Brainomics project references

Selected publications of the Brainomics group

  1. Le Guen, Y., Amalric, M., Pinel, P., Pallier, C., & Frouin, V. (2018). Shared genetic aetiology between cognitive performance and brain activations in language and math tasks. Scientific Reports, 8(1), 17624. https://doi.org/10.1038/s41598-018-35665-0
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  2. Le Guen, Y., Philippe, C., Riviere, D., Lemaitre, H., Grigis, A., Fischer, C., … Frouin, V. (2018). eQTL of KCNK2 regionally influences the brain sulcal widening: evidence from 15,597 UK Biobank participants with neuroimaging data. Brain Structure & Function. https://doi.org/10.1007/s00429-018-1808-9
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  3. Goya-Outi, J., Orlhac, F., Calmon, R., Alentorn, A., Nioche, C., Philippe, C., … Frouin, F. (2018). Computation of reliable textural indices from multimodal brain MRI: suggestions based on a study of patients with diffuse intrinsic pontine glioma. Physics in Medicine and Biology, 63(10), 105003–105003. https://doi.org/10.1088/1361-6560/aabd21
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  4. Orlhac, F., Boughdad, S., Philippe, C., Stalla-Bourdillon, H., Nioche, C., Champion, L., … Buvat, I. (2018). A post-reconstruction harmonization method for multicenter radiomic studies in PET. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine. https://doi.org/10.2967/jnumed.117.199935
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  5. Hadj-Selem, F., Lofstedt, T., Dohmatob, E., Frouin, V., Dubois, M., Guillemot, V., & Duchesnay, E. (2018). Continuation of Nesterov’s Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging. IEEE Transactions on Medical Imaging, 1–1. https://doi.org/10.1109/TMI.2018.2829802
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  6. Le Guen, Y., Leroy, F., Auzias, G., Riviere, D., Grigis, A., Mangin, J. F., … Frouin, V. (2018). The chaotic morphology of the left superior temporal sulcus is genetically constrained. NeuroImage, 174, 297–307. https://doi.org/10.1016/j.neuroimage.2018.03.046
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  7. Charman, T., Loth, E., Tillmann, J., Crawley, D., Wooldridge, C., Goyard, D., … Buitelaar, J. K. (2017). The EU-AIMS Longitunal European Autism Project (LEAP): clinical characterisation. Molecular Autism, 8, 27.
  8. Loth, E., Charman, T., Mason, L., Tillmann, J., Jones, E. J. H., Wooldridge, C., … Buitelaar, J. K. (2017). The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders. Molecular Autism, 8, 24. https://doi.org/10.1186/s13229-017-0146-8
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  9. Le Guen, Y., Auzias, G., Leroy, F., Noulhiane, M., Dehaene-Lambertz, G., Duchesnay, E., … Frouin, V. (2017). Genetic Influence on the Sulcal Pits: On the Origin of the First Cortical Folds. Cerebral Cortex, 1–12. https://doi.org/10.1093/cercor/bhx098
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  10. Grigis, A., Goyard, D., Cherbonnier, R., Gareau, T., Orfanos, D. P., Chauvat, N., … Frouin, V. (2017). Neuroimaging, Genetics, and Clinical Data Sharing in Python Using the CubicWeb Framework. FRONTIERS IN NEUROINFORMATICS, 11, Article Number: 18. https://doi.org/DOI: 10.3389/fninf.2017.00018
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  11. Grigis, A., Goyard, D., Cherbonnier, R., Gareau, T., Papadopoulos Orfanos, D., Chauvat, N., … Frouin, V. (2017). Neuroimaging, genetics, and clinical data sharing in python using the CubicWeb framework. Frontiers in Neuroinformatics, 11. https://doi.org/10.3389/fninf.2017.00018
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  12. Le Guen, Y., Auzias, G., Leroy, F., Noulhiane, M., Dehaene-Lambertz, G., Duchesnay, E., … Frouin, V. (2017). Genetic Influence on the Sulcal Pits: On the Origin of the First Cortical Folds. Cerebral Cortex, 1–12. Retrieved from https://academic.oup.com/cercor/article/3746075
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  13. Loth, E., Charman, T., Mason, L., Tillmann, J., Jones, E. J. H., Wooldridge, C., … Buitelaar, J. K. (2017). The EU-AIMS Longitudinal European Autism Project (LEAP): Design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders. Molecular Autism, 8(1). https://doi.org/10.1186/s13229-017-0146-8
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  14. Papadopoulos Orfanos, D., Michel, V., Schwartz, Y., Pinel, P., Moreno, A., Le Bihan, D., & Frouin, V. (2017). The Brainomics/Localizer database. NeuroImage, 144. https://doi.org/10.1016/j.neuroimage.2015.09.052
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  15. Operto, G., Chupin, M., Batrancourt, B., Habert, M.-O., Colliot, O., Benali, H., … Cointepas, Y. (2016). CATI: A Large Distributed Infrastructure for the Neuroimaging of Cohorts. Neuroinformatics, 14(3). https://doi.org/10.1007/s12021-016-9295-8
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  16. Operto, G., Chupin, M., Batrancourt, B., Habert, M.-O., Colliot, O., Benali, H., … CATI Consortium. (2016). CATI: A Large Distributed Infrastructure for the Neuroimaging of Cohorts. Neuroinformatics, 14(3), 253–264. https://doi.org/10.1007/s12021-016-9295-8
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  17. Cury, C., Toro, R., Cohen, F., Fischer, C., Mhaya, A., Samper-Gonzalez, J., … Colliot, O. (2015). Incomplete Hippocampal Inversion: A Comprehensive MRI Study of Over 2000 Subjects. FRONTIERS IN NEUROANATOMY, 9, 160. https://doi.org/10.3389/fnana.2015.00160
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  18. Papadopoulos Orfanos, D., Michel, V., Schwartz, Y., Pinel, P., Moreno, A., Le Bihan, D., & Frouin, V. (2015). The Brainomics/Localizer database. Neuroimage, 15, 00874–5.
  19. Cury, C., Toro, R., Cohen, F., Fischer, C., Mhaya, A., Samper-González, J., … Colliot, O. (2015). Incomplete hippocampal inversion: A comprehensive MRI study of over 2000 subjects. Frontiers in Neuroanatomy, 9(DEC). https://doi.org/10.3389/fnana.2015.00160
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  20. Duchesnay, E., Bourgin, J., Hadj-Selem, F., & Frouin, V. (2015). Multivariate prediction methods based on neuroimaging: Application to psychiatric disorders. Annales Medico-Psychologiques, 173(3). https://doi.org/10.1016/j.amp.2015.03.002
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  21. Fritsch, V., Da Mota, B., Loth, E., Varoquaux, G., Banaschewski, T., Barker, G. J., … Thirion, B. (2015). Robust regression for large-scale neuroimaging studies. NeuroImage, 111. https://doi.org/10.1016/j.neuroimage.2015.02.048
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  22. Tenenhaus, A., Philippe, C., & Frouin, V. (2015). Kernel Generalized Canonical Correlation Analysis. Computational Statistics and Data Analysis, 90. https://doi.org/10.1016/j.csda.2015.04.004
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  23. Toro, R., Poline, J. B., Huguet, G., Loth, E., Frouin, V., Banaschewski, T., … Bourgeron, T. (2015). Genomic architecture of human neuroanatomical diversity. Molecular Psychiatry, 20(8), 1011–1016. https://doi.org/10.1038/mp.2014.99
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