Portrait of Karim Jerbi

Karim Jerbi

Associate Academic Member
karim.jerbi@mila.quebec
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
hamza.abdelhedi@mila.quebec
Website
Github
Google Scholar
Maryem Benslimane
Master's Research - Université de Montréal
maryem.benslimane@mila.quebec
Hugo Berard
Postdoctorate - Université de Montréal
Co-supervisor :
Eva Portelance
hugo.berard@mila.quebec
Samin Mahdipour Aghabagher
Professional Master's - Université de Montréal
samin.mahdipour@mila.quebec
Website
Github
Google Scholar
Yorguin Mantilla Ramos
Master's Research - Université de Montréal
yorguin.mantilla@mila.quebec
Website
Github
Google Scholar

Publications

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… (see more)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.
2026-07-01
Imaging Neuroscience (published)
doi.org
Critical dynamics in spontaneous EEG predict perturbational complexity in disorders of consciousness with measurable evoked responses
Derek Newman
Charlotte Maschke
Jordan O‘Byrne
Michele Colombo
Angela Comanducci
Silvia Casarotto
Giuseppe Citerio
M Rosanova
Marcello Massimini
Karim Jerbi
Stefanie Blain‐Moraes
Abstract Identifying which severely brain-injured patients retain the capacity for consciousness remains a major challenge in neurocritical … (see more)care. The perturbational complexity index (PCI) provides a reliable assessment of consciousness capacity, but its reliance on transcranial magnetic stimulation and EEG (TMS-EEG) limits bedside scalability. PCI and brain criticality capture complementary dimensions of brain dynamics: PCI quantifies the complexity of the brain’s evoked response to perturbation, whereas criticality characterizes the intrinsic organization of spontaneous activity. Here, we tested whether resting-state EEG signatures of criticality predict PCI max in disorders of consciousness, extending prior findings from anesthesia to severe brain injury. In 26 patients with vascular, traumatic, or anoxic brain injury, multivariate criticality related features did not generalize PCI max prediction across the full heterogeneous cohort. However, criticality features predicted PCI max when analyses were restricted to non-anoxic patients and when restricting analyses to patients with non-zero PCI max values. These findings suggest that spontaneous criticality measures index the brain’s intrinsic dynamical regime that supports complex perturbational responses, while their correspondence with PCI max depends on whether the injured brain retains sufficient capacity to sustain large-scale evoked responses. Together, our results extend the relationship between resting-state criticality and evoked perturbational complexity to disorders of consciousness and support the development of stratified EEG measures in severe brain injury.
2026-06-08
bioRxiv (accepted)
doi.org
Gamer in the scanner : Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content
Yann Harel
Basile Pinsard
Julie A. Boyle
Valentina Borghesani
Maximilien Le Clei
Paul-Henri Mignot
Marie St-Laurent
Karim Jerbi
Lune Bellec
André Cyr
Abstract In recent years, videogames have gathered interest in cognitive neuroscience for their potential to study cognition in dynamical an… (see more)d naturalistic contexts. Yet, the complexity of game environments often challenges traditional modeling approaches, and current annotation methods—typically manual or based on modified games—remain labor-intensive and limited in scope. Here, we introduce a flexible and scalable framework using the gym-retro Python library to emulate a classic action-platformer, Shinobi III: Return of the Ninja Master (Sega, 1993), and automatically annotate gameplay events directly from the game’s memory states. This setup enables the identification of both player actions (e.g., jumping, hitting) and feedback events (e.g., killing an enemy, being hit), without modifying the game. Four individuals played the videogame for a combined total of 32 hours (>7 hours each) while undergoing functional magnetic resonance imaging (fMRI). Resulting activation maps revealed distributed engagement of visual, motor, executive, and limbic systems, consistent with the cognitive demands of gameplay. Within-participant reproducibility of brain responses across sessions was robust across event types (r ≈ .25–.55), with some consistency observed even for rarer events like HealthLoss. Between-participant correlations were notably lower, reflecting participant-specific neural signatures. Multivoxel pattern analysis showed that brain responses to different in-game events were highly discriminable, with classification accuracy typically around or above 90%, though occasionally dropping to ~40% for less frequent events. These findings demonstrate that automated emulator-based annotations enable robust, interpretable, and scalable mapping of naturalistic cognitive processes using commercial videogames.
