Publications

Journals

• C. Poiret, A. Bouyeure, S. Patil, C. Boniteau, E. Duchesnay, A. Grigis, F. Lemaitre, and M. Noulhiane (2024) “Attention-gated 3D CapsNet for robust hippocampal segmentation”, J Med Imaging (Bellingham)
• T. Dupont, N. Kentish-Barnes, F. Pochard, E. Duchesnay, and E. Azoulay (2024) “Prediction of post-traumatic stress disorder in family members of ICU patients: a machine learning approach”, Intensive Care Med
• C. Poiret, A. Bouyeure, S. Patil, A. Grigis, E. Duchesnay, M. Faillot, M. Bottlaender, F. Lemaitre, and M. Noulhiane (2023) “A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics”, Frontiers in Neuroinformatics
• A. Iftimovici, Q. He, C. Jiao, E. Duchesnay, M.-O. Krebs, O. Kebir, and B. Chaumette (2023) “Longitudinal MicroRNA Signature of Conversion to Psychosis”, Schizophr Bull
• A. Iftimovici, J. Bourgin, J. Houenou, O. Gay, A. Grigis, J. Victor, B. Chaumette, M.-O. Krebs, E. Duchesnay, and ICAAR-plus Study Group. (2023) “Asynchronous neural maturation predicts transition to psychosis”, Psychiatry Clin Neurosci
• J. Fraize, C. Fischer, M. Elmaleh-Bergès, E. Kerdreux, A. Beggiato, A. Ntorkou, E. Duchesnay, D. Bekha, O. Boespflug-Tanguy, R. Delorme, L. Hertz-Pannier, and D. Germanaud (2023) “Enhancing fetal alcohol spectrum disorders diagnosis with a classifier based on the intracerebellar gradient of volumetric undersizing”, Human Brain Mapping
• Y. Elandaloussi, D. L. Floris, P. Coupé, E. Duchesnay, A. Mihailov, A. Grigis, I. Bègue, J. Victor, V. Frouin, M. Leboyer, J. Houenou, and C. Laidi (2023) “Understanding the relationship between cerebellar structure and social abilities”, Molecular Autism
• C. Laidi, D. L. Floris, J. Tillmann, Y. Elandaloussi, M. Zabihi, T. Charman, T. Wolfers, S. Durston, C. Moessnang, F. Dell’Acqua, C. Ecker, E. Loth, D. Murphy, S. Baron-Cohen, J. K. Buitelaar, et al. (2022) “Cerebellar atypicalities in autism?”, Biological Psychiatry
• A. Iftimovici, B. Chaumette, E. Duchesnay, and M.-O. Krebs (2022) “Brain anomalies in early psychosis: From secondary to primary psychosis”, Neuroscience & Biobehavioral Reviews
• T. Fovet, P. Yger, R. Lopes, A. de Pierrefeu, E. Duchesnay, J. Houenou, P. Thomas, S. Szaffarczyk, P. Domenech, and R. Jardri (2021) “Decoding Activity in Broca’s Area Predicts the Occurrence of Auditory Hallucinations Across Subjects”, Biological Psychiatry
• B. Dufumier, A. Grigis, J. Victor, C. Ambroise, V. Frouin, and E. Duchesnay (2022) “OpenBHB: a Large-Scale Multi-Site Brain MRI Data-set for Age Prediction and Debiasing”, NeuroImage
• C. Piguet, A. Mihailov, A. Grigis, C. Laidi, E. Duchesnay, and J. Houenou (2021) “Irritability Is Associated With Decreased Cortical Surface Area and Anxiety With Decreased Gyrification During Brain Development”, Front Psychiatry
