Portrait of Lune Bellec

Lune Bellec

Affiliate Member
Full Professor, Université de Montréal, Department of Psychology
Research Topics
Computational Neuroscience
Medical Machine Learning
NeuroAI
Neuroimaging (fMRI)
Website
Google Scholar
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Biography

I am a professor at the department of Psychology of University of Montreal and the principal investigator of the laboratory for brain simulation and exploration (SIMEXP) at the Montreal Geriatrics Institute (CRIUGM). I recently joined Mila - Quebec Artificial Intelligence Institute as affiliated member, and I supervise students in computer science (cognitive computational neuroscience) at DIRO, University of Montreal.

My main research interest is to train artificial neural networks in order to jointly mimic individual human brain activity and behaviour. To achieve that goal, I lead an intensive effort of individual data collection in neuroimaging (fMRI, MEG), the Courtois project on neuronal modelling (CNeuroMod).

I am a Senior FRQS chercheur boursier, a member of the Quebec alliance for Unifying Neuroscience and AI (UNIQUE), the leader of Digital Health at CRIUGM, co-lead of the data management platform at CRIUGM and the associate director for neuroinformatics of the Functional Neuroimaging Unit (UNF) at CRIUGM.

Current Students

Hugo Delhaye
Master's Research - Université de Montréal
Co-supervisor :
Audrey Durand
Maximilien Le Clei
PhD - Université de Montréal
Pravish Sainath
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Github

