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

Independent visiting researcher - Université de Montréal
Principal supervisor :
Master's Research - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal
Principal supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Principal supervisor :
Postdoctorate - Université de Montréal

Publications

Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry
Natasha Bertelsen
Isotta Landi
Richard A.I. Bethlehem
Jakob Seidlitz
Elena Maria Busuoli
Veronica Mandelli
Eleonora Satta
Stavros Trakoshis
Bonnie Auyeung
Prantik Kundu
Eva Loth
Sarah Baumeister
Christian Beckmann
Sven Bölte
Thomas Bourgeron
Tony Charman
Sarah Durston
Christine Ecker
Rosemary Holt … (see 57 more)
Mark Johnson
Emily J. H. Jones
Luke Mason
Andreas Meyer-Lindenberg
Carolin Moessnang
Marianne Oldehinkel
Antonio Persico
Julian Tillmann
Steve C. R. Williams
Will Spooren
Declan Murphy
Jan K. Buitelaar
Jumana Sara Tobias Carsten Michael Daniel Claudia Yvette Bhismadev Chris Ineke Daisy Flavio Jessica Vincent Pilar David Lindsay Hannah Joerg Rosemary J. Xavier Liogier David J. René Andre Maarten Nico Bethany Laurence Bob Gahan Antonio M. Barbara Amber N. V. Jessica Roberto Antonia San José Emily Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Simon Baron-Cohen
Jumana Ahmad
Meng-Chuan Lai
Sara Ambrosino
Michael V. Lombardo
Tobias Banaschewski
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Bhismadev Chakrabarti
Christopher H. Chatham
Ineke Cornelissen
Daisy Crawley
Flavio Dell’Acqua
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Hannah Hayward
Joerg F. Hipp
Xavier Liogier D’ardhuy
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Nico Mueller
Beth Oakley
Laurence O’Dwyer
Bob Oranje
Gahan Pandina
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Roberto Toro
Heike Tost
Jack Waldman
Caroline Wooldridge
Marcel P. Zwiers
Beyond Correlation versus Causation: Multi-brain Neuroscience Needs Explanation
Quentin Moreau
Comment on Starke et al.: “Computing schizophrenia: ethical challenges for machine learning in psychiatry”: From machine learning to student learning: pedagogical challenges for psychiatry – Corrigendum
Christophe Gauld
Jean‐Arthur Micoulaud‐Franchi
Towards robust and replicable sex differences in the intrinsic brain function of autism
Dorothea L. Floris
José O. A. Filho
Meng-Chuan Lai
Steve Giavasis
Marianne Oldehinkel
Maarten Mennes
Tony Charman
Julian Tillmann
Christine Ecker
Flavio Dell’Acqua
Tobias Banaschewski
Carolin Moessnang
Simon Baron-Cohen
Sarah Durston
Eva Loth
Declan Murphy
Jan K. Buitelaar
Christian Beckmann
Michael P. Milham … (see 1 more)
Adriana Di Martino
From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology
Maxwell J. D. Ramstead
Anil K. Seth
Casper Hesp
Lars Sandved-Smith
Jonas Mago
Michael Lifshitz
Giuseppe Pagnoni
Ryan Smith
Andrew E. Lutz
Antoine Lutz
Karl Friston
Axel Constant
Systematic detection of brain protein-coding genes under positive selection during primate evolution and their roles in cognition
Simon Malesys
Thomas Bourgeron
The human brain differs from that of other primates, but the genetic basis of these differences remains unclear. We investigated the evoluti… (see more)onary pressures acting on almost all human protein-coding genes (N = 11,667; 1:1 orthologs in primates) based on their divergence from those of early hominins, such as Neanderthals, and non-human primates. We confirm that genes encoding brain-related proteins are among the most strongly conserved protein-coding genes in the human genome. Combining our evolutionary pressure metrics for the protein-coding genome with recent data sets, we found that this conservation applied to genes functionally associated with the synapse and expressed in brain structures such as the prefrontal cortex and the cerebellum. Conversely, several genes presenting signatures commonly associated with positive selection appear as causing brain diseases or conditions, such as micro/macrocephaly, Joubert syndrome, dyslexia, and autism. Among those, a number of DNA damage response genes associated with microcephaly in humans such as BRCA1, NHEJ1, TOP3A, and RNF168 show strong signs of positive selection and might have played a role in human brain size expansion during primate evolution. We also showed that cerebellum granule neurons express a set of genes also presenting signatures of positive selection and that may have contributed to the emergence of fine motor skills and social cognition in humans. This resource is available online and can be used to estimate evolutionary constraints acting on a set of genes and to explore their relative contributions to human traits.
Personalized Medicine for OSA Syndrome in a Nutshell: Conceptual Clarification for Integration.
Christophe Gauld
Marie Darrason
Jean‐Arthur Micoulaud‐Franchi