• T. Fovet, P. Yger, R. Lopes, A. de Pierrefeu, E. Duchesnay, J. Houenou, P. Thomas, S. Szaffarczyk, P. Domenech, and R. Jardri (2021) “Decoding Activity in Broca’s Area Predicts the Occurrence of Auditory Hallucinations Across Subjects”, Biol Psychiatry
• J. Stout, F. Hozer, A. Coste, F. Mauconduit, N. Djebrani-Oussedik, S. Sarrazin, J. Poupon, M. Meyrel, S. Romanzetti, B. Etain, C. Rabrait-Lerman, J. Houenou, F. Bellivier, E. Duchesnay, and F. Boumezbeur (2020) “Accumulation of Lithium in the Hippocampus of Patients With Bipolar Disorder: A Lithium-7 Magnetic Resonance Imaging Study at 7 Tesla”, Biol Psychiatry
• J.-F. Mangin, D. Rivière, E. Duchesnay, Y. Cointepas, V. Gaura, C. Verny, P. Damier, P. Krystkowiak, A.-C. Bachoud-Lévi, P. Hantraye, P. Remy, and G. Douaud (2020) “Neocortical morphometry in Huntington’s disease: Indication of the coexistence of abnormal neurodevelopmental and neurodegenerative processes”, Neuroimage Clin
• F. Hozer, S. Sarrazin, C. Laidi, P. Favre, M. Pauling, D. Cannon, C. McDonald, L. Emsell, J.-F. Mangin, E. Duchesnay, M. Bellani, P. Brambilla, M. Wessa, J. Linke, M. Polosan, et al. (2020) “Lithium prevents grey matter atrophy in patients with bipolar disorder: an international multicenter study”, Psychol Med
• L.-A. Claude, J. Houenou, E. Duchesnay, and P. Favre (2020) “Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions”, Bipolar Disord
• C. R. K. Ching, D. P. Hibar, T. P. Gurholt, A. Nunes, S. I. Thomopoulos, C. Abé, I. Agartz, R. M. Brouwer, D. M. Cannon, S. M. C. de Zwarte, L. T. Eyler, P. Favre, T. Hajek, U. K. Haukvik, J. Houenou, et al. (2020) “What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group”, Hum Brain Mapp
• J. Scott, D. Hidalgo-Mazzei, R. Strawbridge, A. Young, M. Resche-Rigon, B. Etain, O. A. Andreassen, M. Bauer, D. Bennabi, A. M. Blamire, F. Boumezbeur, P. Brambilla, N. Cattane, A. Cattaneo, M. Chupin, et al. (2019) “Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative”, Int J Bipolar Disord
• C. Laidi, J. Boisgontier, A. de Pierrefeu, E. Duchesnay, S. Hotier, M.-A. d’Albis, R. Delorme, F. Bolognani, C. Czech, C. Bouquet, A. Amestoy, J. Petit, Š. Holiga, J. Dukart, A. Gaman, et al. (2019) “Decreased Cortical Thickness in the Anterior Cingulate Cortex in Adults with Autism”, J Autism Dev Disord
• C. Laidi, T. Hajek, F. Spaniel, M. Kolenic, M.-A. d’Albis, S. Sarrazin, J.-F. Mangin, E. Duchesnay, P. Brambilla, M. Wessa, J. Linke, M. Polosan, P. Favre, A. L. Versace, M. L. Phillips, et al. (2019) “Cerebellar parcellation in schizophrenia and bipolar disorder”, Acta Psychiatr Scand
• P. Favre, M. Pauling, J. Stout, F. Hozer, S. Sarrazin, C. Abé, M. Alda, C. Alloza, S. Alonso-Lana, O. A. Andreassen, B. T. Baune, F. Benedetti, G. F. Busatto, E. J. Canales-Rodríguez, X. Caseras, et al. (2019) “Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals”, Neuropsychopharmacol.
