Portrait de Guillaume Dumas

Guillaume Dumas

Membre académique associé
Professeur agrégé, Université de Montréal, Département de psychiatrie et d’addictologie
Professeur adjoint, McGill University, Département de psychiatrie

Biographie

Guillaume Dumas est professeur agrégé de psychiatrie computationnelle à la Faculté de médecine de l'Université de Montréal et chercheur principal du laboratoire de psychiatrie de précision et de physiologie sociale du Centre de recherche du CHU Sainte-Justine. Il est titulaire de la chaire IVADO IA en santé mentale et chercheur-boursier junior 1 du Fonds de recherche du Québec - Santé (FRQS) dans le domaine de l’ IA en santé et de la santé numérique. En 2023, il a été retenu dans le cadre du Programme des chercheurs mondiaux CIFAR-Azrieli pour le programme de recherche Cerveau, esprit et conscience. Il a également été nommé parmi les Futurs leaders canadiens de la recherche sur le cerveau par la Fondation Brain Canada.

Il a auparavant été chercheur permanent en neurosciences et en biologie computationnelle à l'Institut Pasteur (Paris, France), ainsi que chercheur postdoctoral au Center for Complex Systems and Brain Sciences à l’Université Florida Atlantic (FAU), aux États-Unis. Il est titulaire d'un diplôme d'ingénieur en ingénierie avancée et informatique (École centrale Paris), de deux masters (physique théorique, Université Paris-Saclay; sciences cognitives, ENS/EHESS/Paris 5) et d'un doctorat en neurosciences cognitives (Sorbonne Université).

Ses recherches visent à combiner l’intelligence artificielle, les neurosciences cognitives et la médecine numérique à travers un programme interdisciplinaire suivant deux axes principaux :

- L’intelligence artificielle en santé mentale, par la création de nouveaux algorithmes pour étudier le développement de l'architecture cognitive humaine et pour fournir une médecine personnalisée en neuropsychiatrie grâce à des données allant du génome à celles des téléphones intelligents;

- Les neurosciences sociales en intelligence artificielle, par la traduction de la recherche fondamentale sur le cerveau et le formalisme des systèmes dynamiques en des modèles hybrides neurocomputationnels et d’apprentissage automatique (NeuroML) et de nouvelles architectures présentant des capacités d'apprentissage social (NeuroIA Sociale et IHM).

Étudiants actuels

Visiteur de recherche indépendant - Université de Montréal
Superviseur⋅e principal⋅e :
Maîtrise recherche - Université de Montréal
Maîtrise recherche - Université de Montréal
Superviseur⋅e principal⋅e :
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Postdoctorat - Université de Montréal
Co-superviseur⋅e :

