Portrait de Karim Jerbi

Karim Jerbi

Membre académique associé
Professeur agrégé, Université de Montréal, Département de psychologie
Sujets de recherche
Exploration des données
Neurosciences computationnelles
Systèmes dynamiques
Traitement du langage naturel
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Biographie

Karim Jerbi est professeur agrégé au Département de psychologie de l'Université de Montréal. Il est titulaire de la Chaire de recherche du Canada en neurosciences computationnelles et en neuro-imagerie cognitive et directeur du centre UNIQUE, le centre de recherche en neuro-IA du Québec. Il est membre du Collège de nouveaux chercheurs et créateurs en art et en science de la Société royale du Canada.

Il a obtenu un doctorat en neurosciences cognitives et imagerie cérébrale de l'Université Pierre et Marie Curie à Paris (France) et un diplôme en génie biomédical de l'Université de Karlsruhe (Allemagne). Ses recherches se situent au carrefour des neurosciences cognitives, computationnelles et cliniques. Leur objectif est de sonder le rôle de la dynamique cérébrale à grande échelle dans la cognition d'ordre supérieur et d'étudier les altérations des réseaux cérébraux dans les cas de troubles psychiatriques et neurologiques.

La recherche multidisciplinaire menée dans son laboratoire combine la magnétoencéphalographie (MEG) et l'électroencéphalographie (EEG) du cuir chevelu et intracrânienne avec le traitement avancé des signaux et l'analyse des données, y compris l'apprentissage automatique. Les projets qui y sont en cours utilisent des enregistrements cérébraux électrophysiologiques pour examiner la dynamique des réseaux cérébraux à grande échelle dans une série de processus cognitifs (par exemple la prise de décision et la créativité) et dans différents états de conscience (éveil au repos, sommeil, rêve, anesthésie, méditation et états psychédéliques).

Karim Jerbi est fortement engagé dans la promotion de la justice sociale, de l'équité, de la diversité et de l'inclusion. Il s'intéresse également de près à la convergence entre les sciences du cerveau, l'IA, la créativité et l'art.

Étudiants actuels

Hamza Abdelhedi
Doctorat - UdeM
Site web
Github
Google Scholar
Maryem Benslimane
Maîtrise recherche - UdeM
Samin Mahdipour Aghabagher
Maîtrise professionnelle - UdeM
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Github
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Yorguin Mantilla Ramos
Maîtrise recherche - UdeM
Site web
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Google Scholar

Publications

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 (prépublication)
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… (voir plus)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 (prépublication)
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… (voir plus), 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 (publié)
doi.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,… (voir plus) 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 (publié)
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… (voir plus)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 (publié)
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… (voir plus)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 (publié)
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… (voir plus)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 (publié)
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… (voir plus) 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 (publié)
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 … (voir 75 de plus)
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 (inconnu)
doi.org
Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity
Charlotte Maschke
Jordan O'Byrne
Michele Angelo Colombo
Melanie Boly
Olivia Gosseries
Steven Laureys
Mario Rosanova
Karim Jerbi
Stefanie Blain-Moraes
Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits ad… (voir plus)aptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigate dynamical properties of the resting-state electroencephalogram (EEG) of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams), enabling an experimental dissociation between unresponsiveness and unconsciousness. For each condition, we measure (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related metrics, revealing that states of unconsciousness are characterized by a distancing from both avalanche criticality and the edge of chaos. We then ask whether these same dynamical properties are predictive of the perturbational complexity index (PCI), a TMS-based measure that has shown remarkably high sensitivity in detecting consciousness independently of behavior. We successfully predict individual subjects’ PCI values with considerably high accuracy from resting-state EEG dynamical properties alone. Our results establish a firm link between perturbational complexity and criticality, and provide further evidence that criticality is a necessary condition for the emergence of consciousness.
2024-08-04
Communications Biology (publié)
doi.org
Behavioral Imitation with Artificial Neural Networks Leads to Personalized Models of Brain Dynamics During Videogame Play
Anirudha Kemtur
François Paugam
Basile Pinsard
Yann Harel
Pravish Sainath
Maximilien Le Clei
Julie Boyle
Karim Jerbi
Pierre Bellec
Videogames provide a promising framework to understand brain activity in a rich, engaging, and active environment, in contrast to mostly pas… (voir plus)sive tasks currently dominating the field, such as image viewing. Analyzing videogames neuroimaging data is however challenging, and relies on time-intensive manual annotations of game events, based on somewhat arbitrary rules. Here, we introduce an innovative approach using Artificial Neural networks (ANN) and brain encoding techniques to generate activation maps associated with videogame behaviour using functional magnetic resonance imaging (fMRI). As individual behavior is highly variable across subjects in complex environments, we hypothesized that ANNs need to account for subject-specific behavior to properly capture brain dynamics. In this study, we used data collected while subjects played Shinobi III: Return of the Ninja Master (Sega, 1993), an action-platformer videogame. Using imitation learning, we trained an ANN to play the game while closely replicating the unique gameplay style of individual participants. We found that hidden layers of our imitation learning model successfully encoded task-relevant neural representations, and predicted individual brain dynamics with higher accuracy than models trained on other subjects’ gameplay. Individual-specific models also outperformed a number of baselines to predict brain activity, such as pixel inputs, or button presses. The highest correlations between layer activations and brain signals were observed in biologically plausible brain areas, i.e. somatosensory, attention, and visual networks. Our results demonstrate that training subject-specific ANNs can successfully uncover brain correlates of complex behaviour. This new method combining imitation learning, brain imaging, and videogames opens new research avenues to study decision-making and psychomotor task solving in naturalistic and complex environments.
2024-07-31
bioRxiv (prépublication)
doi.org
Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis
Mathieu Landry
Jason da Silva Castanheira
Catherine Boisvert
Floriane Rousseaux
Jérôme Sackur
Amir Raz
Philippe Richebé
David Ogez
Pierre Rainville
Karim Jerbi
Hypnotic phenomena reflect the ability to alter one’s subjective experiences based on targeted verbal suggestions. This ability varies gre… (voir plus)atly in the population. The brain correlates to explain this variability remain elusive. Addressing this gap, our study employs machine learning to predict hypnotic susceptibility. By recording electroencephalography (EEG) before and after a hypnotic induction and analyzing diverse neurophysiological features, we were able to determine that several features differentiate between high and low hypnotic susceptible individuals both at baseline and during hypnosis. Our analysis revealed that the paramount discriminative feature is non-oscillatory EEG activity before the induction—a new finding in the field. This outcome aligns with the idea that hypnotic susceptibility represents a latent trait observable through a plain five-minutes resting-state EEG.
2023-11-16
bioRxiv (prépublication)
doi.org