• J. Bourgin, E. Duchesnay, E. Magaud, R. Gaillard, M. Kazes, and M.-O. Krebs (2019) “Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms”, Eur Child Adolesc Psychiatry
• A. Nunes, H. G. Schnack, C. R. K. Ching, I. Agartz, T. N. Akudjedu, M. Alda, D. Alnæs, S. Alonso-Lana, J. Bauer, B. T. Baune, E. Bøen, C. D. M. Bonnin, G. F. Busatto, E. J. Canales-Rodríguez, D. M. Cannon, et al. (2018) “Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group”, Mol. Psychiatry
• T. Löfstedt, V. Guillemot, V. Frouin, E. Duchesnay, and Hadj-Selem (2018) “Simulated Data for Linear Regression with Structured and Sparse Penalties: Introducing pylearn-simulate” Journal of Statistical Software
• J. Lebenberg, M. Labit, G. Auzias, H. Mohlberg, C. Fischer, D. Rivière, E. Duchesnay, C. Kabdebon, F. Leroy, N. Labra, F. Poupon, T. Dickscheid, L. Hertz-Pannier, C. Poupon, G. Dehaene-Lambertz, et al. (2018) “A framework based on sulcal constraints to align preterm, infant and adult human brain images acquired in vivo and post mortem”, Brain Struct Funct
• F. Hadj-Selem, T. Löfstedt, E. Dohmatob, V. Frouin, M. Dubois, V. Guillemot, and E. Duchesnay (2018) “Continuation of Nesterov’s Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging”, IEEE Transactions on Medical Imaging
• E. Duchesnay, F. Hadj Selem, F. De Guio, M. Dubois, J.-F. Mangin, M. Duering, S. Ropele, R. Schmidt, M. Dichgans, H. Chabriat, and E. Jouvent (2018) “Different Types of White Matter Hyperintensities in CADASIL”, Front. Neurol.
• A. de Pierrefeu, T. Lofstedt, F. Hadj-Selem, M. Dubois, R. Jardri, T. Fovet, P. Ciuciu, V. Frouin, and E. Duchesnay (2017) “Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty”, IEEE Transactions on Medical Imaging
• A. de Pierrefeu, T. Fovet, F. Hadj-Selem, T. Löfstedt, P. Ciuciu, S. Lefebvre, P. Thomas, R. Lopes, R. Jardri, and E. Duchesnay (2018) “Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity”, Hum. Brain Mapp.
• A. de Pierrefeu, T. Löfstedt, C. Laidi, F. Hadj‐Selem, J. Bourgin, T. Hajek, F. Spaniel, M. Kolenic, P. Ciuciu, N. Hamdani, M. Leboyer, T. Fovet, R. Jardri, J. Houenou, and E. Duchesnay (2018) “Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity”, Acta Psychiatrica Scandinavica
• R. Magalhães, J. Bourgin, F. Boumezbeur, P. Marques, M. Bottlaender, C. Poupon, B. Djemaï, E. Duchesnay, S. Mériaux, N. Sousa, T. M. Jay, and A. Cachia (2016) “White matter changes in microstructure associated with a maladaptive response to stress in rats”, Transl Psychiatry
• Y. Le Guen, G. Auzias, F. Leroy, M. Noulhiane, G. Dehaene-Lambertz, E. Duchesnay, J.-F. Mangin, O. Coulon, and V. Frouin (2017) “Genetic Influence on the Sulcal Pits: On the Origin of the First Cortical Folds”, Cerebral Cortex
