| Comparing families of dynamic causal models WD Penny, KE Stephan, J Daunizeau, MJ Rosa, KJ Friston, TM Schofield, ... PLoS computational biology 6 (3), e1000709, 2010 | 747 | 2010 |
| SPM12 manual J Ashburner, G Barnes, CC Chen, J Daunizeau, G Flandin, K Friston, ... Wellcome Trust Centre for Neuroimaging, London, UK 2464 (4), 2014 | 530 | 2014 |
| PRoNTo: pattern recognition for neuroimaging toolbox J Schrouff, MJ Rosa, JM Rondina, AF Marquand, C Chu, J Ashburner, ... Neuroinformatics 11, 319-337, 2013 | 468 | 2013 |
| Sparse network-based models for patient classification using fMRI MJ Rosa, L Portugal, T Hahn, AJ Fallgatter, MI Garrido, J Shawe-Taylor, ... Neuroimage 105, 493-506, 2015 | 206 | 2015 |
| Connectivity-based neurofeedback: dynamic causal modeling for real-time fMRI Y Koush, MJ Rosa, F Robineau, K Heinen, SW Rieger, N Weiskopf, ... Neuroimage 81, 422-430, 2013 | 163 | 2013 |
| EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches MJ Rosa, J Daunizeau, KJ Friston Journal of integrative neuroscience 9 (04), 453-476, 2010 | 146 | 2010 |
| Estimating the transfer function from neuronal activity to BOLD using simultaneous EEG-fMRI MJ Rosa, J Kilner, F Blankenburg, O Josephs, W Penny Neuroimage 49 (2), 1496-1509, 2010 | 129 | 2010 |
| Post-hoc selection of dynamic causal models MJ Rosa, K Friston, W Penny Journal of neuroscience methods 208 (1), 66-78, 2012 | 99 | 2012 |
| Time scales of representation in the human brain: Weighing past information to predict future events LM Harrison, S Bestmann, MJ Rosa, W Penny, GGR Green Fontiers in Human Neuroscience 5, 37, 2011 | 84 | 2011 |
| Classification and characterisation of brain network changes in chronic back pain: a multicenter study H Mano, G Kotecha, K Leibnitz, T Matsubara, C Sprenger, A Nakae, ... Wellcome open research 3, 2018 | 82 | 2018 |
| Bayesian model selection maps for group studies MJ Rosa, S Bestmann, L Harrison, W Penny Neuroimage 49 (1), 217-224, 2010 | 82 | 2010 |
| Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models J Schrouff, JM Monteiro, L Portugal, MJ Rosa, C Phillips, ... Neuroinformatics 16, 117-143, 2018 | 68 | 2018 |
| Decoding the matrix: benefits and limitations of applying machine learning algorithms to pain neuroimaging MJ Rosa, B Seymour Pain 155 (5), 864-867, 2014 | 67 | 2014 |
| The role of the subgenual anterior cingulate cortex in dorsomedial prefrontal–amygdala neural circuitry during positive‐social emotion regulation F Scharnowski, AA Nicholson, S Pichon, MJ Rosa, G Rey, SB Eickhoff, ... Human brain mapping 41 (11), 3100-3118, 2020 | 53 | 2020 |
| Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition S Ranlund, MJ Rosa, S de Jong, JH Cole, M Kyriakopoulos, CHY Fu, ... NeuroImage: Clinical 20, 1026-1036, 2018 | 49 | 2018 |
| Auditory prediction errors as individual biomarkers of schizophrenia JA Taylor, N Matthews, PT Michie, MJ Rosa, MI Garrido NeuroImage: Clinical 15, 264-273, 2017 | 46 | 2017 |
| Multiple holdouts with stability: improving the generalizability of machine learning analyses of brain–behavior relationships A Mihalik, FS Ferreira, M Moutoussis, G Ziegler, RA Adams, MJ Rosa, ... Biological psychiatry 87 (4), 368-376, 2020 | 40 | 2020 |
| Brain-behaviour modes of covariation in healthy and clinically depressed young people A Mihalik, FS Ferreira, MJ Rosa, M Moutoussis, G Ziegler, JM Monteiro, ... Scientific reports 9 (1), 11536, 2019 | 39 | 2019 |
| Connectivity changes underlying neurofeedback training of visual cortex activity F Scharnowski, MJ Rosa, N Golestani, C Hutton, O Josephs, N Weiskopf, ... PloS one 9 (3), e91090, 2014 | 33 | 2014 |
| Bayesian comparison of neurovascular coupling models using EEG-fMRI MJ Rosa, JM Kilner, WD Penny PLoS computational biology 7 (6), e1002070, 2011 | 29 | 2011 |
Will PennyProfessor of Imaging Neuroscience UCL在 ucl.ac.uk 的电子邮件经过验证
