The following scientific publications make reference to the IBC project because they describe the IBC dataset or use its data/protocols or investigate a related research topic.


  • Pinho, A.L. et al. (2020). Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping. Sci Data 7, 353. DOI: 10.1038/s41597-020-00670-4
  • Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020). Explainable machine learning for scientific insights and discoveries. IEEE Access, 8, 42200-42216.
  • Poppenk, J. (2020). Anatomically guided examination of extrinsic connectivity gradients in the human hippocampus. Cortex; a journal devoted to the study of the nervous system and behavior. 128, 312-317.
  • Dockès, J., Poldrack, R. A., Primet, R., Gözükan, H., Yarkoni, T., Suchanek, F., … & Varoquaux, G. (2020). NeuroQuery, comprehensive meta-analysis of human brain mapping. Elife, 9, e53385.
  • Lorenz, R., Johal, M., Dick, F., Hampshire, A., Leech, R., & Geranmayeh, F. (2020). A Bayesian optimisation approach for rapidly mapping residual network function in stroke. bioRxiv.
  • Zhang, Y., & Bellec, P. (2020). Transferability of Brain decoding using Graph Convolutional Networks. bioRxiv.
  • Amunts, K., Mohlberg, H., Bludau, S., & Zilles, K. (2020). Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Science, 369(6506), 988-992.
  • Zhang, Y., Tetrel, L., Thirion, B., & Bellec, P. (2020). Functional Annotation of Human Cognitive States using Deep Graph Convolution. bioRxiv.
  • Jeng, X. J., & Hu, Y. (2020). Dual Control of Testing Errors in High-Dimensional Data Analysis. arXiv preprint arXiv:2006.15667.
  • Dadi, K., Varoquaux, G., Machlouzarides-Shalit, A., Gorgolewski, K. J., Wassermann, D., Thirion, B., & Mensch, A. (2020). Fine-grain atlases of functional modes for fMRI analysis. arXiv preprint arXiv:2003.05405.


  • Poldrack, R. A., Gorgolewski, K. J., & Varoquaux, G. (2019). Computational and informatic advances for reproducible data analysis in neuroimaging. Annual Review of Biomedical Data Science, Vol. 2:119-138
  • Bazeille, T., Richard, H., Janati, H., & Thirion, B. (2019, June). Local optimal transport for functional brain template estimation. In International Conference on Information Processing in Medical Imaging (pp. 237-248). Springer, Cham.
  • Pinel, P., d’Arc, B. F., Dehaene, S., Bourgeron, T., Thirion, B., Le Bihan, D., & Poupon, C. (2019). The functional database of the ARCHI project: Potential and perspectives. NeuroImage, 197, 527-543.
  • Richard, H., Martin, L., Pinho, A. L., Pillow, J., & Thirion, B. (2019). Fast shared response model for fMRI data. arXiv preprint arXiv:1909.12537.
  • Varoquaux, G., Dadi, K., & Mensch, A. (2019). What’s in a functional brain parcellation?. NeurIPS 2019 Workshop Neuro AI.
  • Duarte, M. F. (2019) Explainable Machine Learning for Scientific Insights and Discoveries. arXiv:1905.08883
  • Al-Darkazali, M., Hoque, S., & Deravi, F. (2019). Spatial signatures for EEG-based biometric person recognition. 9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019), 2019 p. 12 (68 – 73)


  • Eickhoff, S. B., Yeo, B. T., & Genon, S. (2018). Imaging-based parcellations of the human brain. Nature Reviews Neuroscience, 19(11), 672-686.
  • Mensch, A. (2018). Learning representations from functional MRI data (Doctoral dissertation).
  • Poldrack, R. A., Gorgolewski, K. J., & Varoquaux, G. (2018). Computational and informatics advances for reproducible data analysis in neuroimaging. arXiv preprint arXiv:1809.10024
  • Pinho, A. L. et al. (2018). Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping.Sci Data 5, 180105. DOI: 10.1038/sdata.2018.105

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