• L. Pirpamer, E. Hofer, B. Gesierich, F. De Guio, P. Freudenberger, S. Seiler, M. Duering, E. Jouvent, E. Duchesnay, M. Dichgans, and others (2016) “Determinants of iron accumulation in the normal aging brain”, Neurobiology of aging
• E. Jouvent, Z. Y. Sun, F. De Guio, E. Duchesnay, M. Duering, S. Ropele, M. Dichgans, J.-F. Mangin, and H. Chabriat (2016) “Shape of the Central Sulcus and Disability After Subcortical Stroke: A Motor Reserve Hypothesis”, Stroke
• E. Jouvent, E. Duchesnay, F. Hadj-Selem, F. De Guio, J.-F. Mangin, D. Hervé, M. Duering, S. Ropele, R. Schmidt, M. Dichgans, and others (2016) “Prediction of 3-year clinical course in CADASIL”, Neurology
• B. Gesierich, E. Duchesnay, E. Jouvent, H. Chabriat, R. Schmidt, J.-F. Mangin, M. Duering, and M. Dichgans (2016) “Features and Determinants of Lacune Shape: Relationship With Fiber Tracts and Perforating Arteries”, Stroke
• E. Duchesnay, J. Bourgin, F. Hadj-Selem, and V. Frouin (2015) “Méthodes de prédiction multivariées basées sur la neuroimagerie : application aux maladies psychiatriques”, Annales Médico-psychologiques
• J. Bourgin, A. Cachia, F. Boumezbeur, B. Djemaï, M. Bottlaender, E. Duchesnay, S. Mériaux, and T. M. Jay (2015) “Hyper-responsivity to stress in rats is associated with a large increase in amygdala volume. A 7T MRI study”, Eur Neuropsychopharmacol
• S. Sarrazin, C. Poupon, J. Linke, M. Wessa, M. Phillips, M. Delavest, A. Versace, J. Almeida, P. Guevara, D. Duclap, E. Duchesnay, J.-F. Mangin, K. Le Dudal, C. Daban, N. Hamdani, et al. (2013) “A multicenter tractography study of deep white matter tracts in bipolar I disorder: psychotic features and interhemispheric disconnectivity”, JAMA Psychiatry
• M. Duering, B. Gesierich, S. Seiler, L. Pirpamer, M. Gonik, E. Hofer, E. Jouvent, E. Duchesnay, H. Chabriat, S. Ropele, R. Schmidt, and M. Dichgans (2014) “Strategic white matter tracts for processing speed deficits in age-related small vessel disease”, Neurology
• F. De Guio, A. Vignaud, S. Ropele, M. Duering, E. Duchesnay, H. Chabriat, and E. Jouvent (2014) “Loss of Venous Integrity in Cerebral Small Vessel Disease: A 7-T MRI Study in Cerebral Autosomal-Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy (CADASIL)”, Stroke
• F. De Guio, S. Reyes, A. Vignaud, M. Duering, S. Ropele, E. Duchesnay, H. Chabriat, and E. Jouvent (2014) “In Vivo High-Resolution 7 Tesla MRI Shows Early and Diffuse Cortical Alterations in CADASIL”, PLoS ONE
• J. Bourgin and E. Duchesnay (2013) “Phénotypes cliniques précoces et recherche de biomarqueurs stratégiques : les fondements d’une psychiatrie personnalisée”, L’information psychiatrique
• P. Reiner, E. Jouvent, E. Duchesnay, R. Cuingnet, J.-F. Mangin, H. Chabriat, and Alzheimer’s Disease Neuroimaging Initiative (2012) “Sulcal span in Azheimer’s disease, amnestic mild cognitive impairment, and healthy controls”, J. Alzheimers Dis.
