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

Effective Latent Differential Equation Models via Attention and Multiple Shooting
Mahta Ramezanian-Panahi
Pablo Polosecki
Silvina Ponce Dawson
Guillermo Cecchi
Prospective evaluation of bleeding risk among thrombocytopenic patients admitted in intensive care unit.
Geoffroy HARIRI
Vincent Belossi
Louis Pérol
Louai Missri
Paul GABARRE
Vincent BONNY
Tomas URBINA
Jean-Luc Baudel
Bertrand GUIDET
Jérémie JOFFRE
Eric Maury
Hafid AIT-OUFELLA
Protocol for fever control using external cooling in mechanically ventilated patients with septic shock: SEPSISCOOL II randomised controlled trial
Armelle Guénégou-Arnoux
Juliette Murris
Stéphane Bechet
Camille Jung
Johann Auchabie
Julien Dupeyrat
Nadia Anguel
Pierre Asfar
Julio Badie
Dorothée Carpentier
Benjamin Chousterman
Jeremy Bourenne
Agathe Delbove
Jérôme Devaquet
Nicolas Deye
Anne-Florence Dureau
Jean-Baptiste Lascarrou
Stephane Legriel
Christophe Guitton … (see 14 more)
Caroline Jannière-Nartey
Jean-Pierre Quenot
Jean-Claude Lacherade
Julien Maizel
Armand Mekontso Dessap
Bruno Mourvillier
Philippe Petua
Gaetan Plantefeve
Jean-Christophe Richard
Alexandre Robert
Clément Saccheri
Ly Van Phach Vong
Sandrine Katsahian
Frédérique Schortgen
Arbitrary methodological decisions skew inter-brain synchronization estimates in hyperscanning-EEG studies
Marius Zimmermann
Kathrine Schultz-Nielsen
Ivana Konvalinka
Over the past decade, hyperscanning has emerged as an important methodology to study neural processes underlying human interaction using fMR… (see more)I, EEG, fNIRS, and MEG. However, many methodological decisions regarding preprocessing and analysis of hyperscanning data have not yet been standardized in the hyperscanning community, yet may affect inter-brain estimates. Here we systematically investigate the effects common methodological choices can have on estimates of phase-based inter-brain synchronization (IBS) measures, using real and simulated hyperscanning (dual) EEG data. Notably, we introduce a new method to compute circular correlation (CCorr) coefficients in IBS studies, which performs more reliably in comparison to the standard approach, showing that the conventional CCorr implementation leads to large fluctuations in IBS estimates due to fluctuations in circular mean directions. Furthermore, we demonstrate how short epoch durations (of 1 second or less) can lead to inflated IBS estimates in scenarios with no strong underlying interaction. Finally, we show how signal-to-noise ratios and temporal factors may confound IBS estimates, particularly when comparing e.g., resting states with conditions involving motor actions. For each of these investigated effects, we provide recommendations for future research employing hyperscanning-EEG techniques, aimed at increasing validity and replicability of inter-brain synchronization studies.
Corticosteroids induce an early but limited decrease in IL-6 dependent pro-inflammatory responses in critically ill COVID-19 patients
Tomas URBINA
Paul GABARRE
Vincent BONNY
Jean-Rémi Lavillegrand
Marc GARNIER
Jérémie JOFFRE
Nathalie MARIO
Geoffroy HARIRI
Matthieu TURPIN
Emmanuel PARDO
Muriel FARTOUKH
Bertrand GUIDET
Eric Maury
Yannick CHANTRAN
Pierre-Yves BOELLE
Guillaume VOIRIOT
Hafid AIT-OUFELLA
Resilience and Mental-Health Symptoms in ICU Healthcare Professionals Facing Repeated COVID-19 Waves
Elie Azoulay
Frédéric Pochard
Laurent Argaud
Alain Cariou
Raphael Clere-Jehl
Olivier Guisset
Vincent Labbé
Fabienne Tamion
Fabrice Bruneel
Mercé Jourdain
Danielle Reuter
Kada Klouche
Achille Kouatchet
Virginie Souppart
Alexandre Lautrette
Julien Bohé
Antoine Vieillard Baron
Jean Dellamonica
Laurent Papazian
Jean Reignier … (see 3 more)
François Barbier
Nancy Kentish-Barnes
Sources of Richness and Ineffability for Phenomenally Conscious States
George Deane
Axel Constant
Jonathan Simon
Conscious states (states that there is something it is like to be in) seem both rich or full of detail, and ineffable or hard to fully descr… (see more)ibe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience: two important aspects that seem to be part of what makes qualitative character so puzzling.
Diagnosis and management of autoimmune diseases in the ICU
Yaseen M. Arabi
Raquel Bartz
Otavio Ranzani
Franziska Scheibe
Michael Darmon
Julie Helms
From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.
Adam Safron
Casper Hesp
Exploring the multidimensional nature of repetitive and restricted behaviors and interests (RRBI) in autism: neuroanatomical correlates and clinical implications
Aline Lefebvre
Nicolas Traut
Amandine Pedoux
Anna Maruani
Anita Beggiato
Monique Elmaleh
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Christopher H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marianne Alisson
Marion Leboyer
Thomas Bourgeron
Roberto Toro
Clara A. Moreau
Richard Delorme
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Felix Schoeller
Adam Haar Horowitz
Abhinandan Jain
Pattie Maes
Nicco Reggente
Leonardo Christov-Moore
Giovanni Pezzulo
Laura Barca
Micah Allen
Roy Salomon
Mark Miller
Daniele Di Lernia
Giuseppe Riva
Manos Tsakiris
Moussa A. Chalah
Arno Klein
Ben Zhang
Teresa Garcia
Ursula Pollack
Marion Trousselard … (see 4 more)
Charles Verdonk
Vladimir Adrien
Karl Friston

