Multilevel linear modelling for FMRI group analysis using Bayesian inference

By Mark William Woolrich, Timothy Edward John Behrens, Christian F. Beckmann, Mark Jenkinson, Stephen M. Smith. 2004

1,748 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Statistics and Probability

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Citations in the corpus

Robust group analysis using outlier inference (2008, positive): “Hence, we set the priors on the regression and variance parameters to be non-informative Berger- Bernardo reference priors (Woolrich et al., 2004).”
Chronic Pain and the Emotional Brain: Specific Brain Activity Associated with Spontaneous Fluctuations of Intensity of Chronic Back Pain (2006, methodology): “The fMRI signal was then linearly modeled on a voxel by voxel basis using FMRIB’s Improved Linear Model (FILM) with local autocorrelation correction (Woolrich et al., 2001, 2004).”
Handbook of Functional MRI Data Analysis: Index (2011, methodology): “This is the approach taken by Woolrich et al. (2004a) in the FMIRB Linear Optimal Basis Set (FLOBS) algorithm.”
“Roles for the pre-supplementary motor area and the right inferior frontal gyrus in stopping action: electrophysiological responses and functional and structural connectivity” (2011, methodology): “Higher-level analysis was carried out using FLAME (FMRIB's Local Analysis of Mixed Effects) stage 1 (Beckmann et al., 2003; Woolrich, 2008; Woolrich et al., 2004 We note that the locus of his epileptic activity was subsequently determined to not include either the preSMA or the IFG.”
Beyond Feeling: Chronic Pain Hurts the Brain, Disrupting the Default-Mode Network Dynamics (2008, methodology): “…tool (Jezzard et al., 2001); www.fmrib.ox.ac.uk/fsl] The fMRI signal was linearly modeled on a voxel-by-voxel basis using FILM (FMRIB’s improved linear model) with local autocorrelation correction (Woolrich et al., 2001, 2004), yielding statistical parametric maps for each subject and condition.”
Transient and linearly graded deactivation of the human default-mode network by a visual detection task (2008, positive): “Finally, at the third level, a full mixedeffects analysis was performed to reveal the significant responses to each trial-type across all runs and participants using the FLAME algorithm (Beckmann et al., 2003; Woolrich et al., 2004).”
Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses (2007, background): “It is in fact a particular instance of mixed effects models, which recently gained in popularity [Worsley et al., 2002, Friston et al., 2002, Beckmann et al., 2003, Neumann and Lohmann, 2003, Woolrich et al., 2004].”
Great expectations: neural computations underlying the use of social norms in decision-making. (2011, methodology): “A third-level mixed effects model using FEAT with full Bayesian inference (Woolrich et al., 2004) was used to summarize group effects for every specified contrast.”
A multisensory investigation of the functional significance of the "pain matrix" (2011, neutral): “Group-level statistical analyses were carried out using a mixed-effect approach as implemented in FEAT (Woolrich et al., 2004).”
NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain (2014, background): “…also handle mask ﬁles (for describing ROIs), label maps (a result of parcellation studies), posterior probability maps (coming from Bayesian methods; Woolrich et al., 2004), weight maps (coming from multivariate pattern analysis methods; Haxby, 2012), and group-level lesion maps (from clinical…”
Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies (2009, methodology): “In the second-level analyses (Woolrich et al., 2004) MFX activation maps corresponding to the main pain effect were created.”
Neural mechanisms underlying the induction and relief of perceptual curiosity (2012, methodology): “The contrasts were combined across the five runs on a subject-by-subject basis using fixed-effects analyses (Beckmann et al., 2003; Woolrich et al., 2004).”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “The group-level BASC is actually based on the results of the individual-level BASC, making this approach analogous to the hierarchical multi-level framework used for general linear model analysis where parameter and uncertainty estimates are passed from one level of the analysis to the other (Woolri”
Bayesian analysis of neuroimaging data in FSL (2009, background): “However, it is possible for a basis set to produce nonsensical HRFs. Priors can be placed on the basis function regression parameters such that we constrain the inference to only those combinations of parameters that give biophysically plausible HRF shapes (Woolrich et al., 2004b).”
Advances in functional and structural MR image analysis and implementation as FSL. (2004, methodology): “The investigations covered in Beckmann et al. (2003a) assume that all variance components are known; taking this work further, in Woolrich et al. (2004a) , using a fully Bayesian approach, we show the split-level model equivalence taking into account that the variance components are unknown.”