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
Sujets de recherche
Apprentissage automatique médical
Apprentissage par renforcement
Apprentissage profond
Biologie computationnelle
Neurosciences computationnelles
Systèmes dynamiques
Théorie de l'apprentissage automatique
Site web
Google Scholar
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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

Juan David Vargas
Maîtrise recherche - UdeM
Kastel Natalie
Doctorat - UdeM
Mahta Ramezanian
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Irina Rish
Zahra Sheikhbahaee
Postdoctorat - UdeM
Co-superviseur⋅e :
Irina Rish
Github
Pascal Tikeng Notsawo
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Rabusseau
Site web
Github
Google Scholar
Tommaso Tosato
Postdoctorat - UdeM
Site web
Github
Google Scholar

Publications

Processing of social and monetary rewards in autism spectrum disorders
Sarah Baumeister
Carolin Moessnang
Nico Bast
Sarah Hohmann
Pascal Aggensteiner
Anna Kaiser
Julian Tillmann
David Goyard
Tony Charman
Sara Ambrosino
Simon Baron-Cohen
Christian Beckmann
Sven Bölte
Thomas Bourgeron
Annika Rausch
Daisy Crawley
Flavio Dell’Acqua
Guillaume Dumas
Sarah Durston
Christine Ecker … (voir 21 de plus)
Dorothea L. Floris
Vincent Frouin
Hannah Hayward
Rosemary Holt
Mark Johnson
Emily J. H. Jones
Meng-Chuan Lai
Michael V. Lombardo
Luke Mason
Beth Oakley
Marianne Oldehinkel
Antonio Persico
Antonia San José Cáceres
Thomas Wolfers
Eva Loth
Declan Murphy
Jan K. Buitelaar
Heike Tost
Andreas Meyer-Lindenberg
Tobias Banaschewski
Daniel Brandeis
Background Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous … (voir plus)neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD. Aims Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD. Method Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age). Results Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD. Conclusions Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD.
2023-03-01
The British Journal of Psychiatry (publié)
doi.org
Interpersonal attunement in social interactions: from collective psychophysiology to inter-personalized psychiatry and beyond
Dimitris Bolis
Guillaume Dumas
Leonhard Schilbach
In this article, we analyse social interactions, drawing on diverse points of views, ranging from dialectics, second-person neuroscience and… (voir plus) enactivism to dynamical systems, active inference and machine learning. To this end, we define interpersonal attunement as a set of multi-scale processes of building up and materializing social expectations—put simply, anticipating and interacting with others and ourselves. While cultivating and negotiating common ground, via communication and culture-building activities, are indispensable for the survival of the individual, the relevant multi-scale mechanisms have been largely considered in isolation. Here, collective psychophysiology, we argue, can lend itself to the fine-tuned analysis of social interactions, without neglecting the individual. On the other hand, an interpersonal mismatch of expectations can lead to a breakdown of communication and social isolation known to negatively affect mental health. In this regard, we review psychopathology in terms of interpersonal misattunement, conceptualizing psychiatric disorders as disorders of social interaction, to describe how individual mental health is inextricably linked to social interaction. By doing so, we foresee avenues for an inter-personalized psychiatry, which moves from a static spectrum of disorders to a dynamic relational space, focusing on how the multi-faceted processes of social interaction can help to promote mental health. This article is part of the theme issue ‘Concepts in interaction: social engagement and inner experiences’.
2023-02-13
Philosophical Transactions of the Royal Society B: Biological Sciences (publié)
doi.org
Sources of richness and ineffability for phenomenally conscious states
Xu Ji
Eric Elmoznino
George Deane
Axel Constant
Guillaume Dumas
Guillaume Lajoie
Jonathan Simon
Yoshua Bengio
Abstract Conscious states—state that there is something it is like to be in—seem both rich or full of detail and ineffable or hard to fu… (voir plus)lly describe 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.
2023-02-13
ArXiv (prépublication)
doi.org
arxiv.org
Sources of richness and ineffability for phenomenally conscious states
Xu Ji
Eric Elmoznino
George Deane
Axel Constant
Guillaume Dumas
Guillaume Lajoie
Jonathan Simon
Yoshua Bengio
Abstract Conscious states—state that there is something it is like to be in—seem both rich or full of detail and ineffable or hard to fu… (voir plus)lly describe 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.