The perception of body signals play a crucial role in cognition and emotion, which may lead to catastrophic outcomes when it becomes dysf… (see more)unctional. To characterize these mechanisms and intervene on interoception for either diagnostic or treatment purposes, a mounting body of research is concerned with interventions on interoceptive channels such as respiration, cardioception, or thermoception. However, we are still lacking a mechanistic understanding of the underlying psychophysiology. For example, interoceptive signals are often both the cause and consequences of some distress in various mental disorders, and it is still unclear how interoceptive signals bind with exteroceptive cues. In this article, we present existing technologies for manipulating interoception and review their clinical potential in light of the predictive processing framework describing interoception as a process of minimization of prediction errors. We distinguish between three kinds of stimuli: artificial sensations that concern the direct manipulation of interoceptive signals, interoceptive illusions that manipulate contextual cues to induce a predictable drift in body perception, and emotional augmentation technologies that blend artificial sensations with contextual cues of personal significance to generate specific moods or emotions. We discuss how each technology can assess and intervene on the precision-weighting of prediction errors along the cognitive and emotional processing hierarchy and conclude by discussing the clinical relevance of interoceptive technologies in terms of diagnostic stress tests for evaluating interoceptive abilities across clinical conditions and as intervention protocols for conditions such as generalized anxiety disorders, post-traumatic stress disorders, and autism spectrum disorders.

Typology of ICU-Healthcare Providers Who Delayed or Declined COVID-19 Vaccination
Elie Azoulay
Frédéric Pochard
Nancy Kentish-Barnes
OBJECTIVES: To assess COVID-19 vaccination rates in ICU-healthcare providers (HCPs) in France and to identify the typology of those who dela… (see more)yed or declined vaccination. DESIGN: Cross-sectional study. SETTING: Twenty-one ICUs in France. SUBJECTS: Members of the nursing and medical staff and other allied professionals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Six hundred ninety-six of the 950 respondents (73.3%) had undergone a full vaccination schedule. Other HCPs either declined vaccination (n = 112) or delayed vaccination until it became mandatory (n = 142). Factors independently associated with full vaccination were age older than 50 years (odds ratio, 0.25 [95% CI, 0.12–0.51]), more than 5 years of ICU experience (0.66 [0.47–0.93]), increasing working time during the surge (0.94 [0.88–1.00]), and spending time with the family (0.92 [0.85–0.99]). Conversely, being a nurse (1.94 [1.25–2.99]) or a nurse assistant (2.77 [1.62–4.73]), and feeling not supported by hospital and ICU directors (1.49 [1.01–2.20]) was independently associated with not being vaccinated. CONCLUSIONS: These results are important to take into account to better implement vaccination strategies in HCPs for existing or future pandemics.