Portrait of Karim Jerbi

Karim Jerbi

Associate Academic Member
karim.jerbi@mila.quebec
Associate Professor, Université de Montréal, Department of Psychology
Research Topics
Computational Neuroscience
Data Mining
Dynamical Systems
Natural Language Processing
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Biography

Karim Jerbi is a professor in the Department of Psychology at Université de Montréal. He holds the Canada Research Chair in Computational Neuroscience and Cognitive Neuroimaging, and is the director of UNIQUE (Unifying Neuroscience and Artificial Intelligence in Quebec). A member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists, Jerbi obtained a PhD in cognitive neuroscience and brain imaging from the Pierre & Marie Curie University in Paris and a biomedical engineering degree from the University of Karlsruhe (Germany).

Jerbi’s research lies at the crossroads of cognitive, computational and clinical neuroscience. The goal of his research is to probe the role of large-scale brain dynamics in higher-order cognition and to investigate brain network alterations in the case of psychiatric and neurological disorders. The multidisciplinary research conducted in his laboratory combines magnetoencephalography (MEG), scalp- and intracranial electroencephalography (EEG) with advanced signal processing and data analytics, including machine learning. Ongoing projects in his lab use electrophysiological brain recordings to examine large-scale brain network dynamics in a range of cognitive processes (e.g., decision-making and creativity) and across different states of consciousness (resting wakefulness, sleep, dreaming, anesthesia, meditation and psychedelic states). Jerbi is also strongly committed to the promotion of social justice, equity, diversity and inclusion in academia, and he has a keen interest in the convergence between brain science, AI, creativity and art.

Current Students

Hamza Abdelhedi
PhD - Université de Montréal
hamza.abdelhedi@mila.quebec
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Github
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Maryem Benslimane
Master's Research - Université de Montréal
maryem.benslimane@mila.quebec
Hugo Berard
Postdoctorate - Université de Montréal
Co-supervisor :
Eva Portelance
hugo.berard@mila.quebec
Samin Mahdipour Aghabagher
Professional Master's - Université de Montréal
samin.mahdipour@mila.quebec
Website
Github
Google Scholar
Yorguin Mantilla Ramos
Master's Research - Université de Montréal
yorguin.mantilla@mila.quebec
Website
Github
Google Scholar

