Research Topics
| Mark WoolrichSummaryAffiliation: University of Oxford Country: UK Publications
| Collaborators
|
Detail Information
Publications
Bayesian inference in FMRIMark W Woolrich
Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK
Neuroimage 62:801-10. 2012..But perhaps more important than these things, is the ability of Bayes to empower the methods researcher with a mathematically principled framework for inferring on any model...- MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularizationMark Woolrich
OHBA Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK
Neuroimage 57:1466-79. 2011..We apply the approach to simulated and real MEG data, and demonstrate the way in which it can automatically adapt the regularization to give good performance over a range of noise and signal levels... - Bayesian analysis of neuroimaging data in FSLMark W Woolrich
University of Oxford Centre for Functional MRI of the Brain, UK
Neuroimage 45:S173-86. 2009..Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases... 
Fully Bayesian spatio-temporal modeling of FMRI dataMark W Woolrich
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
IEEE Trans Med Imaging 23:213-31. 2004....- Robust group analysis using outlier inferenceMark Woolrich
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
Neuroimage 41:286-301. 2008..The performance of the method is demonstrated on simulated and fMRI data and is compared with iterative reweighted least squares and permutation testing... - Variational Bayes inference of spatial mixture models for segmentationMark W Woolrich
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain FMRIB, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
IEEE Trans Med Imaging 25:1380-91. 2006..We examine the behavior of this approach when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging statistical parametric maps... - Bayesian inference of hemodynamic changes in functional arterial spin labeling dataMark W Woolrich
University of Oxford, Centre for Functional MRI of the Brain, John Radcliffe Hospital, Oxford, United Kingdom
Magn Reson Med 56:891-906. 2006..We also consider how the static magnetization, M, might be related to changes in CBV by assuming the same mechanism for water exchange as in vascular space occupancy... - Mixture models with adaptive spatial regularization for segmentation with an application to FMRI dataMark W Woolrich
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
IEEE Trans Med Imaging 24:1-11. 2005..We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs... - Multilevel linear modelling for FMRI group analysis using Bayesian inferenceMark W Woolrich
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
Neuroimage 21:1732-47. 2004..We also demonstrate that we can infer on the top level of multilevel hierarchies by inferring on the levels of the hierarchy separately and passing summary statistics of a noncentral multivariate t distribution between them... - Constrained linear basis sets for HRF modelling using Variational BayesMark W Woolrich
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
Neuroimage 21:1748-61. 2004..We use spatial mixture modelling to produce final probabilities of activation and demonstrate increased sensitivity on an FMRI dataset... - Bayesian deconvolution of [corrected] fMRI data using bilinear dynamical systemsSalima Makni
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford, UK
Neuroimage 42:1381-96. 2008..We finally demonstrate its use on real fMRI data in one slice of the brain... 
Evidence for a vascular contribution to diffusion FMRI at high b valueKarla L Miller
Centre for Functional MRI of the Brain FMRIB, Oxford Centre for Clinical Magnetic Resonance OCMR, and Department of Experimental Psychology, University of Oxford, Oxford, Oxon OX3 9DU, United Kingdom
Proc Natl Acad Sci U S A 104:20967-72. 2007..These results suggest that a large component of the diffusion FMRI signal at high b value is vascular rather than neuronal...- Network modelling methods for FMRIStephen M Smith
FMRIB Oxford University Centre for Functional MRI of the Brain, Department of Clinical Neurology, University of Oxford, Oxford, UK
Neuroimage 54:875-91. 2011.... - A general framework for the analysis of vessel encoded arterial spin labeling for vascular territory mappingMichael A Chappell
Oxford Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Headington, Oxford, United Kingdom
Magn Reson Med 64:1529-39. 2010..Similar performance was found for data acquired using a pseudo-continuous labeling scheme both in the neck and above the Circle of Willis... - Separation of macrovascular signal in multi-inversion time arterial spin labelling MRIMichael A Chappell
Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
Magn Reson Med 63:1357-65. 2010.... - Linked independent component analysis for multimodal data fusionAdrian R Groves
FMRIB Oxford University Centre for Functional MRI of the Brain, Department Clinical Neurology, University of Oxford, Oxford, UK
Neuroimage 54:2198-217. 2011.... - Vascular territory image analysis using vessel encoded arterial spin labelingMichael A Chappell
Oxford Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX4 3HD, UK
Med Image Comput Comput Assist Interv 12:514-21. 2009..In simulation and on real data the method is shown to reliably determine vascular territories in the brain, including the case where the number of vessels exceeds the number of independent measurements... - Assessment of arterial arrival times derived from multiple inversion time pulsed arterial spin labeling MRIBradley J MacIntosh
FMRIB Centre, Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital, Oxford, UK
Magn Reson Med 63:641-7. 2010..Pulsed arterial spin labeling signals were simulated using an identical sampling scheme as the empiric study and revealed AAT can be estimated robustly when simulated arrival times are well beyond the normal range... - How green is the grass on the other side? Frontopolar cortex and the evidence in favor of alternative courses of actionErie D Boorman
Centre for Functional MRI of the Brain, University of Oxford, Oxford 0X3 9DU, UK
Neuron 62:733-43. 2009..Collectively, these findings reveal complementary prefrontal computations essential for promoting short- and long-term behavioral flexibility... - Combined spatial and non-spatial prior for inference on MRI time-seriesAdrian R Groves
FMRIB Centre, Department of Clinical Neurology, John Radcliffe Hospital, Oxford, UK
Neuroimage 45:795-809. 2009..By combining both types of prior information, this new prior performs consistently well across a wider range of situations than either prior alone, and provides better estimates when both types of prior information are relevant... - Associative learning of social valueTimothy E J Behrens
FMRIB Centre, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
Nature 456:245-9. 2008..These findings suggest that human social valuation can be realized by means of the same associative processes previously established for learning other, simpler, features of the environment... - Advances in functional and structural MR image analysis and implementation as FSLStephen M Smith
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain FMRIB, Department of Clinical Neurology, John Radcliffe Hospital, Oxford University, Headington, Oxford OX3 9DU, UK
Neuroimage 23:S208-19. 2004..The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL)... - Learning the value of information in an uncertain worldTimothy E J Behrens
FMRIB Centre, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
Nat Neurosci 10:1214-21. 2007..Furthermore, variations in this ACC signal across the population predict variations in subject learning rates. Our results provide a formal account of how we weigh our different experiences in guiding our future actions... - Variability in fMRI: a re-examination of inter-session differencesStephen M Smith
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain FMRIB, Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, Oxford, United Kingdom
Hum Brain Mapp 24:248-57. 2005..Finally, we show that the use of different first-level preprocessing, time-series statistics, and registration analysis methodologies can give significantly different inter-session analysis results... - Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition primingChristian F Beckmann
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain FMRIB, University of Oxford, Oxford, UK
Hum Brain Mapp 27:380-91. 2006..These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing... - Knowing when to stop: the brain mechanisms of chasing lossesDaniel K Campbell-Meiklejohn
Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
Biol Psychiatry 63:293-300. 2008..Continued gambling to recover previous losses ("loss-chasing") is central to pathological gambling. However, very little is known about the neural mechanisms that mediate this behavior...