Karim Jerbi

hamza.abdelhedi@mila.quebec

Doctorat - UdeM

Maîtrise recherche - UdeM

maryem.benslimane@mila.quebec

Hugo Berard

Postdoctorat - UdeM

Co-superviseur⋅e :

Eva Portelance

hugo.berard@mila.quebec

Samin Mahdipour Aghabagher

Maîtrise professionnelle - UdeM

samin.mahdipour@mila.quebec

Site web

Github

Google Scholar

Yorguin Mantilla Ramos

Maîtrise recherche - UdeM

yorguin.mantilla@mila.quebec

Maîtrise recherche - UdeM

syrine.matoussi@mila.quebec

Site web

Github

Google Scholar

Publications

From Signals to Trajectories: A Primer on Low-Dimensional Dynamics in Human EEG and MEG

Vanessa Hadid

Annalisa Pascarella

Sara A. Solla

Paul Cisek

Neural activity unfolds not as a set of independent signals, but as a coordinated dynamical process that can be described as a point moving … (voir plus)through a high-dimensional state space. This perspective has contributed substantially to recent developments in systems neuroscience, especially through studies of directly recorded neuronal population activity, but remains comparatively underused in non-invasive human recordings such as EEG and MEG. In this primer, we present a neural trajectory analysis based on Principal Component Analysis (PCA) as an accessible route into extending the concepts of neural trajectories in state space to human electrophysiology. We explain how EEG/MEG activity can be organized into neural state representations, projected onto axes organized by decreasing covariance, truncated into low-dimensional representations, and visualized as trajectories that capture how distributed activity evolves over time. We show how trajectory geometry can be used to compare conditions and relate neural dynamics to behavior or clinical outcomes. An example of EEG motor execution and imagery, accompanied by a tutorial notebook, illustrates the workflow from conventional EEG summaries to trajectory-based analysis. We also clarify the limits of PCA, including its linear, variance-driven nature, and discuss EEG/MEG-specific challenges such as spatial mixing, preprocessing sensitivity, validation, and overinterpretation. Finally, we situate PCA within a broader family of state-space methods. By making neural trajectories practical and interpretable, this primer offers a guide for bringing the conceptual framework of population-level dynamics into mainstream human cognitive neuroscience.

2026-09-25

arXiv (prépublication)

Task-optimized neural networks reveal distinct contributions of specialized and broader visual learning to neural representations of face familiarity

Shahab Bakhtiari

How neural activity across the ventral visual hierarchy supports face recognition is an open question. A long-standing debate asks whether f… (voir plus)ace processing, particularly in fusiform cortex, relies on face-specific computations or representations shared with broader visual recognition. Here we combine source-resolved magnetoencephalography (MEG) with task-optimized neural networks as controlled computational models of visual experience. Rather than manipulating long-term expertise in human observers, we systematically vary learning objective on the model side—what the networks are trained to recognize—while holding architecture and loss function constant within model comparisons. We then ask which learned representational geometries best align with neural responses, where and when. We measured millisecond-resolved brain–model alignment across V1, lateral occipital cortex (LOC) and fusiform cortex while participants viewed familiar, unfamiliar and scrambled faces. The same stimuli were presented to seven CNN architectures trained for face-identity recognition (FR), object-category recognition (OR) or object categorization including a face category (Dual), alongside untrained controls. Familiarity produced a stage-dependent dissociation: in LOC, familiar faces showed earlier brain–model alignment than unfamiliar faces in the M170 range, an effect most consistent in models trained for face-recognition, whereas broader objectives produced more variable, architecture-dependent peak alignment latencies. In fusiform cortex they showed stronger alignment around the M200 range. This fusiform advantage was not uniquely associated with face-recognition training: dual- and object-trained models showed greater fusiform correspondence than face-trained models. Together, these findings support stage-dependent specialization, with training for face-identity recognition constraining intermediate-stage timing while later fusiform representations remain compatible with representational structure acquired through broader visual computations. Significance Statement Recognizing a familiar face feels immediate, yet it remains unclear which stages of visual processing are specifically shaped by learning individual identities. We combine millisecond-resolved MEG with task-optimized neural networks used as controllable models of visual experience, manipulating learning objective on the model side—that is, what the networks are trained to recognize and tracking when and where the resulting representations align with human brain activity. Familiarity advances representational alignment in lateral occipital cortex but strengthens later alignment in fusiform cortex. The earlier LOC effect was most consistent across architectures after face-identity recognition training, whereas the later fusiform effect also emerged under broader visual learning objectives. These findings support a stage-dependent account of face recognition that moves beyond a simple face-specific versus broader-visual-processing dichotomy.