• F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay (2012) “Scikit-learn: Machine Learning in Python”, JMLR
• C. Leroy, L. Karila, J.-L. Martinot, M. Lukasiewicz, E. Duchesnay, C. Comtat, F. Dollé, A. Benyamina, E. Artiges, M.-J. Ribeiro, M. Reynaud, and C. Trichard (2011) “Striatal and extrastriatal dopamine transporter in cannabis and tobacco addiction: a high-resolution PET study”, Addict Biol
• E. Le Floch, V. Guillemot, V. Frouin, P. Pinel, C. Lalanne, L. Trinchera, A. Tenenhaus, A. Moreno, M. Zilbovicius, T. Bourgeron, S. Dehaene, B. Thirion, J.-B. Poline, and E. Duchesnay (2012) “Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares”, Neuroimage
• E. Jouvent, J.-F. Mangin, E. Duchesnay, R. Porcher, M. Düring, Y. Mewald, J.-P. Guichard, D. Hervé, S. Reyes, N. Zieren, M. Dichgans, and H. Chabriat (2011) “Longitudinal changes of cortical morphology in CADASIL”, Neurobiol. Aging
• E. Duchesnay, A. Cachia, N. Boddaert, N. Chabane, J.-F. Mangin, J.-L. Martinot, F. Brunelle, and M. Zilbovicius (2011) “Feature selection and classification of imbalanced datasets: Application to PET images of children with autistic spectrum disorders”, NeuroImage
• J. Penttilä, M.-L. Paillère-Martinot, J.-L. Martinot, D. Ringuenet, M. Wessa, J. Houenou, T. Gallarda, F. Bellivier, A. Galinowski, P. Bruguière, F. Pinabel, M. Leboyer, J.-P. Olié, E. Duchesnay, E. Artiges, et al. (2009) “Cortical folding in patients with bipolar disorder or unipolar depression”, J Psychiatry Neurosci
• M. Fouquet, B. Desgranges, B. Landeau, E. Duchesnay, F. Mézenge, V. de la Sayette, F. Viader, J.-C. Baron, F. Eustache, and G. Chételat (2009) “Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer’s disease”, Brain
• R. Maroy, R. Boisgard, C. Comtat, V. Frouin, P. Cathier, E. Duchesnay, F. Dollé, P. E. Nielsen, R. Trébossen, and B. Tavitian (2007) “Segmentation of rodent whole-body dynamic PET images: an unsupervised method based on voxel dynamics”, IEEE Trans Med Imaging
• J. Dubois, G. Dehaene-Lambertz, M. Perrin, J.-F. Mangin, Y. Cointepas, E. Duchesnay, D. Le Bihan, and L. Hertz-Pannier (2036) “Asynchrony of the early maturation of white matter bundles in healthy infants: quantitative landmarks revealed noninvasively by diffusion tensor imaging”, Hum Brain Mapp
• A. Dubois, A.-S. Hérard, G. Flandin, E. Duchesnay, L. Besret, V. Frouin, P. Hantraye, G. Bonvento, and T. Delzescaux (2007) “Quantitative validation of voxel-wise statistical analyses of autoradiographic rat brain volumes: application to unilateral visual stimulation”, Neuroimage
• A. Cachia, M.-L. Paillère-Martinot, A. Galinowski, D. Januel, R. de Beaurepaire, F. Bellivier, E. Artiges, J. Andoh, D. Bartrés-Faz, E. Duchesnay, D. Rivière, M. Plaze, J.-F. Mangin, and J.-L. Martinot (2007) “Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations”, Neuroimage
• E. Duchesnay, A. Cachia, A. Roche, D. Rivière, Y. Cointepas, D. Papadopoulos-Orfanos, M. Zilbovicius, J.-L. Martinot, J. Régis, and J.-F. Mangin (2007) “Classification based on cortical folding patterns”, IEEE Trans Med Imaging
• A. Dubois, J. Dauguet, A.-S. Herard, L. Besret, E. Duchesnay, V. Frouin, P. Hantraye, G. Bonvento, and T. Delzescaux (2007) “Automated three-dimensional analysis of histological and autoradiographic rat brain sections: application to an activation study”, J. Cereb. Blood Flow Metab.
