Portrait of Karim Jerbi

Karim Jerbi

Associate Academic Member
Associate Professor, Université de Montréal, Department of Psychology


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

PhD - Université de Montréal
Principal supervisor :
Master's Research - Université de Montréal
Co-supervisor :


Human local field potentials in motor and non-motor brain areas encode upcoming movement direction.
Etienne Combrisson
Franck Di Rienzo
Anne-Lise Saive
Marcela Perrone-Bertolotti
Juan LP Soto
Philippe Kahane
Jean-Philippe Lachaux
Aymeric Guillot
Structure-function coupling and decoupling during movie-watching and resting-state: Novel insights bridging EEG and structural imaging
Venkatesh Subramani
Giulia Lioi
Nicolas Farrugia
Unravelling the neural dynamics of hypnotic susceptibility: Aperiodic neural activity as a central feature of hypnosis
Mathieu Landry
Jason da Silva Castanheira
Catherine Boisvert
Floriane Rousseaux
Jérôme Sackur
Amir Raz
Philippe Richebé
David Ogez
Pierre Rainville
Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity
Antea D’Andrea
Pierpaolo Croce
Jordan O’Byrne
Annalisa Pascarella
Antonino Raffone
Vittorio Pizzella
Laura Marzetti
GABAergic inhibition shapes behavior and neural dynamics in human visual working memory
Jan Kujala
Carolina Ciumas
Julien Jung
Sandrine Bouvard
Françoise Lecaignard
Amélie Lothe
Romain Bouet
Philippe Ryvlin
Abstract Neuronal inhibition, primarily mediated by GABAergic neurotransmission, is crucial for brain development and healthy cognition. Gam… (see more)ma-aminobutyric acid concentration levels in sensory areas have been shown to correlate with hemodynamic and oscillatory neuronal responses. How these measures relate to one another during working memory, a higher-order cognitive process, is still poorly understood. We address this gap by collecting magnetoencephalography, functional magnetic resonance imaging, and Flumazenil positron emission tomography data within the same subject cohort using an n-back working-memory paradigm. By probing the relationship between GABAA receptor distribution, neural oscillations, and Blood Oxygen Level Dependent (BOLD) modulations, we found that GABAA receptor density in higher-order cortical areas predicted the reaction times on the working-memory task and correlated positively with the peak frequency of gamma power modulations and negatively with BOLD amplitude. These findings support and extend theories linking gamma oscillations and hemodynamic responses to gamma-aminobutyric acid neurotransmission and to the excitation-inhibition balance and cognitive performance in humans. Considering the small sample size of the study, future studies should test whether these findings also hold for other, larger cohorts as well as to examine in detail how the GABAergic system and neural fluctuations jointly support working-memory task performance.
Neuro-GPT: Towards A Foundation Model for EEG
Wenhui Cui
Woojae Jeong
Philipp Thölke
Takfarinas Medani
Anand A. Joshi
Richard M. Leahy
To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the p… (see more)ower of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG. The code is publicly available at github.com/wenhui0206/NeuroGPT.
Behavioral Imitation with Artificial Neural Networks Leads to Personalized Models of Brain Dynamics During Videogame Play
Anirudha Kemtur
Fraçois Paugam
Basile Pinsard
Pravish Sainath
Maximilien Le Clei
Julie Boyle
Artificial Neural networks (ANN) trained on complex tasks are increasingly used in neuroscience to model brain dynamics, a process called br… (see more)ain encoding. Videogames have been extensively studied in the field of artificial intelligence, but have hardly been used yet for brain encoding. Videogames provide a promising framework to understand brain activity in a rich, engaging, and active environment. A major challenge raised by complex videogames is that individual behavior is highly variable across subjects, and we hypothesized that ANNs need to account for subject-specific behavior in order to properly capture brain dynamics. In this study, we used ANNs to model functional magnetic resonance imaging (fMRI) and behavioral gameplay data, both collected while subjects played the Shinobi III 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 or control models. 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 combining imitation learning, brain imaging, and videogames can allow us to model complex individual brain patterns derived from decision making in a rich, complex environment.
Open design of a reproducible videogame controller for MRI and MEG
Yann Harel
André Cyr
Julie Boyle
Basile Pinsard
Jeremy Bernard
Marie-France Fourcade
Himanshu Aggarwal
Ana Fernanda Ponce
Bertrand Thirion
Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation
Elisabetta Vallarino
Ana-Sofía Hincapié Casas
R. Leahy
Annalisa Pascarella
Alberto Sorrentino
Sara Sommariva
Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia
Charlotte Maschke
Jordan O’Byrne
Michele Angelo Colombo
Melanie Boly
Olivia Gosseries
Steven Laureys
Mario Rosanova
Stefanie Blain-Moraes
Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits ad… (see more)aptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigated dynamical properties of the resting-state electroencephalogram of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. We then studied the relation of these dynamic properties with the perturbational complexity index (PCI), which has shown remarkably high sensitivity in detecting consciousness independent of behavior. 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. We estimated (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related measures, and found that states of unconsciousness were characterized by a distancing from both the edge of activity propagation and the edge of chaos. We were then able to predict individual subjects’ PCI (i.e., PCImax) with a mean absolute error below 7%. Our results establish a firm link between the PCI and criticality and provide further evidence for the role of criticality in the emergence of consciousness. 2 Significance Statement Complexity has long been of interest in consciousness science and had a fundamental impact on many of today’s theories of consciousness. The perturbational complexity index (PCI) uses the complexity of the brain’s response to cortical perturbations to quantify the presence of consciousness. We propose criticality as a unifying framework underlying maximal complexity and sensitivity to perturbation in the conscious brain. We demonstrate that criticality measures derived from resting-state electroencephalography can distinguish conscious from unconscious states, using propofol, xenon and ketamine anesthesia, and from these measures we were able to predict the PCI with a mean error below 7%. Our results support the hypothesis that critical brain dynamics are implicated in the emergence of consciousness and may provide new directions for the assessment of consciousness.
Autonomic nervous system modulation during self-induced non-ordinary states of consciousness
Victor Oswald
Audrey Vanhaudenhuyse
Jitka Annen
Charlotte Martial
Aminata Bicego
Floriane Rousseaux
Corine Sombrun
Yann Harel
Marie-Elisabeth Faymonville
Steven Laureys
Olivia Gosseries
Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study
Etienne Combrisson
Franck Di Rienzo
Anne-Lise Saive
Marcela Perrone-Bertolotti
Juan LP Soto
Philippe Kahane
Jean-Philippe Lachaux
Aymeric Guillot