Portrait de Lune Bellec

Lune Bellec

Membre affilié
Professeure titulaire, Université de Montréal, Département de psychologie
Sujets de recherche
Apprentissage automatique médical
NeuroIA
Neuroimagerie (IRMf)
Neurosciences computationnelles
Site web
Google Scholar
LinkedIn
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Biographie

Je suis professeure au département de psychologie de l’Université de Montréal et chercheuse principale du Laboratoire de simulation et d’exploration du cerveau (SIMEXP) à l’Institut universitaire de gériatrie de Montréal (CRIUGM). J’ai récemment rejoint Mila - Institut québécois d’intelligence artificielle en tant que membre affiliée, et je supervise des étudiant·e·s en informatique (neurosciences computationnelles cognitives) au DIRO, Université de Montréal.

Mon principal intérêt de recherche est d’entraîner des réseaux de neurones artificiels afin d’imiter conjointement l’activité cérébrale humaine individuelle et le comportement. Pour atteindre cet objectif, je dirige un effort intensif de collecte de données individuelles en neuroimagerie (IRMf, MEG) dans le cadre du projet Courtois sur la modélisation neuronale (CNeuroMod).

Je suis chercheuse boursière senior du FRQS, membre de l’Alliance québécoise en neurosciences et intelligence artificielle (UNIQUE), responsable du domaine Santé numérique au CRIUGM, co-responsable de la plateforme de gestion des données au CRIUGM et directrice adjointe à l’informatique neurofonctionnelle de l’Unité de neuroimagerie fonctionnelle (UNF) du CRIUGM.

Étudiants actuels

Hugo Delhaye
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Audrey Durand
Maximilien Le Clei
Doctorat - UdeM
Pravish Sainath
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Lajoie
Github

Publications

Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Julien Cohen-Adad
Abstract Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used q… (voir plus)ualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
2025-01-01
Imaging Neuroscience (publié)
doi.org
Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Julien Cohen-Adad
Abstract Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used q… (voir plus)ualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
2025-01-01
Imaging Neuroscience (publié)
doi.org
Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Julien Cohen-Adad
Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used qualitativ… (voir plus)e MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes three years of data collection and up to ten sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, FA, MD, and RD all exhibited high longitudinal reproducibility (intraclass correlation coefficient— ICC ≃ 1 and within-subject coefficient of variations— wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
2024-01-01
Imaging Neuroscience (publié)
doi.org