Guillaume Dumas

mahta.ramezanian@mila.quebec

Pascal Tikeng Notsawo

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

pascal.tikeng@mila.quebec

Postdoctorat - Université de Montréal

Co-superviseur⋅e :

zahra.sheikhbahaee@mila.quebec

Github

Publications

Interpersonal attunement in social interactions: from collective psychophysiology to inter-personalized psychiatry and beyond

Dimitris Bolis

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)

Restoring the missing person to personalized medicine and precision psychiatry

Ana Gómez-Carrillo

Vincent Paquin

Laurence J. Kirmayer

2023-02-09

Frontiers in Neuroscience (publié)

GOKU-UI: Ubiquitous Inference through Attention and Multiple Shooting for Continuous-time Generative Models

Germán Abrevaya

Mahta Ramezanian-Panahi

Jean-Christophe Gagnon-Audet

Pablo Polosecki

Silvina Ponce Dawson

Guillermo Cecchi

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)

PyNM: a Lightweight Python implementation of Normative Modeling

Annabelle Harvey

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)

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

2022-12-04

Autism Research (publié)

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

H. Jongsma

Gabriel Gatica-bahamonde

Carrie Allison

F. Matthews

Simon Baron-Cohen

C. Brayne

2022-12-01

The Lancet Child & Adolescent Health (publié)

Towards Clinical Phenotyping at Scale with Serious Games in Mixed Reality

Mariem Hafsia

Romain Trachel

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

From analytic to synthetic-organizational pluralisms: A pluralistic enactive psychiatry

Christophe Gauld

Kristopher Nielsen

Manon Job

Hugo Bottemanne

2022-09-27

Frontiers in Psychiatry (publié)

Stratifying the autistic phenotype using electrophysiological indices of social perception

Luke Mason

Carolin Moessnang

Christopher H. Chatham

Lindsay Ham

Julian Tillmann

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)

Generative Models of Brain Dynamics

Mahta Ramezanian-Panahi

Germán Abrevaya

Jean-Christophe Gagnon-Audet

Vikram Voleti

2022-07-15

Frontiers in Artificial Intelligence (publié)

Naming Autism in the Right Context

Andres Roman-Urrestarazu

Varun Warrier

2022-07-01

JAMA Pediatrics (publié)

Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice

Christophe Gauld

Julien Maquet

Jean‐Arthur Micoulaud‐Franchi

Background Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discusse… (voir plus)d both by the public and by various scientific communities. Objective This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed. Methods First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term #autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term “autism” in PubMed without time restriction. Lastly, we compared the results of the 2 databases. Results We retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior. Conclusions This study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries.

2022-06-15

Journal of Medical Internet Research (publié)