2026-05-31
Imaging Neuroscience (published)
doi.org
openreview.net
LSD Reconfigures Cortical Dynamics Through Faster Brain Rhythms and Increased Fractal Dimension
Venkatesh Subramani
Timothy Nest
Annalisa Pascarella
Jérémy Brunel
Yorguin José Mantilla Ramos
Yann Harel
Suresh Muthukumaraswamy
Robin Carhart-Harris
Giulia Lioi
Nicolas Farrugia
Karim Jerbi
Lysergic acid diethylamide (LSD) profoundly alters conscious experience, yet the electrophysiological mechanisms by which it reshapes neural… (see more) dynamics remain incompletely understood. A hallmark of psychedelic states is widespread cortical desynchronization, typically inferred from reductions in spectral power, but whether such effects reflect genuine weakening of neural oscillations or are confounded by shifts in oscillatory peak frequencies remains unresolved. Here, we address this gap by combining source-resolved magnetoencephalography (MEG), spectral parameterization, temporal complexity metrics, and interpretable machine learning in an LSD versus placebo design, with and without music. We show that LSD induces robust, spatially structured increases in alpha and beta peak frequencies alongside genuine attenuation of oscillatory power, with these effects displaying partly dissociable cortical patterns. Beyond rhythmic activity, LSD is associated with flattening of the aperiodic 1/f spectral slope and increased neural signal fractality and complexity, preferentially affecting sensory, language, emotion, and imagery-related networks while sparing motor cortex. Machine-learning analyses further identify peak-frequency shifts, aperiodic parameters, and complexity measures as key discriminators of the psychedelic state. Music does not robustly amplify these neural signatures and instead shows a trend toward attenuation. Together, these findings provide a comprehensive electrophysiological account of how LSD reorganizes large-scale human brain dynamics and highlight features that may differentiate its neural signature from that of other psychedelics.
2026-05-28
bioRxiv (preprint)
doi.org
Beyond Sensory Summation: How Expectations and Sensory Evidence Shape Multisensory Perception
Elizaveta Sycheva
Léa St-Gelais
Jérémy Brunel
Karim Jerbi
Shahab Bakhtiari
Franco Lepore
Vanessa Hadid
Perceptual decisions arise through the interplay of incoming sensory evidence and prior expectations. However, it remains unclear how this i… (see more)nteraction shapes multisensory integration during the accumulation of decision evidence over time. Using dynamic audiovisual (AV) scenes in a semantic decision task, we examined how sensory reliability and semantic expectations influence decision-making. AV signals that were both coherent and congruent accelerated responses relative to unimodal conditions. This facilitation was strongest when visual input was degraded, consistent with increased reliance on joint AV contributions, as indicated by race-model violations demonstrating multisensory coactivation. Diffusion modeling revealed increased drift rates alongside longer non-decision times, indicating stronger evidence accumulation despite additional sensory processing, and resulting in faster overall responses. Together, these findings reveal that multisensory integration is not a fixed sensory-gain mechanism but a context-dependent coactivation process that selectively enhances evidence accumulation when signals converge on a shared semantic interpretation.