Mass‐spectrometry analysis of the human pineal proteome during night and day and in autism
Hany Goubran‐Botros
Mariette Matondo
Cécile Pagan
Cyril Boulègue
Thibault Chaze
Julia Chamot‐Rooke
Erik Maronde
Thomas Bourgeron
The human pineal gland regulates day‐night dynamics of multiple physiological processes, especially through the secretion of melatonin. Us… (see more)ing mass‐spectrometry‐based proteomics and dedicated analysis tools, we identify proteins in the human pineal gland and analyze systematically their variation throughout the day and compare these changes in the pineal proteome between control specimens and donors diagnosed with autism. Results reveal diverse regulated clusters of proteins with, among others, catabolic carbohydrate process and cytoplasmic membrane‐bounded vesicle‐related proteins differing between day and night and/or control versus autism pineal glands. These data show novel and unexpected processes happening in the human pineal gland during the day/night rhythm as well as specific differences between autism donor pineal glands and those from controls.
#EEGManyLabs: Investigating the replicability of influential EEG experiments
Yuri G Pavlov
N. Adamian
Stefan Appelhoff
Mahnaz Arvaneh
C. Benwell
Christian Beste
A. Bland
Daniel E. Bradford
Florian Bublatzky
Niko A. Busch
Peter E. Clayson
Damian Cruse
Artur Czeszumski
Anna Dreber
Benedikt V. Ehinger
Giorgio Ganis
Xun He
J. Hinojosa
Christoph Huber-Huber … (see 39 more)
Michael Inzlicht
B. Jack
Magnus Johannesson
Rhiannon Jones
Evgenii Kalenkovich
Laura Kaltwasser
Hamid Karimi-rouzbahani
And Andreas Keil
P. König
Layla Kouara
Louisa V. Kulke
C. Ladouceur
Nicolas Langer
Heinrich R. Liesefeld
David Luque
Annmarie MacNamara
Liad Mudrik
Muthuraman Muthuraman
Lauren Browning Neal
Gustav Nilsonne
Guiomar Niso
Sebastian Ocklenburg
Robert Oostenveld
Cyril R. Pernet
G. Pourtois
Manuela Ruzzoli
S. Sass
Alexandre Schaefer
Magdalena Senderecka
Joel S. Snyder
Christian Krog Tamnes
E Tognoli
M. V. Vugt
Edelyn Verona
Robin Vloeberghs
Dominik Welke
J. Wessel
Ilya V Zakharov
Faisal Mushtaq
Human attachments shape interbrain synchrony toward efficient performance of social goals
Amir Djalovski
Sivan Kinreich
Ruth Pinkenson Feldman
Interactive Psychometrics for Autism With the Human Dynamic Clamp: Interpersonal Synchrony From Sensorimotor to Sociocognitive Domains
Florence Baillin
Aline Lefebvre
Amandine Pedoux
Yann Beauxis
Denis-Alexander Engemann
Anna Maruani
Frederique Amsellem
J. A. Scott Kelso
Thomas Bourgeron
Richard Delorme
The human dynamic clamp (HDC) is a human–machine interface designed on the basis of coordination dynamics for studying realistic social in… (see more)teraction under controlled and reproducible conditions. Here, we propose to probe the validity of the HDC as a psychometric instrument for quantifying social abilities in children with autism spectrum disorder (ASD) and neurotypical development. To study interpersonal synchrony with the HDC, we derived five standardized scores following a gradient from sensorimotor and motor to higher sociocognitive skills in a sample of 155 individuals (113 participants with ASD, 42 typically developing participants; aged 5 to 25 years; IQ > 70). Regression analyses were performed using normative modeling on global scores according to four subconditions (HDC behavior “cooperative/competitive,” human task “in-phase/anti-phase,” diagnosis, and age at inclusion). Children with ASD had lower scores than controls for motor skills. HDC motor coordination scores were the best candidates for stratification and diagnostic biomarkers according to exploratory analyses of hierarchical clustering and multivariate classification. Independently of phenotype, sociocognitive skills increased with developmental age while being affected by the ongoing task and HDC behavior. Weaker performance in ASD for motor skills suggests the convergent validity of the HDC for evaluating social interaction. Results provided additional evidence of a relationship between sensorimotor and sociocognitive skills. HDC may also be used as a marker of maturation of sociocognitive skills during real-time social interaction. Through its standardized and objective evaluation, the HDC not only represents a valid paradigm for the study of interpersonal synchrony but also offers a promising, clinically relevant psychometric instrument for the evaluation and stratification of sociomotor dysfunctions.
Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia
Clara A. Moreau
Sebastian G. W. Urchs
Kumar Kuldeep
Pierre Orban
Catherine Schramm
Aurélie Labbe
Guillaume Huguet
Elise Douard
Pierre-Olivier Quirion
Amy Lin
Leila Kushan
Stephanie Grot
David Luck
Adrianna Mendrek
Stephane Potvin
Emmanuel Stip
Thomas Bourgeron
Alan C. Evans
Carrie E. Bearden … (see 2 more)
Sébastien Jacquemont