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Olivier Colliot

Research Director, CNRS
Co-Head of the ARAMIS Lab
Chair, Paris Artificial Intelligence Research Institute (PRAIRIE)


Book Cover

Open Access Book

Machine Learning for Brain Disorders 

Click here to download the full PDF or here for individual chapters

Part I - Machine learning fundamentals
Part II - Data
Part III - Methodologies
Part IV - Validation and datasets
Part V - Disorders

Biography

I am Research Director (equivalent to Full Professor) at CNRS (Department of Computer Science) and the founding co-director of the ARAMIS Lab, a joint team between CNRS, Inria, Inserm and Sorbonne University at the Paris Brain Institute (ICM). I hold a Chair at the Paris Artificial Intelligence Research Institute.

Founded in 2012, the ARAMIS Lab, gathering about 35 people, is focused on the design of advanced computational and mathematical approaches to study brain diseases.

Research interests

  • Machine learning
    Medical image analysis
    Applications to brain disorders

To contact me

  • Email: olivier.colliot@cnrs.fr
    Phone: +33 1 57 27 43 65
  • Want to join?
    If you are interested in joining the team, do not hesitate to send me an e-mail stating your field of interest and a CV.
  • To find the lab:
    follow the instructions here.

Editorial activities

Teaching

Software

  • Clinica
    An open source software platform for clinical neuroscience studies
  • Clinica-DL
    Deep learning for brain imaging made easy

Publications

Selected publications - Machine learning and image analysis

  • Christodoulou E*, Reinke A*, Houhou R, Kalinowski P, Erkan S, Sudre CH, Burgos N, Boutaj S, Loizillon S, Solal M, Rieke N, Cheplygina V, Antonelli M, Mayer LD, Tizabi MD, Cardoso MJ, Simpson A, Jäger PF, Kopp-Schneider A, Varoquaux G*, Colliot O*, and Maier-Hein L*, Confidence intervals uncovered: Are we ready for real-world medical imaging AI? In Proc. Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, Marrakech, Morocco, 2024.
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  • Bottani S, Burgos N, Maire A, Wild A, Ströer S, Dormont D, and Colliot O, Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse, Medical Image Analysis, 75, 102903, 2023.
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  • Kmetzsch V, Becker E, Saracino D, Rinaldi D, Camuzat A, Le Ber I, and Colliot O, Disease progression score estimation from multimodal imaging and microRNA data using supervised variational autoencoders, IEEE Journal of Biomedical and Health Informatics, 26(12):6024-6035, 2022.
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  • Lu P and Colliot O. Multilevel Survival Modeling with Structured Penalties for Disease Prediction from Imaging Genetics data. IEEE Journal of Biomedical and Health Informatics, 26(2):798–808, 2022.
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  • Bottani S, Burgos N, Maire, A, Wild A, Stroer, S, Dormont D and Colliot O. Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse. Medical Image Analysis, 75:102219, 2022.
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  • Thibeau-Sutre E, Díaz M, Hassanaly R, Routier A, Dormont D, Colliot O, and Burgos N, ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing, Computer Methods and Programs in Biomedicine, 220, 106818, 2022.
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  • Burgos N, Cardoso MJ, Samper-Gonzalez J, Habert MO, Durrleman S, Ourselin S, and Colliot O. Anomaly detection for the individual analysis of brain PET images. SPIE Journal of Medical Imaging, 8(2):024003, 2021.
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  • Guyot A, Fouquier ABG, Gerardin E, Chupin M, Glaunes JA, Marrakchi-Kacem L, Germain J, Boutet C, Cury C, Hertz-Pannier L, Vignaud A, Durrleman S, Henry TR, van de Moortele PF, Trouve A, and Colliot O. A Diffeomorphic Vector Field Approach to Analyze the Thickness of the Hippocampus From 7 T MRI. IEEE Transactions on Biomedical Engineering, 68(2):393-403, 2021.
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  • Vanderbecq Q, Xu E, Stroer S, Couvy-Duchesne B, Diaz Melo M, Dormont D, and Colliot O. Comparison and validation of seven white matter hyperintensities segmentation software in elderly patients. NeuroImage: Clinical, 27:102357, 2020.
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  • Wen J*, Thibeau-Sutre E*, Diaz-Melo M, Samper-Gonzalez J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, and Colliot O. Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Medical Image Analysis, 63:101694, 2020.
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  • Bône A, Colliot O, and Durrleman S, Learning the spatiotemporal variability in longitudinal shape data sets. International Journal of Computer Vision, 128: 2873–2896, 2020.
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  • Wei W, Poirion E, Bodini B, Durrleman S, Ayache N, Stankoff B, and Colliot O. Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis. Medical Image Analysis, 58:101546, 2019.
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  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert M-O, Durrleman S, Evgeniou T, and Colliot O, Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data. NeuroImage, 183:504-521, 2018.
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  • Schiratti JB, Allassonnière S, Colliot O and Durrleman, S. A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations. The Journal of Machine Learning Research, 18(133), 1-33, 2017.
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  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, and Durrleman S, Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35:12, 2609–2619, 2016.
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  • Cury C, Glaunès JA, and Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, Springer, 2014.
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  • Cuingnet R, Glaunès JA, Chupin M, Benali H, and Colliot O, Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
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  • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, and Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome. Medical Image Analysis, 15(5):729-37, 2011.
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  • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, and Colliot O, Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage, 56(2):766-81, 2011.
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  • Gerardin E, Chételat G, Chupin M, Cuingnet R, Desgranges B, Kim HS, Niethammer M, Dubois B, Garnero L, Lehéricy S, Eustache F, and Colliot O, The ADNI, Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging. NeuroImage, 47 (4):1476-86, 2009.
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Selected publications - Books, book chapters and reviews

