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
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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

Saskia Helbling
Postdoctorate - Université de Montréal
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Nadine Mohamed
Master's Research - Université de Montréal
Rémy Ramadour
Independent visiting researcher - CHU Sainte Justine / Université de Montréal
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Mahta Ramezanian
Master's Research - Université de Montréal
Principal supervisor :
Irina Rish
Pascal Tikeng Notsawo
PhD - Université de Montréal
Principal supervisor :
Guillaume Rabusseau
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Publications

Early Transcriptional Changes in Rabies Virus-Infected Neurons and Their Impact on Neuronal Functions
Seonhee Kim
Florence Larrous
Hugo Varet
Rachel Legendre
Lena Feige
Guillaume Dumas
Rebecca Matsas
Georgia Kouroupi
Regis Grailhe
Hervé Bourhy
Rabies is a zoonotic disease caused by rabies virus (RABV). As rabies advances, patients develop a variety of severe neurological symptoms t… (see more)hat inevitably lead to coma and death. Unlike other neurotropic viruses that can induce symptoms of a similar range, RABV-infected post-mortem brains do not show significant signs of inflammation nor the structural damages on neurons. This suggests that the observed neurological symptoms possibly originate from dysfunctions of neurons. However, many aspects of neuronal dysfunctions in the context of RABV infection are only partially understood, and therefore require further investigation. In this study, we used differentiated neurons to characterize the RABV-induced transcriptomic changes at the early time-points of infection. We found that the genes modulated in response to the infection are particularly involved in cell cycle, gene expression, immune response, and neuronal function-associated processes. Comparing a wild-type RABV to a mutant virus harboring altered matrix proteins, we found that the RABV matrix protein plays an important role in the early down-regulation of host genes, of which a significant number is involved in neuronal functions. The kinetics of differentially expressed genes (DEGs) are also different between the wild type and mutant virus datasets. The number of modulated genes remained constant upon wild-type RABV infection up to 24 h post-infection, but dramatically increased in the mutant condition. This result suggests that the intact viral matrix protein is important to control the size of host gene modulation. We then examined the signaling pathways previously studied in relation to the innate immune responses against RABV, and found that these pathways contribute to the changes in neuronal function-associated processes. We further examined a set of regulated genes that could impact neuronal functions collectively, and demonstrated in calcium imaging that indeed the spontaneous activity of neurons is influenced by RABV infection. Overall, our findings suggest that neuronal function-associated genes are modulated by RABV early on, potentially through the viral matrix protein-interacting signaling molecules and their downstream pathways.
2021-12-12
Frontiers in Microbiology (published)
doi.org
Maternal chemosignals enhance infant-adult brain-to-brain synchrony
Yaara Endevelt-Shapira
Amir Djalovski
Guillaume Dumas
Ruth Feldman
2021-12-09
Science Advances (published)
doi.org
Hypo- and hyper- sensory processing heterogeneity in Autism Spectrum Disorder
Aline Lefebvre
Julian Tillmann
Freddy Cliquet
Frederique Amsellem
Anna Maruani
Claire Leblond
Anita Beggiato
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Daniel Umbricht
Christopher Chattam
Lorraine Murtagh
Manuel Bouvard
Marion Leboyer
Tony Charman
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
Background. Sensory processing atypicalities are part of the core symptoms of autism spectrum disorder (ASD) and could result from an excita… (see more)tion/inhibition imbalance. Yet, the convergence level of phenotypic sensory processing atypicalities with genetic alterations in GABA-ergic and glutamatergic pathways remains poorly understood. This study aimed to characterize the distribution of hypo/hyper-sensory profile among individuals with ASD and investigate the role of deleterious mutations in GABAergic and glutamatergic pathways related genes in sensory processing atypicalities. Method. From the Short Sensory Profile (SSP) questionnaire, we defined and explored a score – the differential Short Sensory Profile (dSSP) - as a normalized and centralized hypo/hypersensitivity ratio for 1136 participants (533 with ASD, 210 first-degree relatives, and 267 controls) from two independent study samples (PARIS and LEAP). We also performed an unsupervised item-based clustering analysis on SSP items scores to validate this new categorization in terms of hypo and hyper sensitivity. We then explored the link between the dSSP score and the burden of deleterious mutations in a subset of individuals for which whole-genome sequencing data were available. Results. We observed a mean dSSP score difference between ASD and controls, driven mostly by a high dSSP score variability among groups (PARIS: p0.0001, η2 = 0.0001, LEAP: p0.0001, Cohen’s d=3.67). First-degree relatives were with an intermediate distribution variability prof
2021-11-29
(published)
doi.org
The meaning of significant mean group differences for biomarker discovery
Eva Loth
Jumana Ahmad
Chris Chatham
Beatriz López
Ben Carter
Daisy Crawley
Bethany Oakley
Hannah Hayward
Jennifer Cooke
Antonia San José Cáceres
Danilo Bzdok
Emily Jones
Tony Charman
Christian Beckmann
Thomas Bourgeron
Roberto Toro
Jan Buitelaar
Declan Murphy
Guillaume Dumas
Over the past decade, biomarker discovery has become a key goal in psychiatry to aid in the more reliable diagnosis and prognosis of heterog… (see more)eneous psychiatric conditions and the development of tailored therapies. Nevertheless, the prevailing statistical approach is still the mean group comparison between “cases” and “controls,” which tends to ignore within-group variability. In this educational article, we used empirical data simulations to investigate how effect size, sample size, and the shape of distributions impact the interpretation of mean group differences for biomarker discovery. We then applied these statistical criteria to evaluate biomarker discovery in one area of psychiatric research—autism research. Across the most influential areas of autism research, effect size estimates ranged from small (d = 0.21, anatomical structure) to medium (d = 0.36 electrophysiology, d = 0.5, eye-tracking) to large (d = 1.1 theory of mind). We show that in normal distributions, this translates to approximately 45% to 63% of cases performing within 1 standard deviation (SD) of the typical range, i.e., they do not have a deficit/atypicality in a statistical sense. For a measure to have diagnostic utility as defined by 80% sensitivity and 80% specificity, Cohen’s d of 1.66 is required, with still 40% of cases falling within 1 SD. However, in both normal and nonnormal distributions, 1 (skewness) or 2 (platykurtic, bimodal) biologically plausible subgroups may exist despite small or even nonsignificant mean group differences. This conclusion drastically contrasts the way mean group differences are frequently reported. Over 95% of studies omitted the “on average” when summarising their findings in their abstracts (“autistic people have deficits in X”), which can be misleading as it implies that the group-level difference applies to all individuals in that group. We outline practical approaches and steps for researchers to explore mean group comparisons for the discovery of stratification biomarkers.
