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Your search keyword '"Woolrich, M.W."' showing total 36 results
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36 results on '"Woolrich, M.W."'

8. Brainwave viscosity in propofol anaesthesia

Author

Fabus, M.S., Woolrich, M.W., and Warnaby, C.E.

Subjects
Anesthesiology and Pain Medicine, Article
Abstract

Human EEG during propofol anaesthesia shows large-scale changes including traveling slow waves 1 . Slow-wave saturation is a potentially individualised marker of loss of perception 2 . However, much remains unclear about the dynamics of slow waves. Iterated empirical mode decomposition (itEMD 3 ) is a novel data-driven method for segregating data into physiologically relevant oscillatory modes. We used itEMD to identify spectral modes and their sources / sinks in propofol EEG. Viscosity is a physical quantity expressing the magnitude of resistance to flow. Considering traveling electric potentials in the brain as a flow, we extended the notion of viscosity to traveling brainwaves. Using this, we explored how brainwave viscosity changes in volunteer propofol anaesthesia.

Published
2022

19. How reliable are MEG resting-state connectivity metrics?

Author

Colclough, G.L., Woolrich, M.W., Tewarie, P.K., Brookes, M.J., Quinn, A.J., and Smith, S.M.

Subjects
Functional connectivity, MEG, Source leakage, Magnetic field spread, Neurology, Cognitive Neuroscience, Connectome, Network analysis
Abstract

MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.

Published
2016
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25. Large-scale Probabilistic Functional Modes from resting state fMRI

Author

Harrison, S.J., Woolrich, M.W., Robinson, E.C., Glasser, M.F., Beckmann, C.F., Jenkinson, M., Smith, S.M., Harrison, S.J., Woolrich, M.W., Robinson, E.C., Glasser, M.F., Beckmann, C.F., Jenkinson, M., and Smith, S.M.

Abstract

Contains fulltext : 135316.pdf (Publisher’s version ) (Open Access), It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is 'at rest'. However, characterising this activity in an interpretable manner is still a very open problem. In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable. We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects.

Published
2015

27. Functional connectomics from resting-state fMRI

Author

Smith, S.M., Vidaurre, D., Beckmann, C.F., Glasser, M.F., Jenkinson, M., Miller, K., Nichols, T.E., Robinson, E.C., Salimi-Khorshidi, G., Woolrich, M.W., Barch, D.M., Ugurbil, K., Essen, D.C. van, Smith, S.M., Vidaurre, D., Beckmann, C.F., Glasser, M.F., Jenkinson, M., Miller, K., Nichols, T.E., Robinson, E.C., Salimi-Khorshidi, G., Woolrich, M.W., Barch, D.M., Ugurbil, K., and Essen, D.C. van

Abstract

Item does not contain fulltext

Published
2013

28. Resting-state fMRI in the Human Connectome Project

Author

Smith, S.M., Beckmann, C.F., Andersson, J., Auerbach, E.J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D.A., Griffanti, L., Harms, M.P., Kelly, M., Laumann, T., Miller, K.L., Moeller, S., Petersen, S., Power, J., Salimi-Khorshidi, G., Snyder, A.Z., Vu, A.T., Woolrich, M.W., Xu, J., Yacoub, E., Ugurbil, K., Essen, D.C. van, Glasser, M.F., Smith, S.M., Beckmann, C.F., Andersson, J., Auerbach, E.J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D.A., Griffanti, L., Harms, M.P., Kelly, M., Laumann, T., Miller, K.L., Moeller, S., Petersen, S., Power, J., Salimi-Khorshidi, G., Snyder, A.Z., Vu, A.T., Woolrich, M.W., Xu, J., Yacoub, E., Ugurbil, K., Essen, D.C. van, and Glasser, M.F.

Abstract

Item does not contain fulltext

Published
2013

36. RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI

Author

Sotiropoulos, S.N., Jbabdi, S., Andersson, J.L., Woolrich, M.W., Ugurbil, K., Behrens, T.E.J., Sotiropoulos, S.N., Jbabdi, S., Andersson, J.L., Woolrich, M.W., Ugurbil, K., and Behrens, T.E.J.

Abstract

The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.

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