• B. Thirion, E. Duchesnay, E. Hubbard, J. Dubois, J.-B. Poline, D. Lebihan, and S. Dehaene (2006) “Inverse retinotopy: inferring the visual content of images from brain activation patterns”, Neuroimage
• J.-F. Mangin, D. Rivière, A. Cachia, E. Duchesnay, Y. Cointepas, D. Papadopoulos-Orfanos, P. Scifo, T. Ochiai, F. Brunelle, and J. Régis (2004) “A framework to study the cortical folding patterns”, Neuroimage
• J.-F. Mangin, F. Poupon, E. Duchesnay, D. Rivière, A. Cachia, D. L. Collins, A. C. Evans, and J. Régis (2004) “Brain morphometry using 3D moment invariants”, Med Image Anal
• J. F. Mangin, D. Rivière, A. Cachia, E. Duchesnay, Y. Cointepas, D. Papadopoulos-Orfanos, D. L. Collins, A. C. Evans, and J. Régis (2004) “Object-based morphometry of the cerebral cortex”, IEEE Trans Med Imaging
• E. Duchesnay, J.-J. Montois, and Y. Jacquelet (2003) “Cooperative agents society organized as an irregular pyramid: A mammography segmentation application”, Pattern Recognition Letters

Conferences

• R. Louiset, E. Duchesnay, B. Dufumier, A. Grigis, and P. Gori (2024) “SepCLR: Separating common from salient patterns with Contrastive Representation Learning”, in The Twelfth International Conference on Learning Representations (ICLR) 2024
• R. Louiset, E. Duchesnay, A. Grigis, B. Dufumier, and P. Gori (2023) “SepVAE: a contrastive VAE to separate pathological patterns from healthy ones”, presented at the Workshop on Interpretable ML in Healthcare at International Conference on Machine Learning (ICML) 2023
• B. Dufumier, C. A. Barbano, R. Louiset, E. Duchesnay, and P. Gori (2024) “Integrating Prior Knowledge in Contrastive Learning with Kernel”, in 40th International Conference on Machine Learning (ICML) 2023
• C. A. Barbano, B. Dufumier, E. Duchesnay, M. Grangetto, and P. Gori (2023) “Contrastive learning for regression in multi-site brain age prediction”, in IEEE 20th International Symposium on Biomedical Imaging (ISBI) 2023
• C. Ambroise, A. Grigis, E. Duchesnay, and V. Frouin (2023) “Multi-View Variational Autoencoders Allow for Interpretability Leveraging Digital Avatars: Application to the HBN Cohort”, in IEEE 20th International Symposium on Biomedical Imaging (ISBI) 2023
• R. Louiset, P. Gori, B. Dufumier, J. Houenou, A. Grigis, and E. Duchesnay (2022) “UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) 2021
• B. Dufumier, P. Gori, J. Victor, A. Grigis, M. Wessa, P. Brambilla, P. Favre, M. Polosan, C. McDonald, C. M. Piguet, and E. Duchesnay (2021) “Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification”, in 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021
• B. Dufumier, P. Gori, J. Victor, A. Grigis, and E. Duchesnay (2022) “Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels”, presented at the Med-NeurIPS - Workshop NeurIPS
• A. de Pierrefeu, T. Lofstedt, C. Laidi, F. Hadj-Selem, M. Leboyer, P. Ciuciu, J. Houenou, and E. Duchesnay (2018) “Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity”, presented at the International Workshop on Pattern Recognition in Neuroimaging (PRNI) 2018
• M. Pauling, C. Henry, S. Sarrazin, E. Duchesnay, J. Stout, D. Hibar, N. Jahanshad, O. Andreassen, P. Thompson, and J. Houenou (2017) “Bipolar disorder and white matter microstructure: ENIGMA bipolar disorder fractional anisotropy DTI results”, in European Neuropsychopharmacology
• Y. L. Guen, G. Auzias, G. Dehaene-Lambertz, F. Leroy, J. F. Mangin, E. Duchesnay, O. Coulon, and V. Frouin (2017) “Regional study of the genetic influence on the sulcal pits”, in IEEE 14th International Symposium on Biomedical Imaging (ISBI) 2017
