Lune Bellec

Noise covariance estimation in multi-task high-dimensional linear models

Kai Tan

Gabriel Romon

Lune Bellec

2024-08-01

Bernoulli (publié)

doi.org

arxiv.org

Nilearn and Big Data Facilitates Transdiagnostic Brain Biomarkers

Hao-Ting Wang

Natasha Clarke

Quentin Dessain

Fraçois Paugam

Lune Bellec

2024-05-01

Biological Psychiatry (publié)

doi.org

Scaling up ridge regression for brain encoding in a massive individual fMRI dataset

Sana Ahmadi

Lune Bellec

Tristan Glatard

2024-03-28

ArXiv (prépublication)

doi.org

arxiv.org

A benchmark of individual auto-regressive models in a massive fMRI dataset

Fraçois Paugam

Basile Pinsard

Guillaume Lajoie

Lune Bellec

Dense functional magnetic resonance imaging datasets open new avenues to create auto-regressive models of brain activity. Individual idiosyn… (voir plus)crasies are obscured by group models, but can be captured by purely individual models given sufficient amounts of training data. In this study, we compared several deep and shallow individual models on the temporal auto-regression of BOLD time series recorded during a natural video watching task. The best performing models were then analyzed in terms of their data requirements and scaling, subject specificity and the space-time structure of their predicted dynamics. We found the Chebnets, a type of graph convolutional neural network, to be best suited for temporal BOLD auto-regression, closely followed by linear models. Chebnets demonstrated an increase in performance with increasing amounts of data, with no complete saturation at 9 h of training data. Good generalization to other kinds of video stimuli and to resting state data marked the Chebnets’ ability to capture intrinsic brain dynamics rather than only stimulus-specific autocorrelation patterns. Significant subject specificity was found at short prediction time lags. The Chebnets were found to capture lower frequencies at longer prediction time lags, and the spatial correlations in predicted dynamics were found to match traditional functional connectivity networks. Overall, these results demonstrate that large individual fMRI datasets can be used to efficiently train purely individual auto-regressive models of brain activity, and that massive amounts of individual data are required to do so. The excellent performance of the Chebnets likely reflects their ability to combine spatial and temporal interactions on large time scales at a low complexity cost. The non-linearities of the models did not appear as a key advantage. In fact, surprisingly, linear versions of the Chebnets appeared to outperform the original nonlinear ones. Individual temporal auto-regressive models have the potential to improve the predictability of the BOLD signal. This study is based on a massive, publicly-available dataset, which can serve for future benchmarks of individual auto-regressive modeling.

2024-01-01

Imaging Neuroscience (publié)

doi.org

Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset

Shima Rastegarnia

Marie St-Laurent

Elizabeth DuPre

Basile Pinsard

Lune Bellec

2023-12-01

NeuroImage (publié)

doi.org

Behavioral Imitation with Artificial Neural Networks Leads to Personalized Models of Brain Dynamics During Videogame Play

Anirudha Kemtur

Fraçois Paugam

Basile Pinsard

Yann Harel

Pravish Sainath

Maximilien Le Clei

Julie Boyle

Karim Jerbi

Lune Bellec

Artificial Neural networks (ANN) trained on complex tasks are increasingly used in neuroscience to model brain dynamics, a process called br… (voir plus)ain encoding. Videogames have been extensively studied in the field of artificial intelligence, but have hardly been used yet for brain encoding. Videogames provide a promising framework to understand brain activity in a rich, engaging, and active environment. A major challenge raised by complex videogames is that individual behavior is highly variable across subjects, and we hypothesized that ANNs need to account for subject-specific behavior in order to properly capture brain dynamics. In this study, we used ANNs to model functional magnetic resonance imaging (fMRI) and behavioral gameplay data, both collected while subjects played the Shinobi III videogame. Using imitation learning, we trained an ANN to play the game while closely replicating the unique gameplay style of individual participants. We found that hidden layers of our imitation learning model successfully encoded task-relevant neural representations, and predicted individual brain dynamics with higher accuracy than models trained on other subjects’ gameplay or control models. The highest correlations between layer activations and brain signals were observed in biologically plausible brain areas, i.e. somatosensory, attention, and visual networks. Our results demonstrate that combining imitation learning, brain imaging, and videogames can allow us to model complex individual brain patterns derived from decision making in a rich, complex environment.