  • Colliot O (editor), Machine Learning for Brain Disorders, Springer, 2023.
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  • Colliot O, Thibeau-Sutre E, and Burgos N, Reproducibility in machine learning for medical imaging, In Machine Learning for Brain Disorders, O. Colliot (editor), Springer, 2023.
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  • Varoquaux G, and Colliot O, Evaluating machine learning models and their diagnostic value, In Machine Learning for Brain Disorders, O. Colliot (editor), Springer, 2023.
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  • Colliot O, A non-technical introduction to machine learning, In Machine Learning for Brain Disorders, O. Colliot (editor), Springer, 2023.
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  • Thibeau-Sutre E, Collin S, Burgos N, and Colliot O, Interpretability of Machine Learning Methods Applied to Neuroimaging, In Machine Learning for Brain Disorders, O. Colliot (editor), Springer, 2023.
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  • Faouzi J, and Colliot O, Classic machine learning algorithms, In Machine Learning for Brain Disorders, O. Colliot (editor), Springer, 2023.
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  • Vakalopoulou M, Christodoulidis S, Burgos N, Colliot O, and Lepetit V, Deep learning: basics and convolutional neural networks, In Machine Learning for Brain Disorders, O. Colliot (editor), Springer,  2023.
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  • Burgos N, Bottani S, Faouzi J, Thibeau-Sutre E, and Colliot O. Deep learning for brain disorders: from data processing to disease treatment. Briefings in Bioinformatics, 22(2):1560-1576, 2021.
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  • Burgos N, and Colliot O. Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges. Current Opinion in Neurology, 33(4):439-450, 2020
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Selected publications - Medicine and neuroscience

