir plus)ol to probe computation in neural circuits, and it has applications in neuroprostheses used to aid recovery of function after stroke or injury. There are two important challenges when designing algorithms to unveil and control neurostimulation-to-motor mappings, thereby linking spatiotemporal patterns of neural stimulation to muscle activation: (1) the exploration of motor maps needs to be fast and efficient (exhaustive search is to be avoided for clinical and experimental reasons) (2) online learning needs to be flexible enough to track ongoing changes in these maps. We propose a stimulation search algorithm to address these issues, and demonstrate its efficacy with experiments in non-human primate models. Our solution is a novel iterative process using Bayesian Optimization via Gaussian Processes on increasingly complex signal spaces. We show that our algorithm can successfully and rapidly learn mappings between complex stimulation patterns and evoked muscle activation patterns, where standard approaches fail. Importantly, we uncover nonlinear circuit-level computations in M1 that would not have been possible to identify using conventional mapping techniques.
2021-12-31
Conference on Cognitive Computational Neuroscience (publié)
doi.org