2026-09-08

bioRxiv (prépublication)

A Roadmap for MEG Foundation Models

Philipp Thölke

Yorguin Mantilla-Ramos

Fouad Lbakali

Oumayma Gharbi

Catherine Duclos

Annalisa Pascarella

Vanessa Hadid

Oiwi Parker Jones

Foundation models are beginning to reshape brain-signal analysis by moving the field beyond task-specific decoding pipelines toward reusable… (voir plus) models pretrained on broad neural datasets. Magnetoencephalography (MEG) is a compelling but still underdeveloped target for this shift: it captures human cortical dynamics at millisecond resolution while offering stronger spatial interpretability than EEG, making it especially valuable for source-resolved studies of perception, language, cognition, and clinical brain function. Yet MEG foundation models remain at an early stage, with only a small number of MEG-specific and MEG-inclusive multi-modal models, modest pretraining corpora, and emerging but still limited benchmarks. This perspective lays down the basic concepts needed to understand MEG foundation models and provides a didactic overview of the field's key design choices, including tokenization, sensor- versus source-space representations, sensor-geometry encoding, backbone architectures, self-supervised objectives, and pretraining data. We then offer a roadmap for future development, organized around native MEG pretraining, adaptation of EEG foundation models, transfer from generic time-series models, and multi-modal integration with EEG, fMRI, MRI, behaviour, and stimulus features. We highlight the need for coordinated infrastructure, including diverse and reusable MEG datasets, rigorous evaluation across subjects, sites, tasks, and clinical settings, and responsible data-sharing practices that address consent, privacy, access, and governance.

2026-09-02

arXiv (prépublication)

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

Francesco Mantegna

Gereon Elvers

Dulhan Jayalath

Gilad Landau

Tasha Kim

Miran Ozdogan

Luisa Kurth

Teyun Kwon

SungJun Cho

Benjamin Ballyk

Alex Fung

Anna Greer

Pratik Somaiya

Christian Herff

Yorguin Mantilla Ramos

Greg Farquhar

Brendan Shillingford

Mark Woolrich … (voir 1 de plus)

Oiwi Parker Jones

The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Desi… (voir plus)gned to progress from foundational tasks toward the linguistic complexity required for a practical brain-computer interface (BCI), it set the stage with speech detection and phoneme classification tasks. Winning submissions reached F1-macro scores of 95.6% and 73.6% on the respective tasks (Elvers et al., 2026), highly significant advances. This success was built on the LibriBrain dataset (Özdogan et al., 2025), the largest within-subject MEG dataset recorded at the time with

2026-09-02

arXiv (prépublication)

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

Francesco Mantegna

Gereon Elvers

Dulhan Jayalath

Gilad Landau

Tasha Kim

Miran Ozdogan

Luisa Kurth

Teyun Kwon

SungJun Cho

Benjamin Ballyk

Alex Fung

Anna Greer

Pratik Somaiya

Christian Herff

Yorguin Mantilla Ramos

Greg Farquhar

Brendan Shillingford

Mark Woolrich … (voir 1 de plus)

Oiwi Parker Jones

2026-09-02

arXiv (prépublication)

Resting-state neural oscillations predict individual differences in verbal learning and encoding strategy use

Victor Oswald

Mathieu Landry

Sarah Lippé

Philippe Robaey

Individuals adopt different encoding strategies to facilitate learning, yet few studies have examined the neurophysiological basis of these … (voir plus)strategies across individuals. The present work addresses this gap by extending our previous findings on the direct relationship between cortical spectral power, measured via resting-state magnetoencephalography, and standard cognitive performance, to test whether resting-state neural features predict individual differences in encoding strategy preferences. Our results highlight the complex interactions between endogenous brain oscillations, learning, and verbal encoding strategies assessed by the California Verbal Learning Test-Second Edition (CVLT-2). First, resting-state theta oscillations were significantly associated with verbal learning and subjective clustering strategies. Second, semantic clustering was facilitated by oscillatory patterns in the left sensory-motor regions. Finally, serial and semantic clustering strategies showed opposite regression patterns, indicating a competitive interaction. Together, these findings provide insights into resting-state neural markers associated with diverse encoding strategies in verbal learning.

2026-08-31

iScience (publié)

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

Pierre Bellec

Videogames provide a promising framework to understand brain activity in a rich, engaging, and active environment, in contrast to mostly pas… (voir plus)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 (publié)

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

Stefanie Blain‐Moraes

Abstract Identifying which severely brain-injured patients retain the capacity for consciousness remains a major challenge in neurocritical … (voir plus)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 (accepté)

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

Paul-Henri Mignot

André Cyr

Abstract In recent years, videogames have gathered interest in cognitive neuroscience for their potential to study cognition in dynamical an… (voir plus)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 (publié)

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

Lysergic acid diethylamide (LSD) profoundly alters conscious experience, yet the electrophysiological mechanisms by which it reshapes neural… (voir plus) 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 (prépublication)

Beyond Sensory Summation: How Expectations and Sensory Evidence Shape Multisensory Perception

Elizaveta Sycheva

Léa St-Gelais

Jérémy Brunel

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… (voir plus)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

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

Sandrine Sonié

Mathilde Bonnefond

C. Schmitz

Abstract To compensate for self-generated movement-induced postural disturbances, the brain generates anticipatory postural adjustments (APA… (voir plus)), 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 (prépublication)