Publications

Combining Virtual Reality and Hypnosis? A User Experience Study in Patients with Multiple Myeloma Following Stem Cell Transplantation
Jade Véronneau
Alexandra Chevestrier-Lefeuvre
Valentyn Fournier
Audrey Laurin
Rémi Caron-Trahan
Mathieu Landry
Joséphine Guiné
Odile Dubey-Harispe
Nadia Godin
Idrissi Moulay
Danny Wade
Sandie Oberoi
Caroline Arbour
Philippe Richebé
Karim Jerbi
Pierre Rainville
Richard LeBlanc
Floriane Rousseaux
David Ogez
Multiple myeloma (MM) and stem cell transplantation (SCT) significantly impact patients’ quality of life. Virtual reality with hypnosis (V… (see more)RH) has emerged as a promising nonpharmacological intervention to address these challenges, yet data on its acceptability and user experience remain scarce. This study assessed the acceptability and user experience of a VRH intervention among adult patients with MM who had undergone allogeneic SCT. Participants used a VRH application and rated their experience through standardized questionnaires and semistructured interviews. Quantitative data were analyzed descriptively, and qualitative data underwent descriptive content analysis. Findings indicated high patients’ satisfaction, strong perceived relevance, and low cybersickness. Qualitative analysis revealed perceived emotional and psychological benefits. VRH was deemed particularly suitable during hospitalization and treatment periods. This study shows the potential of combining virtual reality and hypnosis for MM patients following SCT. Indeed, they showed high satisfaction levels, paving the way for further studies evaluating the clinical efficacy of such interventions.
2025-10-31
Journal of Patient Experience (published)
doi.org
Combining virtual reality and hypnosis to alleviate chronic pain in elderly with hand arthritis: protocol for a randomised phase II clinical trial
Valentyn Fournier
Marie-Fania Simard
Sai Yan Yuen
Joséphine Guiné
Floriane Rousseaux
Julie Lebeau
Karim Jerbi
Philippe Richebé
Mathieu Landry
Pierre Rainville
David Ogez
Chronic pain is a common health condition that significantly impacts the quality of life of those affected, affecting one in five people in … (see more)Canada. The prevalence of this condition tends to increase with age, making it a major health issue given the ageing population. However, its management remains inadequate and requires significant mobilisation of healthcare professionals as well as the development of multiple therapeutic solutions. Among these, non-pharmacological interventions such as hypnosis and virtual reality have proven effective. Nevertheless, while the existing literature seems promising, it presents methodological limitations. Therefore, this study aims to assess the effectiveness of an intervention combining virtual reality and hypnosis in an ageing population suffering from a widespread chronic pain condition, that is, hand arthritis. This study will be a single-centre randomised clinical trial. Participants will be randomly assigned to one of two conditions: one receiving an intervention combining virtual reality and hypnosis, and the other receiving only virtual reality. The effectiveness of the intervention on current perceived pain before and after the intervention (primary outcome) will be evaluated. Secondary outcomes will include anxiety and depressive symptoms, quality of life, relaxation and fatigue. Exploratory analyses will also be conducted to contribute to the emerging literature by examining physiological variables such as heart rate variability, respiratory rate and electrodermal activity during the intervention, and their relationship with primary and secondary outcomes. The project was approved by the Research Ethical Committee of the Hospital Maisonneuve-Rosemont (Project no 2024-3539). Participants will be asked to provide written consent for their participation. Results from this study will be shared through peer-reviewed publications, as well as oral and poster presentations at scientific events. The protocol for this study was preregistered on Open Science Framework and raw anonymised data will be available on this platform ( https://osf.io/vbh72/?view_only=1d17c5708f894faab6669d85e1fde75d ). NCT06833905 .
2025-09-30
BMJ Open (published)
doi.org
Intrinsic Neural Oscillations Predict Verbal Learning Performance and Encoding Strategy Use
Victor Oswald
Mathieu Landry
Hamza Abdelhedi
Sarah Lippé
Philippe Robaey
Karim Jerbi
2025-09-27
BioRxiv (preprint)
doi.org
Effects of a Virtual Reality Hypnosis Intervention on Chronic Pain: A User Experience and Proof-of-concept Study
Alexandra Chevestrier-Lefeuvre
Joséphine Guiné
Jade Véronneau
Julie Lebeau
Floriane Rousseaux
Audrey Laurin
Marie-Fania Simard
Nadia Godin
Karim Jerbi
Philippe Richebé
Mathieu Landry
Pierre Rainville
Valentyn Fournier
David Ogez
Abstract

Chronic pain is a significant public health issue in Canada, with approximately one in four Canadians ove… (see more)r the age of 15 living with this condition. Due to its impact on individuals—both physically and psychologically—and its financial burden on the healthcare system, it is crucial to develop cost-effective and efficient treatment methods. Hypnosis and virtual reality have emerged as promising solutions in this context. This study aims to evaluate the preliminary efficacy and feasibility of an intervention combining virtual reality and hypnosis. The study involved 30 patients with chronic pain who were invited to test a hypnosis application delivered through a virtual reality device. Levels of pain, anxiety, and relaxation were measured before and after the intervention, while satisfaction, cybersickness, and user experience were evaluated post-intervention. At the end of the intervention, participants were invited to participate in a semi-structured interview to provide feedback on their satisfaction with the experience. Participants reported high levels of satisfaction with the intervention, a positive user experience, and minimal symptoms of cybersickness. The intervention was effective in reducing anxiety (W = 173.5, p = .002) and pain (W = 253.5, p< .001) while significantly enhancing relaxation levels (W = 9.00, p< .001). This intervention demonstrated effectiveness in reducing pain and anxiety while improving relaxation levels among individuals with chronic pain, paving the way for further investigations of the involved mechanisms.