2023-11-01

bioRxiv (prépublication)

doi.org

Open design of a reproducible videogame controller for MRI and MEG

Yann Harel

André Cyr

Julie Boyle

Basile Pinsard

Jeremy Bernard

Marie-France Fourcade

Himanshu Aggarwal

Ana Fernanda Ponce

Bertrand Thirion

Karim Jerbi

Lune Bellec

2023-11-01

PLOS ONE (publié)

doi.org

The Canadian Open Neuroscience Platform—An open science framework for the neuroscience community

Rachel J. Harding

Patrick Bermudez

Alexander Bernier

Michael Beauvais

Lune Bellec

Sean Hill

Bartha M. Knoppers

Agah Karakuzu

Paul Pavlidis

Jean-Baptiste Poline

Jane Roskams

Nikola Stikov

Jessica Stone

Stephen Strother

Conp Consortium

Alan C. Evans

The Canadian Open Neuroscience Platform (CONP) takes a multifaceted approach to enabling open neuroscience, aiming to make research, data, a… (voir plus)nd tools accessible to everyone, with the ultimate objective of accelerating discovery. Its core infrastructure is the CONP Portal, a repository with a decentralized design, where datasets and analysis tools across disparate platforms can be browsed, searched, accessed, and shared in accordance with FAIR principles. Another key piece of CONP infrastructure is NeuroLibre, a preprint server capable of creating and hosting executable and fully reproducible scientific publications that embed text, figures, and code. As part of its holistic approach, the CONP has also constructed frameworks and guidance for ethics and data governance, provided support and developed resources to help train the next generation of neuroscientists, and has fostered and grown an engaged community through outreach and communications. In this manuscript, we provide a high-level overview of this multipronged platform and its vision of lowering the barriers to the practice of open neuroscience and yielding the associated benefits for both individual researchers and the wider community.

2023-07-27

PLOS Computational Biology (publié)

doi.org

A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn

Hao-Ting Wang

Steven L. Meisler

Hanad Sharmarke

Natasha Clarke

Nicolas Gensollen

Christopher J Markiewicz

Fraçois Paugam

Bertrand Thirion

Lune Bellec

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) analyses. Many viable str… (voir plus)ategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a fully reproducible denoising benchmark featuring a range of denoising strategies and evaluation metrics, built primarily on the fMRIPrep and Nilearn software packages. We apply this reproducible benchmark to investigate the robustness of the conclusions across two different datasets and two versions of fMRIPrep. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks, especially ICA-AROMA. These results demonstrate that a reproducible denoising benchmark can effectively assess the robustness of conclusions across multiple datasets and software versions. Technologies such as BIDS-App, the Jupyter Book and Neurolibre provided the infrastructure to publish the metadata and report figures. Readers can reproduce the report figures beyond the ones reported in the published manuscript. With the denoising benchmark, we hope to provide useful guidelines for the community, and that our software infrastructure will facilitate continued development as the state-of-the-art advances.

2023-01-01

(publié)

www.semanticscholar.org

Functional connectivity subtypes associate robustly with ASD diagnosis

Sebastian G. W. Urchs

Angela Tam

Pierre Orban

Clara A. Moreau

Yassine Benhajali

Hien Duy Nguyen

Alan C. Evans

Lune Bellec

Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes th… (voir plus)is neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose autism heterogeneity into subtypes of connectivity and promises an unbiased framework to investigate behavioral symptoms and causative genetic factors. Yet, the robustness and generalizability of functional connectivity subtypes is unknown. Here, we show that a simple hierarchical cluster analysis can robustly relate a given individual and brain network to a connectivity subtype, but that continuous assignments are more robust than discrete ones. We also found that functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and these associations generalize to independent replication data. We explored systematically 18 different brain networks as we expected them to associate with different behavioral profiles as well as different key regions. Contrary to this prediction, autism functional connectivity subtypes converged on a common topography across different networks, consistent with a compression of the primary gradient of functional brain organization, as previously reported in the literature. Our results support the use of data driven clustering as a reliable data dimensionality reduction technique, where any given dimension only associates moderately with clinical manifestations.

2022-10-04

eLife (publié)

doi.org

Functional connectivity subtypes associate robustly with ASD diagnosis

S. Urchs

Angela Tam

Pierre Orban

C. Moreau

Yassine Benhajali

Hien Duy Nguyen

Alan C. Evans

Lune Bellec

Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes th… (voir plus)is neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose autism heterogeneity into subtypes of connectivity and promises an unbiased framework to investigate behavioral symptoms and causative genetic factors. Yet, the robustness and generalizability of functional connectivity subtypes is unknown. Here, we show that a simple hierarchical cluster analysis can robustly relate a given individual and brain network to a connectivity subtype, but that continuous assignments are more robust than discrete ones. We also found that functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and these associations generalize to independent replication data. We explored systematically 18 different brain networks as we expected them to associate with different behavioral profiles as well as different key regions. Contrary to this prediction, autism functional connectivity subtypes converged on a common topography across different networks, consistent with a compression of the primary gradient of functional brain organization, as previously reported in the literature. Our results support the use of data driven clustering as a reliable data dimensionality reduction technique, where any given dimension only associates moderately with clinical manifestations.

2022-10-04

eLife (published)

doi.org

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

Guillaume Dumas

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

Lune Bellec

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

Agah 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

2021-06-01

Neuron (publié)

doi.org

Peu importe la taille : démocratiser la découverte de protéines avec l'IA

Boussole des politiques en IA

Demandes de supervision

Lune Bellec

Biographie

Étudiants actuels

Publications

Peu importe la taille : démocratiser la découverte de protéines avec l'IA

Boussole des politiques en IA

Demandes de supervision

Mots-clés populaires:

Lune Bellec

Biographie

Étudiants actuels

Publications