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

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
Tomas 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.
Longitudinal intergenerational hyperscanning reveals indices of relationship formation and loneliness
Ryssa Moffat
Emily S. Cross
Online HD-tRNS over the right temporoparietal junction modulates social inference but not motor coordination
Quentin Moreau
Vincent Chamberland
Lisane Moses
Gabriela Milanova
Online HD-tRNS Over the Right Temporoparietal Junction Modulates Social Inference But Not Motor Coordination
Quentin Moreau
Vincent Chamberland
Lisane Moses
Gabriela Milanova
Grokking Beyond the Euclidean Norm of Model Parameters
Tikeng Notsawo Pascal Junior
Pascal Notsawo
Grokking refers to a delayed generalization following overfitting when optimizing artificial neural networks with gradient-based methods. I… (see more)n 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
Grokking Beyond the Euclidean Norm of Model Parameters
Tikeng Notsawo Pascal Junior
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 C. Bernhardt
Sébastien Jacquemont
D. Gagnon
Asymmetric developmental bifurcations in polarized environments: a new class of human variants, which may include autism.
Laurent Mottron
Alix Lavigne-Champagne
Boris C. Bernhardt
Sébastien Jacquemont
D. Gagnon
Asymmetric developmental bifurcations in polarized environments: a new class of human variants, which may include autism.
Laurent Mottron
Alix Lavigne-Champagne
Boris C. Bernhardt
Sébastien Jacquemont
D. Gagnon