Publications

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
Pierre Philip
Jean‐Arthur Micoulaud‐Franchi
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 … (voir 46 de plus)
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Ineke Cornelissen
Daisy Crawley
Flavio Dell’Acqua
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
Laurence 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
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
Jean‐Arthur Micoulaud‐Franchi
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
Jean‐Arthur Micoulaud‐Franchi
Symptom network analysis of the sleep disorders diagnostic criteria based on the clinical text of the ICSD‐3
Christophe Gauld
Régis Lopez
C. Morin
Pierre A. GEOFFROY
Julien Maquet
Pierre Desvergnes
Aileen McGonigal
Yves A. Dauvilliers
Pierre Philip
J-a Micoulaud-franchi
The third edition of the International Classification of Sleep Disorders (ICSD‐3) is the authoritative clinical text for the diagnosis of … (voir plus)sleep disorders. An important issue of sleep nosology is to better understand the relationship between symptoms found in conventional diagnostic manuals and to compare classifications. Nevertheless, to our knowledge, there is no specific exhaustive work on the general structure of the networks of symptoms of sleep disorders as described in diagnostic manuals. The general aim of the present study was to use symptom network analysis to explore the diagnostic criteria in the ICSD‐3 manual. The ICSD‐3 diagnostic criteria related to clinical manifestations were systematically identified, and the units of analysis (symptoms) were labelled from these clinical manifestation diagnostic criteria using three rules (“Conservation”, “Splitting”, “Lumping”). A total of 37 of the 43 main sleep disorders with 160 units of analysis from 114 clinical manifestations in the ICSD‐3 were analysed. A symptom network representing all individual ICSD‐3 criteria and connections between them was constructed graphically (network estimation), quantified with classical metrics (network inference with global and local measures) and tested for robustness. The global measure of the sleep symptoms network shows that it can be considered as a small world, suggesting a strong interconnection between symptoms in the ICSD‐3. Local measures show the central role of three kinds of bridge sleep symptoms: daytime sleepiness, insomnia, and behaviour during sleep symptoms. Such a symptom network analysis of the ICSD‐3 structure could provide a framework for better systematising and organising symptomatology in sleep medicine.
Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
Rémi Gau
Stephanie Noble
Katja Heuer
Katherine L. Bottenhorn
Isil P. Bilgin
Yu-Fang Yang
Julia M. Huntenburg
Johanna M.M. Bayer
Richard A.I. Bethlehem
Shawn A. Rhoads
Christoph Vogelbacher
V. Borghesani
Elizabeth Levitis
Hao-Ting Wang
Sofie Van Den Bossche
Xenia Kobeleva
Jon Haitz Legarreta
Samuel Guay
Selim Melvin Atay
Gael Varoquaux … (voir 199 de plus)
Dorien C. Huijser
Malin S. Sandström
Peer Herholz
Samuel A. Nastase
AmanPreet Badhwar
Simon Schwab
Stefano Moia
Michael Dayan
Yasmine Bassil
Paula P. Brooks
Matteo Mancini
James M. Shine
David O’Connor
Xihe Xie
Davide Poggiali
Patrick Friedrich
Anibal S. Heinsfeld
Lydia Riedl
Roberto Toro
César Caballero-Gaudes
Anders Eklund
Kelly G. Garner
Christopher R. Nolan
Damion V. Demeter
Fernando A. Barrios
Junaid S. Merchant
Elizabeth A. McDevitt
Robert Oostenveld
R. Cameron Craddock
Ariel Rokem
Andrew Doyle
Satrajit S. Ghosh
Aki Nikolaidis
Olivia W. Stanley
Eneko Uruñuela
Nasim Anousheh
Aurina Arnatkeviciute
Guillaume Auzias
Dipankar Bachar
Elise Bannier
Ruggero Basanisi
Arshitha Basavaraj
Marco Bedini
R. Austin Benn
Kathryn Berluti
Steffen Bollmann
Saskia Bollmann
Claire Bradley
Jesse Brown
Augusto Buchweitz
Patrick Callahan
Micaela Y. Chan
Bramsh Q. Chandio
Theresa Cheng
Sidhant Chopra
Ai Wern Chung
Thomas G. Close
Etienne Combrisson
Giorgia Cona
R. Todd Constable
Claire Cury
Kamalaker Dadi
Pablo F. Damasceno
Samir Das
Fabrizio De Vico Fallani
Krista DeStasio
Erin W. Dickie
Lena Dorfschmidt
Eugene P. Duff
Elizabeth DuPre
Sarah Dziura
Nathalia B. Esper
Oscar Esteban
Shreyas Fadnavis
Guillaume Flandin
Jessica E. Flannery
John Flournoy
Stephanie J. Forkel
Alexandre R. Franco
Saampras Ganesan
Siyuan Gao
José C. García Alanis
Eleftherios Garyfallidis
Tristan Glatard
Enrico Glerean
Javier Gonzalez-Castillo
Cassandra D. Gould van Praag
Abigail S. Greene
Geetika Gupta
Catherine Alice Hahn
Yaroslav O. Halchenko
Daniel Handwerker
Thomas S. Hartmann
Valérie Hayot-Sasson
Stephan Heunis
Felix Hoffstaedter
Daniela M. Hohmann
Corey Horien
Horea-Ioan Ioanas
Alexandru Iordan
Chao Jiang
Michael Joseph
Jason Kai
Agâh Karakuzu
David N. Kennedy
Anisha Keshavan
Ali R. Khan
Gregory Kiar
P. Christiaan Klink
Vincent Koppelmans
Serge Koudoro
Angela R. Laird
Georg Langs
Marissa Laws
Roxane Licandro
Sook-Lei Liew
Tomislav Lipic
Krisanne Litinas
Daniel J. Lurie
Désirée Lussier
Christopher R. Madan
Lea-Theresa Mais
Sina Mansour L
J.P. Manzano-Patron
Dimitra Maoutsa
Matheus Marcon
Daniel S. Margulies
Giorgio Marinato
Daniele Marinazzo
Christopher J. Markiewicz
Camille Maumet
Felipe Meneguzzi
David Meunier
Michael P. Milham
Kathryn L. Mills
Davide Momi
Clara A. Moreau
Aysha Motala
Iska Moxon-Emre
Thomas E. Nichols
Dylan M. Nielson
Gustav Nilsonne
Lisa Novello
Caroline O’Brien
Emily Olafson
Lindsay D. Oliver
John A. Onofrey
Edwina R. Orchard
Kendra Oudyk
Patrick J. Park
Mahboobeh Parsapoor
Lorenzo Pasquini
Scott Peltier
Cyril R. Pernet
Rudolph Pienaar
Pedro Pinheiro-Chagas
Jean-Baptiste Poline
Anqi Qiu
Tiago Quendera
Laura C. Rice
Joscelin Rocha-Hidalgo
Saige Rutherford
Mathias Scharinger
Dustin Scheinost
Deena Shariq
Thomas B. Shaw
Viviana Siless
Molly Simmonite
Nikoloz Sirmpilatze
Hayli Spence
Julia Sprenger
Andrija Stajduhar
Martin Szinte
Sylvain Takerkart
Angela Tam
Link Tejavibulya
Michel Thiebaut de Schotten
Ina Thome
Laura Tomaz da Silva
Nicolas Traut
Lucina Q. Uddin
Antonino Vallesi
John W. VanMeter
Nandita Vijayakumar
Matteo Visconti di Oleggio Castello
Jakub Vohryzek
Jakša Vukojević
Kirstie Jane Whitaker
Lucy Whitmore
Steve Wideman
Suzanne T. Witt
Hua Xie
Ting Xu
Chao-Gan Yan
Fang-Cheng Yeh
B.T. Thomas Yeo
Xi-Nian Zuo
Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks
Germán Abrevaya
Aleksandr Y. Aravkin
Peng Zheng
Jean-Christophe Gagnon-Audet
James Kozloski
Pablo Polosecki
David Cox
Silvina Ponce Dawson
Guillermo Cecchi
Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by … (voir plus)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.
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 … (voir 57 de plus)
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 … (voir 1 de plus)
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