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

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

Maîtrise recherche - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Stagiaire de recherche - UdeM

Publications

One hundred years of EEG for brain and behaviour research
Faisal Mushtaq
Dominik Welke
Anne Gallagher
Yuri G. Pavlov
Layla Kouara
Jorge Bosch-Bayard
Jasper JF 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 J. 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 A. 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 D. Hassall
Katherine E. Hiley
Richard B. Ivry
Michael Jenkins
Jakob Kaiser
Andreas Keil
Robert T. Knight
Silvia Kochen
Boris Kotchoubey
Olave E. 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
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
Stefanie Blain-Moraes
Caffeine induces age-dependent increases in brain complexity and criticality during sleep
Philipp Thölke
Maxine Arcand-Lavigne
Tarek Lajnef
Sonia Frenette
Julie Carrier
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… (voir plus)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.
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… (voir plus)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.
Behavioral Imitation with Artificial Neural Networks Leads to Personalized Models of Brain Dynamics During Videogame Play
Anirudha Kemtur
Fraçois Paugam
Basile Pinsard
Yann Harel
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… (voir plus)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