W D Penny
Affiliation: University College London
- A dynamical pattern recognition model of γ activity in auditory cortexM Zavaglia
Department of Electronics, Computer Science and Systems DEIS, Via Venezia 52, 47023 Cesena, Italy
Neural Netw 28:1-14. 2012..Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain...
- Dynamic Causal Models for phase couplingW D Penny
Wellcome Trust Centre for Neuroimaging, University College, 12 Queen Square, London WC1N 3BG, UK
J Neurosci Methods 183:19-30. 2009..For example, whether activity is driven by master-slave versus mutual entrainment mechanisms. Results are presented on synthetic data from physiological models and on MEG data from a study of visual working memory...
- Modelling functional integration: a comparison of structural equation and dynamic causal modelsW D Penny
Wellcome Department of Imaging Neuroscience, University College London, London, United Kingdom
Neuroimage 23:S264-74. 2004..This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion...
- Robust Bayesian General Linear ModelsW D Penny
Wellcome Department of Imaging Neuroscience, University College London, London WC1N 3BG, UK
Neuroimage 36:661-71. 2007..This allows the RGLM to default to the usual GLM when robustness is not required. The method is compared to other robust regression methods and applied to synthetic data and fMRI...
- Testing for nested oscillationW D Penny
Wellcome Trust Centre for Neuroimaging, University College, London, UK
J Neurosci Methods 174:50-61. 2008..Our overall conclusion is that the GLM measure is the best all-round approach for detecting nested oscillation...
- Comparing dynamic causal models using AIC, BIC and free energyW D Penny
Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK
Neuroimage 59:319-30. 2012..Differences in performance are examined in the context of General Linear Models (GLMs) and Dynamic Causal Models (DCMs). We find that the Free Energy has the best model selection ability and recommend it be used for comparison of DCMs...
- Comparing families of dynamic causal modelsWill D Penny
Wellcome Trust Centre for Neuroimaging, University College, London, United Kingdom
PLoS Comput Biol 6:e1000709. 2010..We illustrate the methods using Dynamic Causal Models of brain imaging data...
- Multivariate autoregressive modeling of fMRI time seriesL Harrison
Wellcome Department of Imaging Neuroscience, University College London, 12 Queen Square, London WC1N 3BG, UK
Neuroimage 19:1477-91. 2003..A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods...
- Comparing dynamic causal modelsW D Penny
Wellcome Department of Imaging Neuroscience, University College London, London, UK
Neuroimage 22:1157-72. 2004..The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition...
- Decoding oscillatory representations and mechanisms in memoryA Jafarpour
Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Magdeburg, Germany
Neuropsychologia 51:772-80. 2013..We conclude that despite its infancy and existing methodological challenges, MVPC of EEG and MEG data is a powerful tool with which to assess mechanistic models of memory...
- Efficient gradient computation for dynamical modelsB Sengupta
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK Electronic address
Neuroimage 98:521-7. 2014..In the context of neuroimaging, adjoint based inversion of dynamical causal models (DCMs) can, in principle, enable the study of models with large numbers of nodes and parameters. ..