  • de Matos K, Cury C, Chougar L, Strike LT, Rolland T, Riche M, Hemforth L, Martin A, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Papadopoulos Orfanos D, Lemaitre H, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Frouin V; IMAGEN Consortium; Bach Cuadra M, Colliot O and Couvy-Duchesne B, Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability, Brain Structure and Function, 2023.
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  • Kmetzsch V, Anquetil V, Saracino D, Rinaldi D, Camuzat A, Gareau T, Jornea L, Forlani S, Couratier P, Wallon D, Pasquier F, Robil N, de la Grange P, Moszer I, Le Ber I, Colliot O, and Becker E, Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry, 92(5):485-493, 2021.
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  • Cury C, Scelsi MA, Toro R, Frouin V, Artiges E, Grigis A, Heinz A, Lemaitre H, Martinot JL, Poline JB, Smolka MN, Walter H, Schumann G, Altmann A, and Colliot O, Genome wide association study of incomplete hippocampal inversion in adolescents. PLoS One, 15(1):e0227355. 2020.
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  • Morin A, Samper-Gonzalez J, Bertrand A, Stroer S, Dormont D, Mendes A, Coupé P, Ahdidan J, Levy M, Samri D, Hampel H, Dubois B, Teichmann M, Epelbaum S, and Colliot O, Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort. Journal of Alzheimer's disease, 74(4):1157-1166, 2020.
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  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Houot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, and Bertrand A, Neurite density is reduced in the presymptomatic phase of C9orf72 disease. J Neurol Neurosurg Psychiatry, 2019.
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  • Bertrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert M-O, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O, and Le Ber I, Early cognitive, structural and microstructural changes in c9orf72 presymptomatic carriers before 40 years of age. JAMA Neurology, 75(2):236-245, 2018.
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  • Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, and Hampel H, Cognitive and neuroimaging features and brain beta-amyloidosis in individuals at risk of Alzheimer's disease (INSIGHT-preAD): a longitudinal observational study. Lancet Neurology, 17(4):335-346, 2018.
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  • Jacquemont T, De Vico Fallani F, Bertrand A, Epelbaum S, Routier A, Dubois B, Hampel H, Durrleman S, and Colliot O, Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, 2017.
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  • Cury C, Toro R, Cohen F, Fischer C, Mhaya A, Samper-González J, Hasboun D, Mangin J-F, Banaschewski T, Bokde ALW, Bromberg U, Buechel C, Cattrell A, Conrod P, Flor H, Gallinat J, Garavan H, Gowland P, Heinz A, Ittermann B, Lemaitre H, Martinot J-L, Nees F, Paillère Martinot M-L, Orfanos DP, Paus T, Poustka L, Smolka MN, Walter H, Whelan R, Frouin V, Schumann G, Glaunès JA, and Colliot O, Incomplete Hippocampal Inversion: a comprehensive MRI study of over 2000 subjects. Frontiers in Neuroanatomy, 9:160, 2015.
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  • Dubois B, Chupin M, Hampel H, Croisile B, Louis-Tisserand G, Touchon J, Bonafe A, Ousset, PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, and Dormont D, The Hippocampus Study Group, Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal AD. Alzheimer's and Dementia, 11(9):1041-9, 2015.
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  • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, and Colliot O, Detection of volume loss in hippocampal layers in Alzheimer's disease using 7T MRI: a feasibility study. NeuroImage: Clinical Jul 31;5:341-8, 2014.
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  • Cruz de Souza L, Chupin M, Bertoux M, Lehéricy S, Dubois B, Lamari F, Le Ber I, Jardel C, Bottlaender M, Colliot O, and Sarazin M, Is hippocampal volume a good marker to differentiate Alzheimer disease from frontotemporal dementia? Journal of Alzheimer's Disease , 36(1):57-66, 2013.
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  • Worbe Y, Gerardin E, Hartmann A, Valabregue R, Chupin M, Tremblay L, Vidailhet M, Colliot O, and Lehéricy S, Distinct structural changes underpin the clinical phenotypes in adult patients with Gilles de la Tourette syndrome. Brain, 133(Pt 12):3649-60, 2010.
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  • Kim H, Bernasconi N, Bernhardt B, Colliot O, and Bernasconi A, Basal temporal sulcal morphology in healthy controls and patients with temporal lobe epilepsy. Neurology, 70(22 Pt 2), 2159-2165, 2008.
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  • Colliot O, Chételat G, Chupin M, Desgranges B, Magnin B, Benali H, Dubois B, Garnero L, Eustache F, and Lehéricy S, Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology, 7248(1):194-201, 2008.
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CNRS / Inria / Inserm / Sorbonne Université / Institut du Cerveau / PRAIRIE / ARAMIS Lab

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