Guillaume Dumas

Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach

Ghazaleh Ranjabaran

Quentin Moreau

Adrien Dubois

This study introduces a self-supervised learning (SSL) approach to hyperscanning electroencephalography (EEG) data, targeting the identifica… (voir plus)tion of autism spectrum condition (ASC) during social interactions. Hyperscanning enables simultaneous recording of neural activity across interacting individuals, offering a novel path for studying brain-to-brain synchrony in ASC. Leveraging a large-scale, single-brain EEG dataset for SSL pretraining, we developed a multi-brain classification model fine-tuned with hyperscanning data from dyadic interactions involving ASC and neurotypical participants. The SSL model demonstrated superior performance (78.13% accuracy) compared to supervised baselines and logistic regression using spectral EEG biomarkers. These results underscore the efficacy of SSL in addressing the challenges of limited labeled data, enhancing EEG-based diagnostic tools for ASC, and advancing research in social neuroscience.

2025-01-30

bioRxiv (prépublication)

La communication financière à l’épreuve de la crise COVID : une gestion des impressions ?

Corinne Bessieux-Ollier

Grégoire Davrinche

Nous étudions l’impact de la crise du COVID-19 sur la gestion des impressions pratiquée par les entreprises françaises cotées. Cette c… (voir plus)rise ayant eu un impact fort sur l’activité des entreprises, nous observons si les dirigeants modifient la manière de présenter l’information liée aux résultats non-GAAP, à travers l’utilisation de stratégies d’obscurcissement. Les données sur la gestion des impressions ont été collectées manuellement dans les communiqués de résultats annuels des entreprises du SBF 120 sur la période 2018-2020. Nous constatons une diminution générale du niveau de gestion des impressions en période de crise, notamment pour les entreprises des secteurs ayant été les plus impactés par la crise COVID. Cette diminution est toutefois moins prononcée pour les entreprises ayant sous-performé par rapport à leur secteur d’activité et pour les entreprises dont la performance a le plus diminué (indépendamment du secteur auquel elles appartiennent). Nos résultats suggèrent que les entreprises dont la baisse de performance pourrait être attribuée à des causes internes (résultats très défavorables, résultats en deçà du secteur d’activité) demeurent soucieuses de l’image qu’elles renvoient et maintiennent leur niveau de gestion des impressions malgré la crise.

2025-01-29

Comptabilité Contrôle Audit (publié)

Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex

Kuldeep Kumar

Sayeh Kazem

Guillaume Huguet

Thomas Renne

Worrawat Engchuan

Martineau Jean-Louis

Jakub Kopal

Zohra Saci

Omar Shanta

Bhooma Thiruvahindrapuram

Jeffrey R. MacDonald

Josephine Mollon

Laura Schultz

Emma E M Knowles

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris

Simon R. Cox

Gunter Schumann … (voir 9 de plus)

Zdenka Pausova

Celia M. T. Greenwood

Tomas Paus

Stephen W Scherer

Laura Almasy

Jonathan Sebat

David C. Glahn

Sébastien Jacquemont

Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (voir plus) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes

2025-01-07

bioRxiv (prépublication)

Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex

Kuldeep Kumar

Sayeh Kazem

Guillaume Huguet

Thomas Renne

Worrawat Engchuan

Martineau Jean-Louis

Jakub Kopal

Zohra Saci

Omar Shanta

Bhooma Thiruvahindrapuram

Jeffrey R. MacDonald

Josephine Mollon

Laura Schultz

Emma E M Knowles

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris

Simon R. Cox

Gunter Schumann … (voir 9 de plus)

Zdenka Pausova

Celia M. T. Greenwood

Tomas Paus

Stephen W Scherer

Laura Almasy

Jonathan Sebat

David C. Glahn

Sébastien Jacquemont

Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (voir plus) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes

2025-01-07

bioRxiv (prépublication)

Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings.

Hanna Seelemeyer

Caroline Gurr

Johanna Leyhausen

Lisa M. Berg

Charlotte M. Pretzsch

Tim Schäfer

Bassem Hermila

Christine M. Freitag

Eva Loth

Beth Oakley

Luke Mason

Jan K. Buitelaar

Christian Beckmann

Dorothea L. Floris

Tony Charman

Tobias Banaschewski

Thomas Bourgeron

Jumana Ahmad

Sara Ambrosino

Bonnie Auyeung … (voir 56 de plus)

Simon Baron-Cohen

Sarah Baumeister

Sven Bölte

Carsten Bours

Michael Brammer

Daniel Brandeis

Claudia Brogna

Yvette de Bruijn

Bhismadev Chakrabarti

Ineke Cornelissen

Daisy Crawley

Flavio Dell’Acqua

Sarah Durston

Christine Ecker

Jessica Faulkner

Vincent Frouin

Pilar Garcés

David Goyard

Lindsay Ham

Hannah Hayward

Joerg F. Hipp

Rosemary Holt

Mark Johnson

Emily J. H. Jones

Prantik Kundu

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 Bast

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

Julian Tillmann

Roberto Toro

Heike Tost

Jack Waldman

Steve C.R. Williams

Caroline Wooldridge

Marcel P. Zwiers

Declan Murphy

2024-12-01

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (publié)

Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings.