2026-05-13
Cognitive Computational Neuroscience (poster)
openreview.net
openreview.net
Distinct SMA beta bursts support the development of anticipatory postural control in children
Viktoriya O. Manyukhina
Fanny Barlaam
Judith Vergne
Anaëlle Bain
Oussama Abdoun
Sébastien Daligault
Claude Delpuech
Karim Jerbi
Sandrine Sonié
Mathilde Bonnefond
C. Schmitz
Abstract To compensate for self-generated movement-induced postural disturbances, the brain generates anticipatory postural adjustments (APA… (see more)), ensuring smooth, coordinated actions. APA development continues into late adolescence, yet the specific pathways and mechanisms that remain immature in children are poorly understood. We studied APA mechanisms in 24 children (7-12 years old) using magnetoencephalography (MEG) while they performed the naturalistic bimanual load-lifting task (BLLT). In the BLLT, participants lift a load placed on one forearm with the contralateral hand while keeping the postural forearm horizontal, as if lifting a glass from a tray. To counteract forearm deflection caused by unloading, the brain generates APAs, which involve anticipatory inhibition of the postural Biceps brachii . We found that stronger anticipatory Biceps brachii inhibition was associated with reduced excitability, as indexed by high-gamma (90-130 Hz) suppression, and increased high-beta power (19-29 Hz) in the contralateral Supplementary Motor Area (SMA). Analysis of transient beta events revealed two functionally distinct burst types: (1) 19-24 Hz bursts: time-locked to immediate high-gamma suppression correlated with 26-28 Hz beta power; predicted stronger muscle inhibition and received directed input from middle frontal cortex and precentral gyrus; (2) 24-29 Hz bursts: linked to delayed (∼100 ms) high-gamma suppression correlated with 8 Hz alpha power; predicted earlier and prolonged muscle inhibition and better forearm stabilization, but did not show directional influence from other regions. Results on anticipatory inhibition-related beta bursts replicated mechanisms reported in adults, suggesting that the efferent pathways and transient inhibitory processes underlying APA may already be mature in children. In contrast, higher-frequency beta bursts revealed a child-specific, complementary APA mechanism that may compensate for imprecise anticipatory inhibition. These results reveal two oscillatory mechanisms supporting APA in children and indicate that beta bursts may reflect both immediate cortical inhibition linked to muscle control and indirect alpha-mediated inhibition likely compensating for forearm instability.
2026-03-18
bioRxiv (preprint)
doi.org
LSD Relaxes Structural Constraints on Brain Dynamics and Default Mode Decoupling Tracks Ego Dissolution
Venkatesh Subramani
Annalisa Pascarella
Jérémy Brunel
Yann Harel
Suresh Muthukumaraswamy
Robin Carhart-Harris
Karim Jerbi
Giulia Lioi
Nicolas Farrugia
Abstract Psychedelics profoundly alter conscious experience, yet how they reshape the relationship between brain anatomy and function remain… (see more)s unclear. In particular, it is unknown whether psychedelic states reflect a global disruption of structure–function organization or a frequency– and network-specific reconfiguration of neural dynamics relative to the structural connectome. Here we address this question using source-localized magnetoencephalography mapped onto connectome harmonics to quantify structure–function coupling in humans under lysergic acid diethylamide (LSD) and placebo. LSD induces a robust decoupling of low-frequency (theta, alpha and beta) activity from anatomical constraints, indicating a global loosening of structure-aligned large-scale dynamics. In contrast, high-frequency gamma activity shows selective reorganization rather than uniform disruption. Greater gamma-band decoupling within core default-mode network regions predicts the intensity of ego dissolution across individuals, demonstrating that while LSD broadly alters large-scale dynamics, subjective loss of self is specifically linked to frequency-selective reorganization of the default-mode network. Functional decoding reveals that LSD does not produce indiscriminate disintegration but instead drives system-specific rebalancing, with preferential decoupling of visual and attentional systems and strengthened coupling within auditory networks. Together, these findings provide electrophysiological evidence that psychedelic states emerge from a frequency-dependent relaxation of structural constraints on brain activity and identify default-mode reorganization as a neural correlate of ego dissolution. These results offer a mechanistic framework for understanding how LSD may exert therapeutic effects by transiently relaxing rigid structural constraints and enhancing dynamical flexibility within networks involved in self-related processing.
2026-03-04
bioRxiv (preprint)
doi.org
Using virtual reality hypnosis during stem cell transplant for patients in hematology: A protocol for a feasibility randomized study
Audrey Laurin
Floriane Rousseaux
Isaiah Gitonga
Jean Roy
Mathieu Landry
Richard LeBlanc
Nadia Godin
Caroline Arbour
Philippe Richebé
Karim Jerbi
Pierre Rainville
David Ogez
Valentyn Fournier
ClinicalTrials.gov NCT06817759.
2026-02-26
PLoS ONE (published)
doi.org
Using virtual reality hypnosis during stem cell transplant for patients in hematology: A protocol for a feasibility randomized study
Audrey Laurin
Floriane Rousseaux
Isaiah Gitonga
Jean Roy
Mathieu Landry
Richard LeBlanc
Nadia Godin
Caroline Arbour
Philippe Richebé
Karim Jerbi
Pierre Rainville
David Ogez
Valentyn Fournier
ClinicalTrials.gov NCT06817759.
