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

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

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

Publications

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
Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
Philipp Thölke
Yorguin-José Mantilla-Ramos
Hamza Abdelhedi
Charlotte Maschke
Arthur Dehgan
Yann Harel
Anirudha Kemtur
Loubna Mekki Berrada
Myriam Sahraoui
Tammy Young
Antoine Bellemare Pépin
Clara El Khantour
Mathieu Landry
Annalisa Pascarella
Vanessa Hadid
Etienne Combrisson
Jordan O’Byrne
Differential and overlapping effects between exogenous and endogenous attention shape perceptual facilitation during visual processing
Mathieu Landry
Jason da Silva Castanheira
Aperiodic brain activity and response to anesthesia vary in disorders of consciousness
Charlotte Maschke
Catherine Duclos
Adrian M. Owen
Stefanie Blain-Moraes
Stefanie
Rhythmic Information Sampling in the Brain during Visual Recognition
Laurent Caplette
Frédéric Gosselin
Processing visual ambiguity in fractal patterns: Pareidolia as a sign of creativity
Antoine Bellemare Pépin
Yann Harel
Jordan O’Byrne
Geneviève Mageau
Arne Dietrich
Magnetoencephalography resting-state correlates of executive and language components of verbal fluency
Victor Oswald
Younes Zerouali
Aubrée Boulet-Craig
Maja Krajinovic
Caroline Laverdière
Daniel Sinnett
Pierre Jolicoeur
Sarah Lippé
Philippe Robaey
Optimizing deep learning for Magnetoencephalography (MEG): From sensory perception to sex prediction and brain fingerprinting
Arthur Dehgan
Processing visual ambiguity in fractal patterns: Pareidolia as a sign of creativity
Antoine Bellemare
Yann Harel
Jordan O’Byrne
Genevieve A. Mageau
Arne Dietrich
Magnetoencephalography resting-state correlates of executive and language components of verbal fluency
Victor Oswald
Younes Zerouali
Aubrée Boulet-Craig
M. Krajinovic
Caroline Laverdière
D. Sinnett
Pierre W. Jolicoeur
Sarah Lippé
Philippe Robaey