Portrait of Karim Jerbi

Karim Jerbi

Associate Academic Member
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
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Maryem Benslimane
Master's Research - Université de Montréal
Samin Mahdipour Aghabagher
Professional Master's - Université de Montréal
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Yorguin Mantilla Ramos
Master's Research - Université de Montréal
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Publications

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
2024-08-05
Communications Biology (published)
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
2024-08-05
Communications Biology (published)
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
2024-08-05
Communications Biology (published)
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
How well a person responds to hypnosis is a stable trait, which exhibits considerable inter-individual diversity across the general populati… (see more)on. Yet, its neural underpinning remains elusive. Here, we address this gap by combining EEG data, multivariate statistics, and machine learning in order to identify brain patterns that differentiate between individuals high and low in susceptibility to hypnosis. In particular, we computed the periodic and aperiodic components of the EEG power spectrum, as well as graph theoretical measures derived from functional connectivity, from data acquired at rest (pre-induction) and under hypnosis (post-induction). We found that the 1/f slope of the EEG spectrum at rest was the best predictor of hypnotic susceptibility. Our findings support the idea that hypnotic susceptibility is a trait linked to the balance of cortical excitation and inhibition at baseline and offers novel perspectives on the neural foundations of hypnotic susceptibility. Future work can explore the contribution of background 1/f activity as a novel target to distinguish the responsiveness of individuals to hypnosis at baseline in the clinic. Significance Statement Hypnotic phenomena reflect the ability to alter one’s subjective experiences based on targeted verbal suggestions. This ability varies greatly 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.
2024-07-15
bioRxiv (preprint)
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,… (see more) especially during sleep. We analyzed sleep EEG in 40 subjects, contrasting 200mg 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-REM 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) whose sleep brain dynamics were less affected by caffeine. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance provides novel insights into the effects caffeine has on the sleeping brain.
2024-06-01
bioRxiv (preprint)
doi.org
Neuro-GPT: Towards A Foundation Model for EEG
Wenhui Cui
Woojae Jeong
Philipp Thölke
Takfarinas Medani
Karim Jerbi
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.
2024-05-27
2024 IEEE International Symposium on Biomedical Imaging (ISBI) (published)
doi.org
arxiv.org
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
Karim Jerbi
2024-04-27
Communications Biology (published)
doi.org
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
Karim Jerbi
2024-04-27
Communications Biology (published)
doi.org
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
Karim Jerbi
2024-03-20
bioRxiv (preprint)
doi.org
Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity
Antea D’Andrea
Pierpaolo Croce
Jordan O’Byrne
Karim Jerbi
Annalisa Pascarella
Antonino Raffone
Vittorio Pizzella
Laura Marzetti
2024-02-02
Frontiers in Neuroscience (published)
doi.org
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
Karim Jerbi
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.
2024-01-06
Cerebral Cortex (published)
doi.org
Bio-Mechanical Poet: An Immersive Audiovisual Playground for Brain Signals and Generative AI.
Philipp Thölke
Antoine Bellemare‐Pepin
Yann Harel
François Lespinasse
Karim Jerbi
2024-01-01
ICCC (published)
dblp.uni-trier.de