2023-02-13
ArXiv (prépublication)
doi.org
arxiv.org
Restoring the missing person to personalized medicine and precision psychiatry
Ana Gómez-Carrillo
Vincent Paquin
Guillaume Dumas
Laurence J. Kirmayer
2023-02-09
Frontiers in Neuroscience (publié)
doi.org
GOKU-UI: Ubiquitous Inference through Attention and Multiple Shooting for Continuous-time Generative Models
Germán Abrevaya
Mahta Ramezanian-Panahi
Jean-Christophe Gagnon-Audet
Irina Rish
Pablo Polosecki
Silvina Ponce Dawson
Guillermo Cecchi
Guillaume Dumas
Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnosti… (voir plus)c machine learning techniques. In this work, we introduce GOKU-UI, an evolution of the SciML generative model GOKU-nets. The GOKU-UI broadens the original model’s spectrum to incorporate other classes of differential equations, such as Stochastic Differential Equations (SDEs), and integrates a distributed, i.e. ubiquitous, inference through attention mechanisms and a novel multiple shooting training strategy in the latent space. These enhancements have led to a significant increase in its performance in both reconstruction and forecast tasks, as demonstrated by our evaluation of simulated and empirical data. Specifically, GOKU-UI outperformed all baseline models on synthetic datasets even with a training set 32-fold smaller, underscoring its remarkable data efficiency. Furthermore, when applied to empirical human brain data, while incorporating stochastic Stuart-Landau
2023-01-01
arXiv.org (prépublication)
doi.org
PyNM: a Lightweight Python implementation of Normative Modeling
Annabelle Harvey
Guillaume Dumas
The majority of studies in neuroimaging and psychiatry are focussed on case-control analysis (Marquand et al., 2019). However, case-control … (voir plus)relies on well-defined groups which is more the exception than the rule in biology. Psychiatric conditions are diagnosed based on symptoms alone, which makes for heterogeneity at the biological level (Marquand et al., 2016). Relying on mean differences obscures this heterogeneity and the resulting loss of information can produce unreliable results or misleading conclusions (Loth et al., 2021).
2022-12-08
Journal of Open Source Software (publié)
doi.org
Tackling hypo and hyper sensory processing heterogeneity in autism: From clinical stratification to genetic pathways
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 H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marion Leboyer
Tony Charman
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
2022-12-04
Autism Research (publié)
doi.org
Autism incidence and spatial analysis in more than 7 million pupils in English schools: a retrospective, longitudinal, school registry study.
Andres Roman-Urrestarazu
Justin Christopher Yang
R. van Kessel
Varun Warrier
Guillaume Dumas
H. Jongsma
Gabriel Gatica-bahamonde
Carrie Allison
F. Matthews
Simon Baron-Cohen
C. Brayne
2022-12-01
The Lancet Child & Adolescent Health (publié)
doi.org
Towards Clinical Phenotyping at Scale with Serious Games in Mixed Reality
Mariem Hafsia
Romain Trachel
Guillaume Dumas
Context: Mental healthcare systems are facing an ever-growing demand for appropriate assessment and intervention. Unfortunately, services ar… (voir plus)e often centralized, overloaded, and inaccessible, resulting in greater institutional and social inequities. Therefore, there is an urgent need to establish easy-to-implement methods for early diagnosis and personalized follow-up. In recent years, serious games have started to offer such a clinical tool at scale. Problem: There are critical challenges to the development of secure and inclusive serious games for clinical research. First, the quality of the data and features analyzed must be well defined early in the research process in order to draw meaningful conclusions. Second, algorithms must be aligned with the purpose of the research while not perpetuating bias. Finally, the technologies used must be widely accessible and sufficiently engaging for users. Focus of the paper: To tackle these challenges, we designed a participatory project that combines three innovative technologies: Mixed Reality, Serious Gaming, and Machine Learning. We analyze preliminary data with a focus on the identification of the players and the measurement of classical biases such as sex and environment of data collection. Method: We co-developed with patients and their families, as well as clinicians, a serious game in mixed reality specifically designed for evaluation and therapeutic intervention in autism. Preliminary data were collected from neurotypical individuals with a mixed reality headset. Relevant behavioral features were extracted and used to train several classification algorithms for player identification. Results: We were able to classify players above chance with slightly higher accuracy of neural networks. Interestingly, the accuracy was significantly higher when players were separated by sex. Furthermore, the uncontrolled condition showed better levels of accuracy than the controlled condition. This could mean that the data are richer when the player interacts freely with the game. Our proof of concept cannot exclude the possibility that this last result is linked to the experimental setup. Future development will clarify this point with a larger sample size and the use of deep learning algorithms. Implications: We show that serious games in mixed reality can be a valuable tool to collect clinical data. Our preliminary results highlight important biases to consider for future studies, especially for the sex and context of data collection. Next, we will evaluate the usability, accessibility, and tolerability of the device and the game in autistic children. In addition, we will evaluate the psychometric properties of the serious game, especially for patient stratification. This project aims to develop a platform for the diagnosis and therapy of autism, which can eventually be easily extended to other conditions and settings such as the evaluation of depression or stroke rehabilitation. Such a tool can offer novel possibilities for the study, evaluation, and treatment of mental conditions at scale, and thus ease the burden on healthcare systems.