2025-07-13
Research Square (preprint)
doi.org
Decoding Humor-Induced Amusement via Facial Expression Analysis: Toward Emotion-Aware Applications
Gabrielle Toupin
Arthur Dehgan
Marie Buffo
Clément Feyt
Golnoush Alamian
Karim Jerbi
Anne-Lise Saive
Humor is widely recognized for its positive effects on well-being, including stress reduction, mood enhancement, and cognitive benefits. Yet… (see more), the lack of reliable tools to objectively quantify amusement—particularly its temporal dynamics—has limited progress in this area. Existing measures often rely on self-report or coarse summary ratings, providing little insight into how amusement unfolds over time. To address this gap, we developed a Random Forest model to predict the intensity of amusement evoked by humorous video clips, based on participants’ facial expressions—particularly the co-activation of Facial Action Units 6 and 12 (“% Smile”)—and video features such as motion, saliency, and topic. Our results show that exposure to humorous content significantly increases “% Smile”, with amusement peaking toward the end of videos. Importantly, we observed emotional carry-over effects, suggesting that consecutive humorous stimuli can sustain or amplify positive emotional responses. Even when trained solely on humorous content, the model reliably predicted amusement intensity, underscoring the robustness of our approach. Overall, this study provides a novel, objective method to track amusement on a fine temporal scale, advancing the measurement of nonverbal emotional expression. These findings may inform the design of emotion-aware applications and humor-based therapeutic interventions to promote well-being and emotional health.
2025-07-02
Applied Sciences (published)
doi.org
Artificial Neural Networks for Magnetoencephalography: A review of an emerging field
Arthur Dehgan
Hamza Abdelhedi
Vanessa Hadid
Irina Rish
Karim Jerbi
Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive proces… (see more)ses with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.
2025-05-26
Journal of Neural Engineering (published)
doi.org
arxiv.org
Caffeine induces age-dependent increases in brain complexity and criticality during sleep
Philipp Thölke
Maxine Arcand-Lavigne
Tarek Lajnef
Sonia Frenette
Julie Carrier
Karim Jerbi
Caffeine is the most widely consumed psychoactive stimulant worldwide. Yet important gaps persist in understanding its effects on the brain,… (see more) especially during sleep. We analyzed sleep electroencephalography (EEG) in 40 subjects, contrasting 200 mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. Being most prominent during non-rapid eye movement (NREM) sleep, these results suggest that caffeine shifts the brain towards a critical regime and more diverse neural dynamics. Interestingly, this was more pronounced in younger adults (20–27 years) compared to middle-aged participants (41–58 years) during rapid eye movement (REM) sleep, while no significant age effects were observed during NREM. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance suggests that caffeine promotes a shift in brain dynamics towards increased neural excitation and closer proximity to a critical regime, particularly during NREM sleep.
2025-04-29
Communications Biology (published)
doi.org
Structure–function coupling and decoupling during movie watching and resting state: Novel insights bridging EEG and structural imaging
Venkatesh Subramani
Giulia Lioi
Karim Jerbi
Nicolas Farrugia
The intricate structural and functional architecture of the brain enables a wide range of cognitive processes ranging from perception and ac… (see more)tion to higher-order abstract thinking. Despite important progress, the relationship between the brain’s structural and functional properties is not yet fully established. In particular, the way the brain’s anatomy shapes its electrophysiological dynamics remains elusive. The electroencephalography (EEG) activity recorded during naturalistic tasks is thought to exhibit patterns of coupling with the underlying brain structure that vary as a function of behavior. Yet these patterns have not yet been sufficiently quantified. We address this gap by jointly examining individual Diffusion-Weighted Imaging (DWI) scans and continuous EEG recorded during video-watching and resting state, using a Graph Signal Processing (GSP) framework. By decomposing the structural graph into Eigenmodes and expressing the EEG