Biomedical subjects

Mark W Woolrich

Publications and source records attributed to Mark W Woolrich.

7 recordsLinked to original sources

PubMed2006

Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming.

This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing.

Brain Mapping↗

PubMed2005

Variability in fMRI: a re-examination of inter-session differences.

We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708-734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. 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.

Adult↗

PubMed2005

Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.

Algorithms↗

PubMed2004

Multilevel linear modelling for FMRI group analysis using Bayesian inference.

Functional magnetic resonance imaging studies often involve the acquisition of data from multiple sessions and/or multiple subjects. A hierarchical approach can be taken to modelling such data with a general linear model (GLM) at each level of the hierarchy introducing different random effects variance components. Inferring on these models is nontrivial with frequentist solutions being unavailable. A solution is to use a Bayesian framework. One important ingredient in this is the choice of prior on the variance components and top-level regression parameters. Due to the typically small numbers of sessions or subjects in neuroimaging, the choice of prior is critical. To alleviate this problem, we introduce to neuroimage modelling the approach of reference priors, which drives the choice of prior such that it is noninformative in an information-theoretic sense. We propose two inference techniques at the top level for multilevel hierarchies (a fast approach and a slower more accurate approach). 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.

Bayes Theorem↗

PubMed2004

Constrained linear basis sets for HRF modelling using Variational Bayes.

FMRI modelling requires flexible haemodynamic response function (HRF) modelling, with the HRF being allowed to vary spatially and between subjects. To achieve this flexibility, voxelwise parameterised HRFs have been proposed; however, inference on such models is very slow. An alternative approach is to use basis functions allowing inference to proceed in the more manageable General Linear Model (GLM) framework. However, a large amount of the subspace spanned by the basis functions produces nonsensical HRF shapes. In this work we propose a technique for choosing a basis set, and then the means to constrain the subspace spanned by the basis set to only include sensible HRF shapes. Penny et al. showed how Variational Bayes can be used to infer on the GLM for FMRI. Here we extend the work of Penny et al. to give inference on the GLM with constrained HRF basis functions and with spatial Markov Random Fields on the autoregressive noise parameters. Constraining the subspace spanned by the basis set allows for far superior separation of activating voxels from nonactivating voxels in FMRI data. We use spatial mixture modelling to produce final probabilities of activation and demonstrate increased sensitivity on an FMRI dataset.

Bayes Theorem↗

PubMed2004

Advances in functional and structural MR image analysis and implementation as FSL.

The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).

Bayes Theorem↗

PubMed2004

Fully Bayesian spatio-temporal modeling of FMRI data.

We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time vector autoregressive process. Previous FMRI noise models have either been purely temporal, separable or modeling deterministic trends. The specific form of the noise process is determined using model selection techniques. Notably, this results in the need for a spatially nonstationary and temporally stationary spatial component. Within the same full model, we also investigate the variation of the HRF in different areas of the activation, and for different experimental stimuli. We propose a novel HRF model made up of half-cosines, which allows distinct combinations of parameters to represent characteristics of interest. In addition, to adaptively avoid over-fitting we propose the use of automatic relevance determination priors to force certain parameters in the model to zero with high precision if there is no evidence to support them in the data. We apply the model to three datasets and observe matter-type dependence of the spatial and temporal noise, and a negative correlation between activation height and HRF time to main peak (although we suggest that this apparent correlation may be due to a number of different effects).

Bayes Theorem↗