2022-10-21
NeurIPS.cc/2022/Workshop/PAI4MH (poster)
openreview.net
openreview.net
From analytic to synthetic-organizational pluralisms: A pluralistic enactive psychiatry
Christophe Gauld
Kristopher Nielsen
Manon Job
Hugo Bottemanne
Guillaume Dumas
2022-09-27
Frontiers in Psychiatry (publié)
doi.org
Stratifying the autistic phenotype using electrophysiological indices of social perception
Luke Mason
Carolin Moessnang
Christopher H. Chatham
Lindsay Ham
Julian Tillmann
Guillaume Dumas
Claire Ellis
Claire Leblond
Freddy Cliquet
Thomas Bourgeron
Christian Beckmann
Tony Charman
Beth Oakley
Tobias Banaschewski
Andreas Meyer-Lindenberg
Simon Baron-Cohen
Sven Bölte
Jan K. Buitelaar
Sarah Durston
Eva Loth … (voir 7 de plus)
Bob Oranje
Antonio Persico
Flavio Dell’Acqua
Christine Ecker
Mark Johnson
Declan Murphy
Emily J. H. Jones
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication, but also great heter… (voir plus)ogeneity. To offer individualized medicine approaches, we need to better target interventions by stratifying autistic people into subgroups with different biological profiles and/or prognoses. We sought to validate neural responses to faces as a potential stratification factor in ASD by measuring neural (electroencephalography) responses to faces (critical in social interaction) in N = 436 children and adults with and without ASD. The speed of early-stage face processing (N170 latency) was on average slower in ASD than in age-matched controls. In addition, N170 latency was associated with responses to faces in the fusiform gyrus, measured with functional magnetic resonance imaging, and polygenic scores for ASD. Within the ASD group, N170 latency predicted change in adaptive socialization skills over an 18-month follow-up period; data-driven clustering identified a subgroup with slower brain responses and poor social prognosis. Use of a distributional data-driven cutoff was associated with predicted improvements of power in simulated clinical trials targeting social functioning. Together, the data provide converging evidence for the utility of the N170 as a stratification factor to identify biologically and prognostically defined subgroups in ASD. Description N170 latency to faces relates to fusiform activity and ASD genetics, predicts social prognosis, and could improve power in clinical trials. Exploiting face processing in patients with ASD The heterogeneity observed in patients with autism spectrum disorder (ASD) highlights the need for better patient stratification methods. Here, Mason et al. evaluated the use of the speed of early-stage face processing (N170 latency) for patient stratification and prognosis in subjects with ASD and age-matched healthy individuals. N170 latency was slower in individuals with ASD and correlated with response to faces measured with fMRI and with polygenic risk score. Among subjects with ASD, the N170 values stratified patients according to socialization prognosis and improved power in a simulated clinical trial. The results suggest that including N170 evaluation in patient stratification might help the design and development of patient-specific therapies for ASD.
2022-08-17
Science Translational Medicine (publié)
doi.org