Portrait of Guillaume Dumas

Guillaume Dumas

Associate Academic Member
Associate Professor, Université de Montréal, Department of Psychiatry and Addiction
Adjunct Professor, McGill University, Department of Psychiatry
Research Topics
Computational Biology
Computational Neuroscience
Deep Learning
Dynamical Systems
Machine Learning Theory
Medical Machine Learning
Reinforcement Learning

Biography

Guillaume Dumas is an associate professor of computational psychiatry in the Faculty of Medicine, Université de Montréal, and principal investigator in the Precision Psychiatry and Social Physiology laboratory at the Centre hospitalier universitaire (CHU) Sainte-Justine Research Centre. He holds the IVADO professorship for AI in Mental Health, and the Fonds de recherche du Québec - Santé (FRQS) J1 in AI and Digital Health. In 2023, Dumas was recognized as a CIFAR Azrieli Global Scholar – Brain, Mind, and Consciousness program, and nominated as a Future Leader in Canadian Brain Research by the Brain Canada Foundation.

Dumas was previously a permanent researcher in neuroscience and computational biology at the Institut Pasteur (Paris). Before that, he was a postdoctoral fellow at the Center for Complex Systems and Brain Sciences (Florida Atlanta University). He holds an engineering degree in advanced engineering and computer science (École Centrale Paris), two MSc degrees (theoretical physics, Paris-Saclay University; cognitive science, ENS/EHESS/Paris 5), and a PhD in cognitive neuroscience (Sorbonne University).

The goal of his research is to cross-fertilize AI/ML, cognitive neuroscience and digital medicine through an interdisciplinary program with two main axes:

- AI/ML for Mental Health, which aims to create new algorithms to investigate the development of human cognitive architecture and deliver personalized medicine in neuropsychiatry using data from genomes to smartphones.

- Social Neuroscience for AI/ML, which translates basic brain research and dynamical systems formalism into neurocomputational and machine learning hybrid models (NeuroML) and machines with social learning abilities (Social NeuroAI & HMI).

Current Students

Postdoctorate - Université de Montréal
Master's Research - Université de Montréal
Independent visiting researcher - CHU Sainte Justine / Université de Montréal
Master's Research - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :

Publications

Online HD-tRNS over the Right Temporoparietal Junction Enhances Mentalizing during Social Interactions
Vincent Chamberland
Quentin Moreau
Lisane Moses
Gabriela Milanova
Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach
Ghazaleh Ranjabaran
Quentin Moreau
Adrien Dubois
Abstract This study introduces a self-supervised learning (SSL) approach to hyperscanning electroencephalog… (see more)raphy (EEG) data, targeting the identification of autism spectrum condition (ASC) during social interactions. Hyperscanning enables simultaneous recording of neural activity across interacting individuals, offering a novel path for studying brain-to-brain synchrony in ASC. Leveraging a large-scale, single-brain EEG dataset for SSL pretraining, we developed a multi-brain classification model fine-tuned with hyperscanning data from dyadic interactions involving ASC and neurotypical participants. The SSL model demonstrated superior performance (78.13% accuracy) compared to supervised baselines and logistic regression using spectral EEG biomarkers. These results underscore the efficacy of SSL in addressing the challenges of limited labeled data, enhancing EEG-based diagnostic tools for ASC, and advancing research in social neuroscience.
Now is the time: operationalizing generative neurophenomenology through interpersonal methods
Anne Monnier
Lena Adel

Lived experience is shaped by intersubjective, social, cultural, and historical dimensions. For the past 30 years, neurophenomenology has… (see more) adopted an embodied perspective of the mind by integrating first-person experiential and third-person neurobehavioral perspectives. Indeed, the neurophenomenology pragmatic approach has embraced an embodied perspective of the mind by integrating experiential first-person and neurobehavioural third-person perspectives. Neurophenomenology reveals mutual constraints between both, as they co-constitute a person’s lived experience. This article emphasizes the intersubjective and social facets of lived experience as well as the readiness of the scientific community to use a "generative neurophenomenology" approach, envisioned in the 1990s by Francisco Varela. For this endeavour, we clarify three meanings of “generative” as it applies distinctly to generative phenomenology, generative passages, and generative models. Then, we propose to combine existing methods to update neurophenomenology program: First, by transitioning from individual to multiple people phenomenology methods that include intersubjectivity experience; second, by expanding traditional neuroscience to include measures of multimodal interpersonal synchrony; and third, by leveraging multiple computational tools to integrate different viewpoints, thereby enriching our understanding of lived experience; We also underscore the potential of diverse mathematical formalisms to capture aspects of human experience, all while underscoring that using computational approaches to model neurophenomenology does not entail endorsing computationalism as a grounding hypothesis of human experience. Finally, we illustrate the clinical relevance of this paradigm through two case studies in psychiatry—(1) with interactive dyads in autism and (2) with multiple members in family therapy sessions—demonstrating its translational potential.

Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex
Kuldeep Kumar
Sayeh Kazem
Worrawat Engchuan
Thomas Renne
Martineau Jean-Louis
Omar Shanta
Zohra Saci
Bhooma Thiruvahindrapuram
Jeffrey MacDonald
Josephine Mollon
Laura M Schultz
Emma E M Knowles
David Porteous
Gail Davies
Paul Redmond
Sarah Harris
Simon Cox
Gunter Schumann … (see 9 more)
Zdenka Pausova
Celia Greenwood
Tomáš Paus
Stephen Scherer
Laura Almasy
Jonathan Sebat
David Glahn
Sébastien Jacquemont
Cognitive deficits are common across many neurodevelopmental and psychiatric conditions, including those studied in the current set of PGC-C… (see more)NV papers. How changes in regional gene expression across the cerebral cortex influence cognitive ability remains unknown. Population variation in gene dosage—which significantly impacts gene expression—represents a unique paradigm to address this question. We developed a cerebral-cortex gene-set burden analysis (CC-GSBA) to associate a trait with genomic deletions and duplications that disrupt genes with similar expression profiles across 180 cortical regions. We performed CC-GSBA across 180 cortical regions to test associations with cognitive ability in 260,000 individuals from general population cohorts. Most cortical gene sets were associated with a decrease in cognitive ability when deleted or duplicated, and this novel approach revealed opposing cortical patterns for the effect sizes of deletions and duplications. These cortical patterns of effect sizes followed the cortical gradient previously characterized at the molecular, cellular, and functional levels. We show that genes with preferential expression in sensorimotor regions demonstrated the largest effect on cognition when deleted. At the opposing end of the cortical gradient, genes with preferential expression in multimodal association regions affected cognition the most when duplicated. These two gene dosage cortical patterns could not be explained by particular cell types, developmental epochs, or genetic constraints, highlighting the fact that the macroscopic network organization of the cerebral cortex is key to understanding the effects of gene dosage on cognitive traits.
Determinants of pleiotropy and monotonic gene dosage responses across human traits
Sayeh Kazem
Kuldeep Kumar
Josephine Mollon
Thomas Renne
Laura M. Schultz
Emma E.M. Knowles
Worrawat Engchuan
Omar Shanta
Bhooma Thiruvahindrapuram
Jeffrey R. MacDonald
Celia M. T. Greenwood
Stephen W. Scherer
Laura Almasy
Jonathan Sebat
David C. Glahn
Sébastien Jacquemont
While pleiotropic effects of gene dosage are of particular relevance for comorbidities observed in the developmental pediatric and psychiatr… (see more)ic clinic, the biological processes underlying such pleiotropy remain unknown. We developed a new functional burden analysis (FunBurd) to investigate all CNVs, genome-wide, beyond well-studied recurrent CNVs. In ~500,000 UK-Biobank participants, we tested the association between 43 traits and CNVs disrupting 172 tissue or cell-type gene-sets. CNVs affected all traits. Pleiotropy was correlated with genetic constraint and was higher in the brain compared to non-brain functions, even after normalizing for genetic constraint. The levels of pleiotropy, measured by burden correlation, were similar in deletions and loss-of-function SNVs and higher compared to common variants and duplications. Gene sets under high genetic constraint showed less monotonic gene dosage responses across traits. Even in the absence of a monotonic response, we observed a negative correlation between deletion and duplication effect sizes across most traits. Overall, functional gene sets are preferentially associated with a given trait when either deleted or duplicated, but rarely both.
Towards an informational account of interpersonal coordination
Edoardo Chidichimo
Andrea Luppi
Pedro A. Mediano
Victoria Leong
Andres Canales-Johnson
Richard A.I. Bethlehem

Human sociality is grounded in the dynamic coordination of individuals as they interact with one another. Indeed, interpersonal coordinat… (see more)ion on various levels—neural, behavioural, physiological, affective, linguistic—are hallmarks of successful social communication and cooperation. However, describing these complex, interdependent dynamics has been limited by current methodological approaches, owing to a restrictive repertoire of tools and the absence of a unified, standardised methodological framework. Here, we identify information theory, the mathematical theory of communication, as a particularly well-suited conceptual framework to address this shortfall, given its appropriate sensitivity to complex dynamics, including potential nonlinearity and higher-order interactions, and its data-driven, model-agnostic foundations. With deep roots in computational, cognitive, and systems neuroscience, the formal introduction of information-theoretic quantities and methods into the study of interpersonal coordination is perhaps overdue. This Perspective advances the case for a unified information-theoretic framework for the field while paving the path for a new generation of empirically testable, theoretically grounded research questions.

