Marco Bonizzato

Biography

Marco Bonizzato is a control, electrical and life sciences engineer with over ten years’ experience in implantable brain-computer interfaces and neuromodulation technology. He has unique double expertise in (a) neural prostheses, and (b) machine intelligence and optimization.

Bonizzato is an assistant professor of electrical engineering at Polytechnique Montréal and an adjunct professor of neuroscience at Université de Montréal.

He also directs the Polytechnique’s sciNeurotech Lab, whose research goal is to develop the entire translational arc of new neurostimulation therapies aimed at restoring sensorimotor function after neurotrauma—from discovery in rodents to application in human medical technologies tailored and personalized to the user using AI.

Current Students

Alireza Aminian

PhD - Polytechnique Montréal

Website

Ivan Brillo

Research Intern - Polytechnique Montréal

Website

Davide Burchielli

PhD - Polytechnique Montréal

Radu Darie

Postdoctorate - Polytechnique Montréal

Postdoctorate - Polytechnique Montréal

Juan Guerra

Master's Research - Polytechnique Montréal

Co-supervisor :

PhD - Université de Montréal

Github

Jules Merckel

Research Intern - Université de Montréal

Mauricio Rivera

Master's Research - Université de Montréal

Co-supervisor :

Salamanca-Giron Salamanca-Giron

Postdoctorate - Polytechnique Montréal

Website

Github

Publications

Gait training combined with transcutaneous spinal stimulation to enhance lower limbs motor recovery in people with spinal cord injury: Pilot Study

Nicolas Hoang Quang

Marianne Cossette-Levasseur

Sammy-Jo Beauregard-Veillette

Nancy Dubé

El-Mehdi Meftah

Héloïse Bourgeois

Nok-Yeung Law

Amedeo Ceglia

Marina Martinez

Diana Zidarov

Dorothy Barthélemy

2025-12-31

Neuromodulation: Technology at the Neural Interface (published)

Modulation of leg trajectory by transcranial magnetic stimulation during walking

Héloïse Bourgeois

Rose Guay-Hottin

El-Mehdi Meftah

Marina Martinez

Dorothy Barthélemy

The primary motor cortex is involved in initiation and adaptive control of locomotion. However, the role of the motor cortex in controlling … (see more)gait trajectories remains unclear. In animals, cortical neuromodulation allows for precise control of step height. We hypothesized that a similar control framework applies to humans, whereby cortical stimulation would primarily increase foot elevation. Transcranial magnetic stimulation (TMS) was applied over the motor cortex to assess the involvement of the corticospinal tract over the limb trajectory during human walking. Ten healthy adults (aged 20–32 years) participated in treadmill walking at 1.5 km/h. TMS was applied over the left motor cortex at an intensity of 120% of the threshold to elicit a dorsiflexion of the right ankle during the swing phase of gait. Electromyographic (EMG) measurements and three-dimensional (3D) lower limb kinematics were collected. When delivered during the early swing phase, TMS led to a significant increase in the maximum height of the right toe by a mean of 34.9% ± 9.6% (21.4 mm ± 7.9 mm, p = 0.032) and knee height by 52.8% ± 14.1% (28.8 mm ± 7.7 mm, p = 0.0021) across participants. These findings indicate that TMS can influence limb trajectory during walking, highlighting its potential as a tool for studying cortical control of locomotion.

2025-06-30

Scientific Reports (published)

Combining cortical and spinal stimulation maximizes improvement of gait after spinal cord injury

Roxanne Drainville

Davide Burchielli

Rose Guay-Hottin

Alexandre Sheasby

Marina Martinez

Most spinal cord injuries (SCI) spare descending motor pathways and sublesional networks, which can be activated through motor cortex and sp… (see more)inal cord stimulation to mitigate locomotor deficits. However, the potential synergy between cortical and spinal stimulation as a neuroprosthetic intervention remains unknown. Here, we first investigated phase-locked electrical stimulation of the motor cortex and lumbar spinal cord at 40 Hz in a rat model of unilateral SCI. Combining cortical and lumbar stimulation around the anticipated lift synergistically enhanced leg movements. When integrated into rehabilitation training, cortical stimulation proved essential for recovery of skilled locomotion. As a further refinement, we next investigated the effects of high-frequency (330 Hz) lumbar and sacral stimulation combined with cortical stimulation. Timely integration during the swing phase showed that cortical and rostral lumbar stimulations enhance the initial and mid-swing phases, while sacral stimulation improves extension velocity in the late swing. These findings indicate that supraspinal and sublesional neuromodulation offer complementary neuroprosthetic effects in targeted SCI gait rehabilitation. Cortical and spinal stimulations summate motor outputs via distinct pathways. Each improves gait post-SCI, but combined stimulation maximizes gait improvement. Integrating cortico-spinal stimulation into rehabilitation promotes lasting recovery. EES capabilities extended using high-frequency lumbosacral protocols.

