Prof. Jean-François Mangin Profile

Prof. Jean-François Mangin

at NeuroSpin

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Publications (5)

Proceedings Article | 2 April 2024 Presentation + Paper

Optimizing contrastive learning for cortical folding pattern detection

Aymeric Gaudin, Louise Guillon, Clara Fischer, Arnaud Cachia, Denis Rivière, Jean-François Mangin, Joël Chavas

Proceedings Volume 12926, 129260Q (2024) https://doi.org/10.1117/12.3006183

KEYWORDS: Education and training, Double patterning technology, Data modeling, Magnetic resonance imaging, Voxels, Mathematical optimization, Brain, 3D modeling, Databases, 3D image processing

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The human cerebral cortex has many bumps and grooves called gyri and sulci. Even though there is a high interindividual consistency for the main cortical folds, this is not the case when we examine the exact shapes and details of the folding patterns. Because of this complexity, characterizing the cortical folding variability and relating them to subjects’ behavioral characteristics or pathologies is still an open scientific problem. Classical approaches include labeling a few specific patterns, either manually or semi-automatically, based on geometric distances, but the recent availability of MRI image datasets of tens of thousands of subjects makes modern deep-learning techniques particularly attractive. Here, we build a self-supervised deep-learning model to detect folding patterns in the cingulate region. We train a contrastive self-supervised model (SimCLR) on both Human Connectome Project (1101 subjects) and UKBioBank (21070 subjects) datasets with topological-based augmentations on the cortical skeletons, which are topological objects that capture the shape of the folds. We explore several backbone architectures (convolutional network, DenseNet, and PointNet) for the SimCLR. For evaluation and testing, we perform a linear classification task on a database manually labeled for the presence of the ”double-parallel” folding pattern in the cingulate region, which is related to schizophrenia characteristics. The best model, giving a test AUC of 0.76, is a convolutional network with 6 layers, a 10-dimensional latent space, a linear projection head, and using the branch-clipping augmentation. This is the first time that a self-supervised deep learning model has been applied to cortical skeletons on such a large dataset and quantitatively evaluated. We can now envisage the next step: applying it to other brain regions to detect other biomarkers. The GitHub repository is publicly available on https://github.com/neurospin-projects/2022 jchavas cingulate inhibitory control.

Proceedings Article | 6 March 2023 Paper

Group-wise cortical parcellation based on structural connectivity and hierarchical clustering

Joaquín Molina, Cristóbal Mendoza, Claudio Román, Josselin Houenou, Cyril Poupon, Jean François Mangin, Wael El-Deredy, Cecilia Hernández, Pamela Guevara

Proceedings Volume 12567, 125670L (2023) https://doi.org/10.1117/12.2670138

KEYWORDS: Matrices, Magnetic resonance imaging, Brain, White matter, Diffusion

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Proceedings Article | 6 March 2023 Paper

Fiber bundles simulator using exponential curves to validate fiber clustering algorithms

Elida Poo, Carlos Kublik, Josselin Houenou, Cyril Poupon, Jean-François Mangin, Cecilia Hernández, Pamela Guevara

Proceedings Volume 12567, 125670J (2023) https://doi.org/10.1117/12.2669811

KEYWORDS: Computer simulations, Optical simulations, Brain, Evolutionary algorithms, Matrices, White matter, Algorithm development, Voxels, Reconstruction algorithms, Tunable filters

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Proceedings Article | 6 March 2023 Paper

Superficial white matter shape characterization using hierarchical clustering and a multi-subject bundle atlas

C. Mendoza, C. Román, J. Molina, C. Poupon, J.-F. Mangin, C. Hernández, P. Guevara

Proceedings Volume 12567, 125670N (2023) https://doi.org/10.1117/12.2669738

KEYWORDS: Tunable filters, Matrices, White matter, Databases, Brain, Reconstruction algorithms, Novel fiber development, Neuroscience

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Proceedings Article | 21 December 2018 Paper

Sulcal-based morphometry in Parkinson’s disease: a study of reliability and disease effects

Fabrizio Pizzagalli, Guillaume Auzias, Armand Amini, Joshua Faskowitz, Faisal Rashid, Daniel Moyer, Peter Kochunov, Denis Rivière, Jean-François Mangin, Paul Thompson, Neda Jahanshad

Proceedings Volume 10975, 109750T (2018) https://doi.org/10.1117/12.2511590

KEYWORDS: Reliability, Nomenclature, Brain, Magnetic resonance imaging, Feature extraction, Genetics, Neuroimaging, Statistical analysis, Neurodegeneration, Image segmentation

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