Publications

Brain states recur across diverse narrative contexts during longitudinal viewing
Yibei Chen
Matin Ghavami
Marie St‐Laurent
Lune Bellec
Satrajit S. Ghosh
Abstract What does the brain do during the continuous, varied experience of watching a story unfold? One account holds that the brain traver… (see more)ses a finite repertoire of recurring states, but whether that repertoire is a stable property of the individual or is reshaped by each new experience has not been tested across diverse naturalistic content within the same person. We characterized the dynamic brain-state repertoire in six individuals who watched the television series Friends across its six seasons during fMRI (up to ∼146 episodes, ∼54 hours per person). For each individual we fit a sticky hierarchical Dirichlet process hidden Markov model across all episodes, discovering brain states (recurring whole-brain activity patterns with characteristic coupling) without pre-specifying their number. Each individual’s brain visited roughly forty-five states arrayed along a continuous recurrence gradient, from states active in nearly every episode to episode-specific ones, with no sharp division between them. The repertoire was heterogeneous in why its states recurred: a minority locked to scan-run structure, the majority remaining eligible for content. Transitions were organized by the functional-connectivity similarity between states (per-individual Spearman ρ = 0.33–0.55) and, in most individuals, respected resting-state network boundaries. Episode content was associated with which states the brain occupied moment to moment. The recurrence ordering discovered in Friends transferred to state occupancy during other social-narrative films (five of six individuals) and attenuated as stimuli departed from that class, weakening for visual-only reading and audio-only listening. Across diverse narrative experience, the dynamic repertoire is a property of the individual: content varies which states are visited and when, not which states exist.
2026-06-02
bioRxiv (preprint)
doi.org
Translating Brain Encoding Models to Clinical Cohorts: Challenges of Domain Adaptation
Lune Bellec
Marie St‐Laurent
Julie Boyle
Basile Pinsard
Elizabeth DuPré
We’ve optimized fMRI biomarkers to generalize across participants, not cognitive states. Brain encoding models might finally let us model … (see more)both and change what “good data” means. These are the slides of a presentation by Dr Lune Bellec at the AI4health workshop, ÉTS, Montréal, Feb 2026.
2026-04-13
Zenodo (unknown)
doi.org
Scenes partitioning and annotations of Super Mario Bros. levels
Hugo Delhaye
Yann Harel
Basile Pinsard
Lune Bellec
2026-03-24
Zenodo (unknown)
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.
2026-03-19
OpenReview (published)
doi.org
openreview.net
CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience
Marie St-Laurent
Basile Pinsard
Oliver Contier
Elizabeth DuPré
Katja Seeliger
Valentina Borghesani
Julie A. Boyle
Lune Bellec
Martin N. Hebart
Data-hungry neuro-AI modelling requires ever larger neuroimaging datasets. CNeuroMod-THINGS meets this need by capturing neural representati… (see more)ons for a wide set of semantic concepts using well-characterized images in a new densely-sampled, large-scale fMRI dataset. Importantly, CNeuroMod-THINGS exploits synergies between two existing projects: the THINGS initiative (THINGS) and the Courtois Project on Neural Modelling (CNeuroMod). THINGS has developed a common set of thoroughly annotated images broadly sampling natural and man-made objects which is used to acquire a growing collection of multimodal neural responses. Meanwhile, CNeuroMod is acquiring hundreds of hours of fMRI data from a core set of participants during controlled and naturalistic tasks, including visual tasks like movie watching and videogame playing. For CNeuroMod-THINGS, four CNeuroMod participants each completed 33-36 sessions of a continuous recognition paradigm using 4320 images from the THINGS stimulus set spanning 720 categories. We report behavioural and neuroimaging metrics that showcase the quality of the data. By bridging together large existing resources, CNeuroMod-THINGS expands our capacity to model human vision in controlled and naturalistic settings.
2026-01-28
Scientific Data (published)
doi.org
arxiv.org
Sex Classification Based on the Functional Connectivity Patterns of the Language Network: A Resting State <scp>fMRI</scp> Study
Xanthy Lajoie
C. DeRoy
C. Bedetti
Bérengère Houzé
N. Clarke
Sébastien Hétu
M.‐È. Picard
Lune Bellec
S. M. Brambati
ABSTRACT Research on sex differences in the brain is essential for a better understanding of how the brain develops and ages, and how neurol… (see more)ogical and psychiatric conditions can impact men and women differently. While numerous studies have focused on sex differences in brain structures, few have examined the characteristics of functional networks, particularly the language network. Although previous research suggests similar overall language performance across sexes, differences may still exist in the brain networks that underlie language processing. In addition, prior studies on sex differences in language have predominantly relied on task‐based fMRI, which may fail to capture subtle differences in underlying functional activity. In this study, we applied a machine learning approach to classify participants' sex based on resting‐state functional connectivity patterns of the language network in healthy young adults (270 men and 288 women; age: 22–36 years), and to identify the most predictive functional connectivity features. The classifier achieved 91.3% accuracy, with key discriminant features anchored to the left opercular part of the inferior frontal gyrus, the left planum temporale, and the left anterior middle temporal gyrus. These regions show distinctive connectivity patterns with heteromodal association cortices, including the occipital poles, angular gyrus, posterior cingulate gyrus, and intraparietal sulcus. Although there was an overlap between men and women, men displayed stronger functional connectivity values in these regions. These findings highlight sex‐related differences in functional connectivity patterns of the language network at rest, underscoring the importance of considering sex as a variable in future research on language and brain function.
2026-01-09
Human Brain Mapping (published)
doi.org
AI Models of Human Brain and Behaviour During Naturalistic Videogame Play
Lune Bellec
Yann Harel
François Paugam
André Cyr
Basile Pinsard
Julie Boyle
Slides of the presentation given at the Montreal Artificial Intelligence and Neuroscience (MAIN) conference, December 2025, by Lune Bellec. … (see more)A video recording of the presentation is available on youtube.
2025-12-11
Zenodo (unknown)
doi.org
Training neural networks from scratch in a videogame leads to brittle brain encoding
François Paugam
Basile Pinsard
Marie St-Laurent
Guillaume Lajoie
Lune Bellec
Recent brain-encoding studies using videogame tasks suggest that the training objective of an artificial neural network plays a central role… (see more) in how well the network’s representations align with brain activity. This study investigates the alignment of artificial neural network activations with brain activity elicited by a video game task using models trained from scratch in controlled settings. We specifically compared three model training objectives: reinforcement learning, imitation learning, and a vision task, while accounting for other potential factors which may impact performance such as training data and model architecture. We tested models on brain encoding, i.e. their ability to predict functional magnetic resonance imaging (fMRI) signals acquired while human subjects played different levels of the video game Super Mario Bros. When tested on new playthroughs from the game levels seen at training, the reinforcement learning objective had a small but significant advantage in brain encoding, followed by the imitation learning and vision models. We hypothesized that brain-aligned representations would emerge only in task-competent models, and that the specific brain regions well encoded by a model would depend on the nature of the task it was trained on. While brain encoding did improve during model training, even an untrained model with matching architecture approached the performance of the best models. Contrary to our hypotheses, no model layers or specific training objectives aligned preferentially with specific brain areas. Large performance gaps also persisted in fully trained models across game levels, both those seen during training and entirely novel ones. Overall, even though reinforcement learning presented a small advantage to train brain encoding models for videogame data, all tested brain encoding models exhibited brittle performance with limited generalization both within- and out-of-distribution. Overall, our results suggest that training small artificial models from scratch is not sufficiently reliable, and that incorporating pretrained models such as foundation vision–action models may ultimately be necessary to support robust inferences about brain representations.
2025-12-01
bioRxiv (preprint)
doi.org
Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso-Ortiz
Julie Boyle
Lune Bellec
Julien Cohen-Adad
Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used qualitativ… (see more)e MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsatexhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
2024-12-09
Imaging Neuroscience (published)
doi.org