Hanna Seelemeyer

Caroline Gurr

Johanna Leyhausen

Lisa M. Berg

Charlotte M. Pretzsch

Tim Schäfer

Bassem Hermila

Christine M. Freitag

Dorothea L. Floris

Emily Jones

Jumana Ahmad

Sara Ambrosino

Bonnie Auyeung

Tobias Banaschewski

Simon Baron-Cohen

Sarah Baumeister

Christian Beckmann

Sven Bölte

Thomas Bourgeron

Carsten Bours … (voir 58 de plus)

Michael Brammer

Daniel Brandeis

Claudia Brogna

Yvette de Bruijn

Jan K. Buitelaar

Bhismadev Chakrabarti

Tony Charman

Ineke Cornelissen

Daisy Crawley

Flavio Dell’Acqua

Sarah Durston

Jessica Faulkner

Vincent Frouin

Pilar Garcés

David Goyard

Lindsay Ham

Hannah Hayward

Joerg F. Hipp

Rosemary Holt

Mark Johnson

Emily J. H. Jones

Prantik Kundu

Meng-Chuan Lai

Xavier Liogier D’ardhuy

Michael V. Lombardo

Eva Loth

David J. Lythgoe

René Mandl

Andre Marquand

Luke Mason

Maarten Mennes

Andreas Meyer-Lindenberg

Carolin Moessnang

Nico Bast

Declan G.M. 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

Julian Tillmann

Roberto Toro

Heike Tost

Jack Waldman

Steve C. R. Williams

Caroline Wooldridge

Marcel P. Zwiers

Declan Murphy

Christine Ecker

2024-12-01

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (publié)

Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings.

Hanna Seelemeyer

Caroline Gurr

Johanna Leyhausen

Lisa M. Berg

Charlotte M. Pretzsch

Tim Schäfer

Bassem Hermila

Christine M. Freitag

Eva Loth

Beth Oakley

Luke Mason

Jan K. Buitelaar

Christian Beckmann

Dorothea L. Floris

Tony Charman

Tobias Banaschewski

Thomas Bourgeron

Jumana Ahmad

Sara Ambrosino

Bonnie Auyeung … (voir 56 de plus)

Simon Baron-Cohen

Sarah Baumeister

Sven Bölte

Carsten Bours

Michael Brammer

Daniel Brandeis

Claudia Brogna

Yvette de Bruijn

Bhismadev Chakrabarti

Ineke Cornelissen

Daisy Crawley

Flavio Dell’Acqua

Sarah Durston

Christine Ecker

Jessica Faulkner

Vincent Frouin

Pilar Garcés

David Goyard

Lindsay Ham

Hannah Hayward

Joerg F. Hipp

Rosemary Holt

Mark Johnson

Emily J. H. Jones

Prantik Kundu

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 Bast

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

Julian Tillmann

Roberto Toro

Heike Tost

Jack Waldman

Steve C. R. Williams

Caroline Wooldridge

Marcel P. Zwiers

Declan Murphy

2024-12-01

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (publié)

Introducing Brain Foundation Models

Mohammad Javad Darvishi Bayazi

Hena Ghonia

Roland Riachi

Bruno Aristimunha

Arian Khorasani

Md Rifat Arefin

Sylvain Chevallier

Amin Darabi

Brain function represents one of the most complex systems driving our world. Decoding its signals poses significant challenges, particularly… (voir plus) due to the limited availability of data and the high cost of recordings. The existence of large hospital datasets and laboratory collections partially mitigates this issue. However, the lack of standardized recording protocols, varying numbers of channels, diverse setups, scenarios, and recording devices further complicate the task. This work addresses these challenges by introducing the Brain Foundation Model (BFM), a suite of open-source models trained on brain signals. These models serve as foundational tools for various types of time-series neuroimaging tasks. This work presents the first model of the BFM series, which is trained on electroencephalogram signal data. Our results demonstrate that BFM-EEG can generate signals more accurately than other models. Upon acceptance, we will release the model weights and pipeline.

2024-10-10

NeurIPS.cc/2024/Workshop/TSALM (publié)

LLMs and Personalities: Inconsistencies Across Scales

Tosato Tommaso

Mahmood Hegazy

David Lemay

Mohammed Abukalam

This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (voir plus)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.

2024-10-09

NeurIPS.cc/2024/Workshop/Behavioral_ML (présentation orale)

LLMs and Personalities: Inconsistencies Across Scales

Tosato Tommaso

Mahmood Hegazy

David Lemay

Mohammed Abukalam

This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (voir plus)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.

2024-10-09

NeurIPS.cc/2024/Workshop/Behavioral_ML (présentation orale)

Scalable Approaches for a Theory of Many Minds

Maximilian Puelma Touzel

Amin Memarian

Matthew D Riemer

Andrei Mircea

Andrew Robert Williams

Elin Ahlstrand

Lucas Lehnert

Rupali Bhati

A major challenge as we move towards building agents for real-world problems, which could involve a massive number of human and/or machine a… (voir plus)gents, is that we must learn to reason about the behavior of these many other agents. In this paper, we consider the problem of scaling a predictive Theory of Mind (ToM) model to a very large number of interacting agents with a fixed computational budget. Motivated by the limited diversity of agent types, existing approaches to scalable TOM learn versatile single-agent representations for quickly adapting to new agents encountered sequentially. We consider the more general setting that many agents are observed in parallel and formulate the corresponding Theory of Many Minds (ToMM) problem of estimating the joint policy. We frame the scaling behavior of solutions in terms of parameter sharing schemes and in particular propose two parameter-free architectural features that endow models with the ability to exploit action correlations: encoding a multi-agent context, and decoding through an abstracted joint action space. The increased predictive capabilities that have come with foundation models have made it easier to imagine the possibility of using these models to make simulations that imitate the behavior of many agents within complex real-world systems. Being able to perform these simulations in a general-purpose way would not only help make more capable agents, it also would be a very useful capability for applications in social science, political science, and economics.

2024-06-18

ICML.cc/2024/Workshop/Agentic_Markets (poster)