activity as an extension of anatomy, GSP provides a way to quantify the structure-function coupling. We elucidate how the structure shapes function during naturalistic tasks such as movie-watching and how this association is modulated by tasks. We quantify the coupling relationship in a region-, time-, frequency-resolved manner. First of all, our findings indicate that the EEG activity in the sensorimotor cortex is strongly coupled with brain structure, while the activity in higher-order systems is less constrained by anatomy, i.e., shows more flexibility. In addition, we found that watching videos was associated with stronger structure-function coupling in the sensorimotor cortex, as compared to resting-state data. Second, time-resolved analysis revealed that the unimodal systems undergo minimal temporal fluctuation in structure-function association, and the transmodal system displays highest temporal fluctuations, with the exception of PCC seeing low fluctuations. Lastly, our frequency-resolved analysis revealed a consistent topography across different EEG rhythms, suggesting a similar relationship with the anatomical structure across frequency bands. Together, this unprecedented characterization of the link between structure and function using continuous EEG during naturalistic behavior underscores the role of anatomy in shaping ongoing cognitive processes. Taken together, by combining the temporal and spectral resolution of EEG and the methodological advantages of GSP, our work sheds new light onto the anatomo-functional organization of the brain.
2024-12-31
Imaging Neuroscience (published)
doi.org
Divergent Perception: Framing Creative Cognition Through the Lens of Sensory Flexibility
Antoine Bellemare‐Pepin
Karim Jerbi
Creativity is a cornerstone of human evolution and is typically defined as the multifaceted ability to produce novel and useful artifacts. A… (see more)lthough much research has focused on divergent thinking, growing evidence underscores the importance of perceptual processing in fostering creativity, particularly through perceptual flexibility. The present work aims to offer a framework that relates creativity to perception, showing how sensory affordances, especially in ambiguous stimuli, can contribute to the generation of novel ideas. In doing so, we contextualize the phenomenon of pareidolia, which involves seeing familiar patterns in noisy or ambiguous stimuli, as a key perceptual mechanism of idea generation—one of the central stages of the creative process. We introduce “divergent perception” to describe the process by which individuals actively engage with the perceptual affordances provided by ambiguous sensory information, and illustrate how this concept could account for the heightened creativity observed in psychedelic and psychotic states. Moreover, we explore how divergent perception relates to cognitive mechanisms crucial in creative thinking, particularly focusing on the role of attention. Finally, we discuss future paths for the exploration of divergent perception, including targeted manipulation of stimulus characteristics and the investigation of the intricate interplay between bottom‐up and top‐down cognitive processes.
2024-12-23
The Journal of creative behavior (published)
doi.org
Prediction of Post Traumatic Epilepsy Using <scp>MR</scp>‐Based Imaging Markers
Haleh Akrami
Wenhui Cui
Paul E. Kim
Christianne Heck
Andrei Irimia
Karim Jerbi
Dileep Nair
Richard M. Leahy
Anand A. Joshi
Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high pre… (see more)valence of PTE, current methods for predicting its occurrence remain limited. In this study, we aimed to identify imaging-based markers for the prediction of PTE using machine learning. Specifically, we examined three imaging features: Lesion volumes, resting-state fMRI-based measures of functional connectivity, and amplitude of low-frequency fluctuation (ALFF). We employed three machine-learning methods, namely, kernel support vector machine (KSVM), random forest, and an artificial neural network (NN), to develop predictive models. Our results showed that the KSVM classifier, with all three feature types as input, achieved the best prediction accuracy of 0.78 AUC (area under the receiver operating characteristic (ROC) curve) using nested cross-validation. Furthermore, we performed voxel-wise and lobe-wise group difference analyses to investigate the specific brain regions and features that the model found to be most helpful in distinguishing PTE from non-PTE populations. Our statistical analysis uncovered significant differences in bilateral temporal lobes and cerebellum between PTE and non-PTE groups. Overall, our findings demonstrate the complementary prognostic value of MR-based markers in PTE prediction and provide new insights into the underlying structural and functional alterations associated with PTE.