2026-02-26
PLoS ONE (published)
doi.org
Divergent creativity in humans and large language models
Antoine Bellemare-Pepin
François Lespinasse
Philipp Thölke
Yann Harel
Kory Mathewson
Jay A. Olson
Yoshua Bengio
Karim Jerbi
Psychology Department
U. Montr'eal
Montreal
Qc
Canada
Music department
C. University
Sociology
Anthropology department
Mila
Departmentof Psychology
University of Toronto Mississauga … (see 5 more)
Mississauga
On
Department of Computer Science
Operations Research
Unique Center
The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilitie… (see more)s. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs’ semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. These divergence-based measures index associative thinking—the ability to access and combine remote concepts in semantic space—an established facet of creative cognition. We benchmark performance on the Divergent Association Task (DAT) and across multiple creative-writing tasks (haiku, story synopses, and flash fiction), using identical, objective scoring. We found evidence that LLMs can surpass average human performance on the DAT, and approach human creative writing abilities, yet they remain below the mean creativity scores observed among the more creative segment of human participants. Notably, even the top performing LLMs are still largely surpassed by the aggregated top half of human participants, underscoring a ceiling that current LLMs still fail to surpass. We also systematically varied linguistic strategy prompts and temperature, observing reliable gains in semantic divergence for several models. Our human-machine benchmarking framework addresses the polemic surrounding the imminent replacement of human creative labor by AI, disentangling the quality of the respective creative linguistic outputs using established objective measures. While prompting deeper exploration of the distinctive elements of human inventive thought compared to those of AI systems, we lay out a series of techniques to improve their outputs with respect to semantic diversity, such as prompt design and hyper-parameter tuning.
2026-01-20
Scientific Reports (published)
doi.org
arxiv.org
Meditation induces shifts in neural oscillations, brain complexity, and critical dynamics: novel insights from MEG
Annalisa Pascarella
Philipp Thölke
David Meunier
Jordan O'Byrne
Tarek Lajnef
Antonino Raffone
Roberto Guidotti
Vittorio Pizzella
Laura Marzetti
Karim Jerbi
While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We exam… (see more)ined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open-monitoring technique. By combining source-space magnetoencephalography with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity, and criticality in meditation. In addition to power spectral density, we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel–Ziv complexity, 1/f slope, Higuchi fractal dimension, and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, alongside widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence likely due to the correction of the power spectrum by the 1/f slope, which could reduce potential confounds from broadband 1/f activity. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open-monitoring meditation practices.
2025-11-22
Neuroscience of Consciousness (published)
doi.org
Biotuner: A python toolbox integrating music theory and signal processing for harmonic analysis of physiological and natural time series
Antoine Bellemare-Pepin
Karim Jerbi
The Biotuner Toolbox is an open-source Python toolbox for biosignals that integrates concepts from neuroscience, music theory, and signal pr… (see more)ocessing. It introduces a harmonic perspective on physiological oscillations by applying musical constructs such as consonance, rhythm, and scale construction. The core biotuner_object processes neural, cardiac, and auditory time series, providing a unified interface for extracting spectral peaks, computing harmonicity metrics, and supporting downstream analyses. Companion modules extend harmonic analyses across temporal (time-resolved harmonicity), spatial (harmonic connectivity), and spectral (harmonic spectrum) dimensions. Biotuner identifies harmonic structure across different biosignals, revealing significant variations in harmonicity between physiological states. Specifically, the toolbox extracts spectral peaks from complex signals using multiple algorithms, ensuring robust peak detection under varying signal-to-noise ratios. Moreover, we show how harmonicity metrics change across distinct sleep stages and capture variations in the slopes of the aperiodic (1/f) component of the power spectrum. Biotuner provides an extensible framework that unifies music-theoretic constructs with biosignal processing, enabling hypothesis-driven analyses for researchers and, in parallel, creative exploration of complex natural patterns for artists.
2025-11-20
Brain Informatics (published)
doi.org