• A. Teillac, S. Lefrance, E. Duchesnay, F. Poupon, M. A. R. Fuster, D. L. Bihan, J.-F. Mangin, and C. Poupon (2016) “Colocalization of Functional Activity and Neurite Density Within Cortical Areas”, in Computational Diffusion MRI
• T. Lofstedt, F. Hadj-Selem, V. Guillemot, C. Philippe, E. Duchesnay, A. Tenenhaus, and V. Frouin (2014) “Analysis, Structured variable selection for generalized canonical correlation”, in International Conference on Partial Least Squares and Related Methods
• M. Dubois, F. Hadj-Selem, T. Löfstedt, M. Perrot, C. Fischer, V. Frouin, and E. Duchesnay (2014) “Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI”, presented at the International Workshop on Pattern Recognition in Neuroimaging (PRNI) 2014
• B. Da Mota, J. LI, M. Cadenne, V. Ducrot, S. Monnot, and V. Frouin (2014) “Permuted Voxelwise Genome-Wide Association”, presented at the Human Brain Mapping
• B. Da Mota and E. Duchesnay (2014) “Le calcul haute performance pour l’analyse de données de neuroimagerie-génétique en grandes dimensions”, presented at the Forum TERATEC
• J. LI, E. Duchesnay, M. Duboism, L. Hugo, and B. D. Mota (2013) “Embarrassingly Parallel Array Computing (EPAC)”, presented at the NIPS Workshop on Machine Learning Open Source Software (MLOSS 2013): Towards Open Workflows
• E. Le Floch, L. Trinchera, V. Guillemot, A. Tenenhaus, J.-B. Poline, V. Frouin, and E. Duchesnay (2013) “Dimension Reduction and Regularization Combined with Partial Least Squares in High Dimensional Imaging Genetics Studies”, in New Perspectives in Partial Least Squares and Related Methods
• V. Guillemot, A. Tenenhaus, E. Duchesnay, J.-B. Poline, and V. Frouin (2013) “A multi-block approach in imaging genetics”, in 9th International Imaging Genetics Conference
• A.-L. Fouque, C. Fischer, V. Frouin, P. Ciuciu, and E. Duchesnay (2013) “Comparison of Features for Voxel-Based Analysis and Classification of Anatomical Neuroimaging Data”, in 2013 International Workshop on Pattern Recognition in Neuroimaging
• E. Le Floch, P. Pinel, A. Tenenhaus, L. Trinchera, J.-B. Poline, V. Frouin, and E. Duchesnay (2012) “Discovering associations in high dimensional imaging-genetics data: A comparison study of dimension reduction and regularisation strategies combined with partial least squares”, in ISBI
• E. Le Floch, C. Lalanne, P. Pinel, A. Moreno, L. Trinchera, A. Tenenhaus, B. Thirion, J.-B. Poline, V. Frouin, and E. Duchesnay (2011) “Bridging the gap between imaging and genetics : a multivariate statistical investigation”, presented at the Human brain mapping
• A.-L. Fouque, P. Fillard, A. Bargiacchi, A. Cachia, M. Zilbovicius, B. Thyreau, E. Le Floch, P. Ciuciu, and E. Duchesnay (2011) “Voxelwise multivariate statistics and brain-wide machine learning using the full diffusion tensor”, in MICCAI
• C. Damon, E. Duchesnay, and M. Depecker (2011) “Structured Multivariate Pattern Classification to Detect MRI Markers for an Early Diagnosis of Alzheimer’s Disease”, in 2011 10th International Conference on Machine Learning and Applications and Workshops
• J.-B. Poline, C. Lalanne, A. Tenenhaus, E. Duchesnay, B. Thirion, and V. Frouin (2010) “Imaging Genetics: Bio-Informatics and Bio-Statistics Challenges”, in compstat
• E. Le Floch, M. Keller, V. Frouin, C. Lalanne, P. Pinel, J.-B. Poline, and E. Duchesnay (2010) “Cluster-level Inference and Resampling-Based Multiple Testing applied to Imaging Genetics Studies”, presented at the Human brain mapping