2025-05-26

BioRxiv (preprint)

Bidirectional Information Flow (BIF) -- A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization

Juan D. Guerra

Thomas Garbay

Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing for different models to address each part, mak… (see more)ing them well-suited for problems with inherent hierarchical structure. However, typical H-GP models do not fully take advantage of this structure, only sending information up or down the hierarchy. This one-way coupling limits sample efficiency and slows convergence. We propose Bidirectional Information Flow (BIF), an efficient H-GP framework that establishes bidirectional information exchange between parent and child models in H-GPs for online training. BIF retains the modular structure of hierarchical models - the parent combines subtask knowledge from children GPs - while introducing top-down feedback to continually refine children models during online learning. This mutual exchange improves sample efficiency, enables robust training, and allows modular reuse of learned subtask models. BIF outperforms conventional H-GP Bayesian Optimization methods, achieving up to 4x and 3x higher

2024-12-31

arXiv (preprint)

arxiv.org

Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training

Shahrad Mohammadzadeh

Juan David Guerra

Reihaneh Rabbany

Golnoosh Farnadi

As large language models (LLMs) become increasingly prevalent, concerns about their reliability, particularly due to hallucinations - factua… (see more)lly inaccurate or irrelevant outputs - have grown. Our research investigates the relationship between the uncertainty in training dynamics and the emergence of hallucinations. Using models from the Pythia suite and several hallucination detection metrics, we analyze hallucination trends and identify significant variance during training. To address this, we propose Sensitivity Dropout (SenD), a novel training protocol designed to reduce hallucination variance during training by deterministically dropping embedding indices with significant variability. In addition, we develop an unsupervised hallucination detection metric, Efficient EigenScore (EES), which approximates the traditional EigenScore in 2x speed. This metric is integrated into our training protocol, allowing SenD to be both computationally scalable and effective at reducing hallucination variance. SenD improves test-time reliability of Pythia and Meta's Llama models by up to 17% and enhances factual accuracy in Wikipedia, Medical, Legal, and Coding domains without affecting downstream task performance.

2024-12-31

ACL (1) (published)

openreview.net

Robust prior-biased acquisition function for human-in-the-loop Bayesian optimization.

Rose Guay-Hottin

Lison Kardassevitch

Hugo Pham

2024-12-31

Knowl. Based Syst. (published)

Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats

Leo Choiniere

Rose Guay-Hottin

Rémi Picard

Numa Dancause

2024-02-29

STAR Protocols (published)

Cortical neuroprosthesis-mediated functional ipsilateral control of locomotion in rats with spinal cord hemisection

Elena Massai

Isley De Jesus

Roxanne Drainville

Marina Martinez

Abstract Control of voluntary limb movement is predominantly attributed to the contralateral motor cortex. However, increasi… (see more)ng evidence suggests the involvement of ipsilateral cortical networks in this process, especially in motor tasks requiring bilateral coordination, such as locomotion. In this study, we combined a unilateral thoracic spinal cord injury (SCI) with a cortical neuroprosthetic approach to investigate the functional role of the ipsilateral motor cortex in rat movement through spared contralesional pathways. Our findings reveal that in all SCI rats, stimulation of the ipsilesional motor cortex promoted a bilateral synergy. This synergy involved the elevation of the contralateral foot along with ipsilateral hindlimb extension. Additionally, in two out of seven animals, stimulation of a sub-region of the hindlimb motor cortex modulated ipsilateral hindlimb flexion. Importantly, ipsilateral cortical stimulation delivered after SCI immediately alleviated multiple locomotor and postural deficits, and this effect persisted after ablation of the homologous motor cortex. These results provide strong evidence of a causal link between cortical activation and precise ipsilateral control of hindlimb movement. This study has significant implications for the development of future neuroprosthetic technology and our understanding of motor control in the context of spinal cord injury.

2023-12-26

eLife (published)

Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review.

Jordan J. Levett

Lior M. Elkaim

Farbod Niazi

Michael H. Weber

Christian Iorio-Morin

Alexander G. Weil

2023-10-31

Neuromodulation: Technology at the Neural Interface (published)

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

Rose Guay Hottin

Sandrine L. Côté

Elena Massai

Leo Choiniere

Uzay Macar

Samuel Laferrière

Parikshat Sirpal

Stephan Quessy

Marina Martinez

Numa Dancause

Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged ne… (see more)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 (published)

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

David Bergeron

Christian Iorio-Morin

Nathalie Orr Gaucher

Éric Racine

Alexander G. Weil

2023-02-28

Journal of Child Neurology (published)

Implementing automation in deep brain stimulation: has the time come?

Alfonso Fasano

2022-11-30

The Lancet Digital Health (published)