2024-11-18
Human Brain Mapping (published)
doi.org
Ongoing Dynamics of Peak Alpha Frequency Characterize Hypnotic Induction in Highly Hypnotic-Susceptible Individuals
Mathieu Landry
Jason da Silva Castanheira
Floriane Rousseaux
Pierre Rainville
David Ogez
Karim Jerbi
Hypnotic phenomena exhibit significant inter-individual variability, with some individuals consistently demonstrating efficient responses to… (see more) hypnotic suggestions, while others show limited susceptibility. Recent neurophysiological studies have added to a growing body of research that shows variability in hypnotic susceptibility is linked to distinct neural characteristics. Building on this foundation, our previous work identified that individuals with high and low hypnotic susceptibility can be differentiated based on the arrhythmic activity observed in resting-state electrophysiology (rs-EEG) outside of hypnosis. However, because previous work has largely focused on mean spectral characteristics, our understanding of the variability over time of these features, and how they relate to hypnotic susceptibility, is still limited. Here we address this gap using a time-resolved assessment of rhythmic alpha peaks and arrhythmic components of the EEG spectrum both prior to and following hypnotic induction. Using multivariate pattern classification, we investigated whether these neural features differ between individuals with high and low susceptibility to hypnosis. Specifically, we used multivariate pattern classification to investigate whether these non-stationary neural features could distinguish between individuals with high and low susceptibility to hypnosis before and after a hypnotic induction. Our analytical approach focused on time-resolved spectral decomposition to capture the intricate dynamics of neural oscillations and their non-oscillatory counterpart, as well as Lempel–Ziv complexity. Our results show that variations in the alpha center frequency are indicative of hypnotic susceptibility, but this discrimination is only evident during hypnosis. Highly hypnotic-susceptible individuals exhibit higher variability in alpha peak center frequency. These findings underscore how dynamic changes in neural states related to alpha peak frequency represent a central neurophysiological feature of hypnosis and hypnotic susceptibility.
2024-08-29
Brain Sciences (published)
doi.org
100 years of EEG for brain and behaviour research
Faisal Mushtaq
Dominik Welke
Anne Gallagher
Yuri G. Pavlov
Layla Kouara
Jorge Bosch-Bayard
Jasper J. F. van den Bosch
Mahnaz Arvaneh
Amy R. Bland
Maximilien Chaumon
Cornelius Borck
Xun He
Steven J. Luck
Maro G. Machizawa
Cyril Pernet
Aina Puce
Sidney Segalowitz
Christine Rogers
Muhammad Awais
Claudio Babiloni … (see 75 more)
Neil W. Bailey
Sylvain Baillet
Robert C. A. Bendall
Daniel Brady
Maria L. Bringas-Vega
Niko Busch
Ana Calzada-Reyes
Armand Chatard
Peter E. Clayson
Michael X. Cohen
Jonathan Cole
Martin Constant
Alexandra Corneyllie
Damien Coyle
Damian Cruse
Ioannis Delis
Arnaud Delorme
Damien Fair
Tiago H. Falk
Matthias Gamer
Giorgio Ganis
Kilian Gloy
Samantha Gregory
Cameron Hassall
Katherine Hiley
Richard B. Ivry
Karim Jerbi
Michael Jenkins
Jakob Kaiser
Andreas Keil
Robert T. Knight
Silvia Kochen
Boris Kotchoubey
Olave Krigolson
Nicolas Langer
Heinrich R. Liesefeld
Sarah Lippé
Raquel E. London
Annmarie MacNamara
Scott Makeig
Welber Marinovic
Eduardo Martínez-Montes
Aleya A. Marzuki
Ryan K. Mathew
Christoph Michel
José d. R. Millán
Mark Mon-Williams
Lilia Morales-Chacón
Richard Naar
Gustav Nilsonne
Guiomar Niso
Erika Nyhus
Robert Oostenveld
Katharina Paul
Walter Paulus
Daniela M. Pfabigan
Gilles Pourtois
Stefan Rampp
Manuel Rausch
Kay Robbins
Paolo M. Rossini
Manuela Ruzzoli
Barbara Schmidt
Magdalena Senderecka
Narayanan Srinivasan
Yannik Stegmann
Paul M. Thompson
Mitchell Valdes-Sosa
Melle J. W. van der Molen
Domenica Veniero
Edelyn Verona
Bradley Voytek
Dezhong Yao
Alan C. Evans
Pedro Valdes-Sosa
2024-08-21
Nature Human Behaviour (unknown)
doi.org