• Z. Y. Sun, D. Riviere, E. Duchesnay, B. Thirion, F. Poupon, and J.-F. Mangin (2008) “Defining cortical sulcus patterns using partial clustering based on bootstrap and bagging”, in 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
• C. Damon, P. Pinel, M. Perrot, V. Michel, E. Duchesnay, J.-B. Poline, and B. Thirion (2008) “Discriminating between populations of subjects based on FMRI data using sparse features selection and SRDA classifier”, in MICCAI Analysis of Functional Medical Images Workshop
• B. Thirion, E. Duchesnay, E. Hubbard, J. Dubois, J.-B. Poline, and S. Dehaene (2006) “Reading the brain visual system as an inverse problem”, in ISBI
• B. Thirion, E. Duchesnay, J. Dubois, J.-B. Poline, and D. Le Bihan (2005) “Reading in the subject’s mind: the case of low-level vision”, in Proc. 11th HBM CD-Rom Neuroimage vol 26 (1)
• J.-L. Martinot, T. Kircher, M.-L. Paillère-Martinot, M. Plaze, D. Januel, F. Bellivier, J. Andoh, R. De Beaurepaire, S. Chanraud, E. Artiges, E. Duchesnay, J.-F. Mangin, T. Kellermann, D. Leube, D. Bartrés-Faz, et al. (2005) “Brain folding in schizophrenia”, in Proc. 8th World congress of biological psychiatry. The world journal of biological psychiatry vol 6 (1)
• A. Cachia, E. Duchesnay, M. Plaze, M.-L. Paillère-Martinot, D. Januel, F. Bellivier, A. Galinowski, J. Andoh, D. Bartrés-Faz, R. De Beaurepaire, E. Artiges, D. Rivière, J.-F. Mangin, and J.-L. Martinot (2005) “Sulcus-based morphometry of heteromodal cortex in schizophrenia patients with resistant auditory hallucinations.”, in Proc. 11th HBM CD-Rom Neuroimage vol 26 (1)
• E. Duchesnay, A. Roche, D. Rivière, D. Papadopoulos-Orfanos, Y. Cointepas, and J.-F. Mangin (2004) “Population Classification Based on Structural Morphometry of Cortical Sulci”, in IEEE ISBI
• E. Duchesnay, A. Roche, D. Rivière, D. Papadopoulos-Orfanos, Y. Cointepas, and J.-F. Mangin (2004) “Guessing the Sex from the Shapes of Cortical Folds”, in HBM
• Y. Jacquelet, J. Montois, E. Duchesnay, and A. Kinic (2002) “Combinatorial pyramid transposed to behavioural space for object recognition process”, in Systems
• A. Kinie, E. Duchesnay, F. Wendling, J.-J. Montois, and Y. Jacquelet (2001) “Segmentation vectorielle des signaux épileptiques une approche expérimentale multi-agents”, in 18° Colloque sur le traitement du signal et des images
• Y. Jacquelet, J.-J. Montois, E. Duchesnay, M. GARREAU, and A. KINIE (2001) “Architecture pyramidale agent pour la segmentation d’image: Application à l’extraction d’une zone lobulaire issue d’une mammographie”, in 18° Colloque sur le traitement du signal et des images
• E. Duchesnay, J.-J. Montois, Y. Jacquelet, and A. Kinie (2001) “An agent-based implementation of irregular pyramid for distributed image segmentation”, in Emerging Technologies and Factory Automation
• E. Duchesnay, J.-J. Montois, and Y. Jacquelet (2000) “A Multiagent System for a Cooperative and Distributed Vision System”, in Advances in Concurrent Engineering: CE2000 Proceedings
• E. Duchesnay, J. J. Montois, and Y. Jacquelet (2000) “Résolution distribuée de conflits dans un réseau d’agents”, in 10ème Journées Neurosciences et Sciences de l’Ingénieur
• E. Duchesnay, J. J. Montois, Y. Jacquelet, and A. Kinie (2000) “Irregular adaptative pyramid of agents for segmentation to interpretation of image”, in 2000 IEEE International Conference on Systems