2021-10-31
PLoS Computational Biology (published)
doi.org
Inter-Brain Synchronization: From Neurobehavioral Correlation to Causal Explanation
Guillaume Dumas
2021-09-30
International Journal of Psychophysiology (published)
doi.org
THE EFFECT SIZE OF GENES ON COGNITIVE ABILITIES IS LINKED TO THEIR EXPRESSION ALONG THE MAJOR HIERARCHICAL GRADIENT IN THE HUMAN BRAIN
Sébastien Jacquemont
Guillaume Huguet
Elise Douard
Zohra Saci
Guillaume Dumas
Laura Almasy
David C. Glahn
2021-09-30
European Neuropsychopharmacology (published)
doi.org
Hybrid Harmony: A Multi-Person Neurofeedback Application for Interpersonal Synchrony
Phoebe Chen
Sophie Hendrikse
Kaia Sargent
Michele Romani
Matthias Oostrik
Tom F. Wilderjans
Sander Koole
Guillaume Dumas
David Medine
Suzanne Dikker
Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple … (see more)individuals during social interaction. For example, so-called “hyperscanning” research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.
2021-08-11
Frontiers in Neuroergonomics (published)
doi.org
A systematic analysis of ICSD-3 diagnostic criteria and proposal for further structured iteration.
Christophe Gauld
Régis Lopez
Pierre A. GEOFFROY
Charles Morin
Kelly Guichard
Elodie Giroux
Yves Dauvilliers
Guillaume Dumas
Pierre Philip
Jean‐Arthur Micoulaud‐Franchi
2021-07-31
Sleep Medicine Reviews (published)
doi.org
Temporal Profiles of Social Attention Are Different Across Development in Autistic and Neurotypical People.
Teresa Del Bianco
Luke Mason
Tony Charman
Julianne Tillman
Eva Loth
Hannah Hayward
F. Shic
Jan K. Buitelaar
Mark Johnson
Emily J. H. Jones
Jumana Ahmad
Sara Ambrosino
Tobias Banaschewski
Simon Baron-Cohen
Sarah Baumeister
Christian Beckmann
Sven Bölte
Thomas Bourgeron
Carsten Bours
M. Brammer … (see 46 more)
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Ineke Cornelissen
Daisy Crawley
Flavio Dell’Acqua
Guillaume Dumas
Sarah Durston
Christine Ecker
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Joerg F. Hipp
Rosemary Holt
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Carolin Moessnang
Nico Mueller
Declan Murphy
Beth Oakley
Larry O’Dwyer
Marianne Oldehinkel
Bob Oranje
Gahan Pandina
Antonio Persico
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
2021-07-31
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (published)
doi.org
Why do sleep disorders belong to mental disorder classifications? A network analysis of the "Sleep-Wake Disorders" section of the DSM-5.
Christophe Gauld
Régis Lopez
Charles Morin
Julien Maquet
Aileen McGonigal
Pierre A. GEOFFROY
Eric Fakra
Pierre Philip
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2021-07-31
Journal of Psychiatric Research (published)
doi.org
Symptom network analysis of the sleep disorders diagnostic criteria based on the clinical text of the ICSD‐3
Christophe Gauld
Régis Lopez
Charles Morin
Pierre A. GEOFFROY
Julien Maquet
Pierre Desvergnes
Aileen McGonigal
Yves Dauvilliers
Pierre Philip
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2021-07-15
Journal of Sleep Research (published)
doi.org
Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks.
Germán Abrevaya
Guillaume Dumas
Aleksandr Y. Aravkin
Peng Zheng
Jean-christophe Gagnon-audet
James R. Kozloski
Pablo Polosecki
Guillaume Lajoie
David D. Cox
Silvina Ponce Dawson
Guillermo A. Cecchi
Irina Rish
Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by … (see more)linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.
2021-06-06
Neural Computation (unknown)
doi.org