Longitudinal intergenerational hyperscanning indexes changes in social connection
Ryssa Moffat
Emily S. Cross
Loneliness is globally acknowledged as a severe and burgeoning health risk, fuelling interest in helping people of all ages form meaningful … (see more)social connections. One promising approach consists of intergenerational social programs. While behavioural and qualitative evidence derived from such programs promise health and wellbeing benefits, the physiological consequences of repeated intergenerational encounters remain unknown. Insight into physiological changes will shed light on the mechanisms of social connection and can inform program design choices. We charted changes in interpersonal neural synchrony (INS) in 31 intergenerational (older/younger adult) and 30 same generation (younger adult) dyads across a six-session creative drawing program. At each session, dyads completed self-report measures, drew together and alone, and had their cortical activation recorded with fNIRS. In both groups, INS was greater while dyads drew together than alone. Across sessions, intergenerational dyads’ INS decreased and same generation dyads’ INS increased. INS in RIFG∼RTPJ and RIFG∼RIFG were predictive of loneliness levels and feelings of social closeness, respectively. The research reinforces the multi-faceted nature of INS dynamics as social connections are forged.
Grokking Beyond the Euclidean Norm of Model Parameters
Pascal Jr Tikeng Notsawo
Pascal Jr Tikeng Notsawo
Grokking refers to a delayed generalization following overfitting when optimizing artificial neural networks with gradient-based methods. In… (see more) this work, we demonstrate that grokking can be induced by regularization, either explicit or implicit. More precisely, we show that when there exists a model with a property
Asymmetric developmental bifurcations in polarized environments: a new class of human variants, which may include autism.
Laurent Mottron
Alix Lavigne-Champagne
Boris Bernhardt
Sébastien Jacquemont
David Gagnon
Acute respiratory distress syndrome in patients with cancer: the YELENNA prospective multinational observational cohort study.
Peter Schellongowski
Michael Darmon
Philipp Eller
Laveena Munshi
Tobias Liebregts
Victoria Metaxa
Luca Montini
Tobias Lahmer
Andry Van de Louw
Martin Balik
Peter Pickkers
Pleun Hemelaar
Hemang Yadav
Andreas Barratt-Due
Thomas Karvunidis
Jordi Riera
Gennaro Martucci
Ignacio Martin-Loeches
Pedro Castro
Nina Buchtele … (see 24 more)
Virginie Lemiale
Stefan Hatzl
Thomas Staudinger
Elie Azoulay
Gottfried Gürkan Christian Elisabeth Alexis Gennaro Giovanna Heinz Sengölge Zauner Lobmeyr Maillard De Pascale
Gottfried Heinz
G. Sengölge
Christian Zauner
Elisabeth Lobmeyr
Alexis Maillard
G. De Pascale
G. Panarello
Philippe R. Bauer
M. Flaksa
Brozek
Fabio S. Taccone
I. Crippa
Andreas Barrat-Due
Sandra García-Roche
Cándido Díaz-Lagares
Andrés Pacheco
A. Téllez
I. Loeches
Aperiodic and Periodic EEG Component Lifespan Trajectories: Monotonic Decrease versus Growth-then-Decline
Min Li
Ying Wang
Yaqi Chen
Adrien E. E. Dubois
Gangyong Jia
Ying Wang
Maria L. Bringas-Vega
Pedro A. Valdes-Sosab
1.1 Unraveling the lifespan trajectories of human brain development is critical for understanding brain health and … (see more)disease. Recent research demonstrates that electroencephalography signals are composed of periodic and aperiodic components reflecting distinct physiological substrates. This dissociation raises the possibility that they follow different developmental tendencies. Here, we delineate the lifespan trajectories of aperiodic and periodic neural oscillations using a large international cohort (N=1,563, ages 5–95, resting state, eyes closed). We reveal two fundamental developmental patterns: a Monotonic decrease in aperiodic activity and a Growth-and-Decline pattern for periodic activity. Both components have inflections around age 20 and transition to a stable senescent phase around age 40. Spatially, anterior regions mainly exhibit aperiodic activity, while periodic activity concentrate on posterior regions and these patterns remain stable throughout life. Crucially, multimodal analysis shows these trajectories map onto distinct biological substrates. The periodic component’s Growth and Decline trajectory aligns with GABAergic function and myelination. In contrast, the monotonically decreasing trajectory of aperiodic activity mirrors fundamental biomarkers of biological aging, such as DNA methylation and telomere length. Transforming age to a logarithmic scale simplifies these nonlinear trajectories into a linear decreasing and a piecewise concave linear model for aperiodic and periodic components. This form provides a robust and parsimonious framework for quantifying maturation and identifying neurological deviations. We delineate distinct lifespan trajectories of aperiodic and periodic neural activity in a large-scale international cohort (N=1,563, ages 5–95). Aperiodic activity undergoes a Monotonic Decrease with age. In contrast, periodic activity follows a Growth-then-Decline trajectory, peaking in early adulthood. Both trajectories feature a critical transition around age 20 and stabilize into a protracted senescent phase from approximately 40 onward. These neural trajectories map onto distinct biological substrates: periodic activity tracks integrative functions (myelination, GABAergic, and aperiodic decline mirrors fundamental aging processes (DNA methylation). A stable pattern observed throughout the lifespan is the spatial segregation of neural activity, where aperiodic signals are dominant in anterior regions and periodic signals are concentrated in posterior ones. Logarithmically transforming age linearized the developmental trajectories, yielding a monotonic decline for the aperiodic component and a concave piecewise for the periodic one. This process establishes robust linear norms for the personalized assessment of brain dysfunction.
Pathfinding: a neurodynamical account of intuition
Steven Kotler
Michael Mannino
Karl Friston
Gyorgy Buzsáki
J. A. Scott Kelso