ORCID Profile
0000-0001-8460-8854
Current Organisation
University of Oxford
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Publisher: Elsevier BV
Date: 08-2012
DOI: 10.1016/J.NEUROIMAGE.2011.09.015
Abstract: FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.
Publisher: Elsevier BV
Date: 04-2004
Publisher: Elsevier BV
Date: 04-2015
Publisher: Springer Science and Business Media LLC
Date: 05-03-2019
DOI: 10.1038/S41467-019-08999-0
Abstract: Traveling patterns of neuronal activity—brain waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently erse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Elsevier BV
Date: 07-2018
Publisher: Frontiers Media SA
Date: 28-08-2018
Publisher: Elsevier BV
Date: 03-2009
DOI: 10.1016/J.NEUROIMAGE.2008.10.055
Abstract: Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.
Publisher: Elsevier BV
Date: 07-2020
Publisher: Cold Spring Harbor Laboratory
Date: 17-09-2018
DOI: 10.1101/419374
Abstract: Even in response to apparently simple tasks such as hand moving, human brain activity shows remarkable inter-subject variability. Presumably, this variability reflects genuine behavioural or functional variability. Recently, spatial variability of resting-state features in fMRI - specifically connectivity - has been shown to explain (spatial) task-response variability. Such a link, however, is still missing for M/EEG data and its spectrally rich structure. At the same time, it has recently been shown that task responses in M/EEG can be well represented using transient spectral events bursting at fast time scales. Here, we show that in idual differences in the spatio-spectral structure of M/EEG task responses, can, to a reasonable degree, be predicted from in idual differences in transient spectral events identified at rest. In a MEG dataset of erse task conditions (including motor responses, working memory and language comprehension tasks) and resting-state sessions for each subject (n = 89), we used Hidden-Markov-Modelling to identify transient spectral events as a feature set to learn the mapping of space-time-frequency content from rest to task. Resulting trial-averaged, subject-specific task-response predictions were then compared with the actual task responses in left-out subjects. All task conditions were predicted significantly above chance. Furthermore, we observed a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. These findings support the idea that subject-specific transient spectral events in resting-state neural activity are linked to, and predictive of, subject-specific trial-averaged task responses in a wide range of experimental conditions.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
Publisher: Elsevier BV
Date: 2004
DOI: 10.1016/J.NEUROIMAGE.2004.07.051
Abstract: 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).
Publisher: Wiley
Date: 14-01-2005
DOI: 10.1002/HBM.20080
Publisher: Proceedings of the National Academy of Sciences
Date: 07-02-2012
Abstract: Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even “at rest,” the brain's different functional networks spontaneously fluctuate in their activity level each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks one ideally wants a network model that explicitly allows overlap, for ex le, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging s ling rate. We identify multiple “temporal functional modes,” including several that sub ide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.
Publisher: Elsevier BV
Date: 12-2013
Publisher: Public Library of Science (PLoS)
Date: 23-02-2018
Publisher: Elsevier BV
Date: 2007
DOI: 10.1016/J.NEUROIMAGE.2006.09.019
Abstract: Optimising the efficiency of an experimental design is known to be of great importance. However, existing methods for calculating design rank deficiency and contrast estimability (an important aspect of experimental design) relate to computational precision rather than image noise and are therefore not very meaningful. For ex le, a contrast between two experimental conditions may be mathematically "estimable" while requiring a huge differential BOLD response for statistical significance to be reached. In this paper we formulate standard efficiency equations in terms of required BOLD effect, and use this to generate measures of rank/estimability which are meaningful. This takes into account the strength and smoothness of the timeseries noise and is applicable to complex contrasts we show how to re-express several regressors and an associated contrast vector as a single equivalent regressor, so that we can calculate the contrast's effective peak-peak height unambiguously. We also present some ex le results on typical designs, and characterise noise results from a range of typical FMRI acquisitions, in order to allow experimenters to apply efficiency estimation in advance of acquiring data.
Publisher: Cold Spring Harbor Laboratory
Date: 31-07-2017
DOI: 10.1101/170779
Abstract: The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information ( e.g . derived from diffusion tractography) in order to couple dynamical models of local neuronal activity into networks of interacting regions distributed across the cortex. In practice however, these models are difficult to parametrise, and their simulation is often delicate and computationally expensive. This undermines the experimental aspect of scientific modelling, and stands in the way of comparing different parametrisations, network architectures, or models in general, with confidence. Here, we advocate the use of Bayesian optimisation for assessing the capabilities of biophysical network models, given a set of desired properties ( e.g . band-specific functional connectivity) and in turn the use of this assessment as a principled basis for incremental modelling and model comparison. We adapt an optimisation method designed to cope with costly, high-dimensional, non-convex problems, and demonstrate its use and effectiveness. We find that this method is able to converge to regions of high functional similarity with real MEG data, with very few s les given the number of parameters, without getting stuck in local extrema, and while building and exploiting a map of uncertainty defined smoothly across the parameter space. We compare the results obtained using different methods of structural connectivity estimation from diffusion tractography, and find that one method leads to better simulations.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Mark Woolrich.