| Scikit-learn: Machine learning in Python F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... the Journal of machine Learning research 12, 2825-2830, 2011 | 99176 | 2011 |
| Machine learning for neuroimaging with scikit-learn A Abraham, F Pedregosa, M Eickenberg, P Gervais, A Mueller, J Kossaifi, ... Frontiers in neuroinformatics 8, 71792, 2014 | 1989 | 2014 |
| Variability in the analysis of a single neuroimaging dataset by many teams R Botvinik-Nezer, F Holzmeister, CF Camerer, A Dreber, J Huber, ... Nature 582 (7810), 84-88, 2020 | 988 | 2020 |
| Best practices in data analysis and sharing in neuroimaging using MRI TE Nichols, S Das, SB Eickhoff, AC Evans, T Glatard, M Hanke, ... Nature neuroscience 20 (3), 299-303, 2017 | 702 | 2017 |
| Assessing and tuning brain decoders: cross-validation, caveats, and guidelines G Varoquaux, PR Raamana, DA Engemann, A Hoyos-Idrobo, Y Schwartz, ... NeuroImage 145, 166-179, 2017 | 674 | 2017 |
| Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example A Abraham, MP Milham, A Di Martino, RC Craddock, D Samaras, ... NeuroImage 147, 736-745, 2017 | 645 | 2017 |
| Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses B Thirion, P Pinel, S Mériaux, A Roche, S Dehaene, JB Poline Neuroimage 35 (1), 105-120, 2007 | 634 | 2007 |
| An automatic valuation system in the human brain: evidence from functional neuroimaging M Lebreton, S Jorge, V Michel, B Thirion, M Pessiglione Neuron 64 (3), 431-439, 2009 | 547 | 2009 |
| Recruitment of an area involved in eye movements during mental arithmetic A Knops, B Thirion, EM Hubbard, V Michel, S Dehaene Science 324 (5934), 1583-1585, 2009 | 511 | 2009 |
| Inverse retinotopy: inferring the visual content of images from brain activation patterns B Thirion, E Duchesnay, E Hubbard, J Dubois, JB Poline, D Lebihan, ... Neuroimage 33 (4), 1104-1116, 2006 | 429 | 2006 |
| Seeing it all: Convolutional network layers map the function of the human visual system M Eickenberg, A Gramfort, G Varoquaux, B Thirion NeuroImage 152, 184-194, 2017 | 408 | 2017 |
| Which fMRI clustering gives good brain parcellations? B Thirion, G Varoquaux, E Dohmatob, JB Poline Frontiers in neuroscience 8, 167, 2014 | 391 | 2014 |
| Brain covariance selection: better individual functional connectivity models using population prior G Varoquaux, A Gramfort, JB Poline, B Thirion Advances in neural information processing systems 23, 2010 | 343 | 2010 |
| Benchmarking functional connectome-based predictive models for resting-state fMRI K Dadi, M Rahim, A Abraham, D Chyzhyk, M Milham, B Thirion, ... NeuroImage 192, 115-134, 2019 | 324 | 2019 |
| Connectivity‐based parcellation: Critique and implications SB Eickhoff, B Thirion, G Varoquaux, D Bzdok Human brain mapping 36 (12), 4771-4792, 2015 | 300 | 2015 |
| Deciphering cortical number coding from human brain activity patterns E Eger, V Michel, B Thirion, A Amadon, S Dehaene, A Kleinschmidt Current Biology 19 (19), 1608-1615, 2009 | 276 | 2009 |
| Dealing with the shortcomings of spatial normalization: Multi‐subject parcellation of fMRI datasets B Thirion, G Flandin, P Pinel, A Roche, P Ciuciu, JB Poline Human brain mapping 27 (8), 678-693, 2006 | 245 | 2006 |
| Multi-subject dictionary learning to segment an atlas of brain spontaneous activity G Varoquaux, A Gramfort, F Pedregosa, V Michel, B Thirion Information Processing in Medical Imaging: 22nd International Conference …, 2011 | 243 | 2011 |
| A group model for stable multi-subject ICA on fMRI datasets G Varoquaux, S Sadaghiani, P Pinel, A Kleinschmidt, JB Poline, B Thirion Neuroimage 51 (1), 288-299, 2010 | 209 | 2010 |
| Scikit-learn: Machine learning in python journal of machine learning research F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... Journal of machine learning research 12, 2825-2830, 2011 | 208 | 2011 |
