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211
Multilevel linear modelling for FMRI group analysis using Bayesian inference
 Neuroimage
, 2004
"... 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 varia ..."
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Cited by 90 (6 self)
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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 toplevel 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 informationtheoretic 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.
Reading speech from still and moving faces: The neural substrates of visible speech
 J. of Cog. Neuroscience
, 2003
"... & Speech is perceived both by ear and by eye. Unlike heard speech, some seen speech gestures can be captured in stilled image sequences. Previous studies have shown that in hearing people, natural timevarying silent seen speech can access the auditory cortex (left superior temporal regions). Us ..."
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Cited by 76 (1 self)
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& Speech is perceived both by ear and by eye. Unlike heard speech, some seen speech gestures can be captured in stilled image sequences. Previous studies have shown that in hearing people, natural timevarying silent seen speech can access the auditory cortex (left superior temporal regions). Using functional magnetic resonance imaging (fMRI), the present study explored the extent to which this circuitry was activated when seen speech was deprived of its timevarying characteristics. In the scanner, hearing participants were instructed to look for a prespecified visible speech target sequence (‘‘voo’’ or ‘‘ahv’’) among other monosyllables. In one condition, the image sequence comprised a series of stilled key frames showing apical gestures (e.g., separate frames for ‘‘v’ ’ and
I (2004) Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. Neuroimage 21:16521664. Supplemental Figure Legends Supplemental Figure 1. Graph analysis procedure. Figure illustrates the individual st
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"... Carbon dioxide is a potent cerebral vasodilator. We have identified a significant source of lowfrequency variation in blood oxygen leveldependent (BOLD) magnetic resonance imaging (MRI) signal at 3 T arising from spontaneous fluctuations in arterial carbon dioxide level in volunteers at rest. Fluct ..."
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Cited by 60 (0 self)
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Carbon dioxide is a potent cerebral vasodilator. We have identified a significant source of lowfrequency variation in blood oxygen leveldependent (BOLD) magnetic resonance imaging (MRI) signal at 3 T arising from spontaneous fluctuations in arterial carbon dioxide level in volunteers at rest. Fluctuations in the partial pressure of endtidal carbon dioxide (PETCO 2)ofF1.1 mm Hg in the frequency range 0 –0.05 Hz were observed in a cohort of nine volunteers. Correlating with these fluctuations were significant generalized grey and white matter BOLD signal fluctuations. We observed a mean (Fstandard error) regression coefficient across the group of 0.110 F 0.033 % BOLD signal change per mm Hg CO2 for grey matter and 0.049 F 0.022 % per mm Hg in white matter. PET CO2related BOLD signal fluctuations showed regional differences across the grey matter, suggesting variability of the responsiveness to carbon dioxide at rest. Functional magnetic resonance imaging (fMRI) results were corroborated by transcranial
Variational Bayesian Inference for fMRI time series
 NeuroImage
"... We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models with Autoregressive (AR) error processes. We make use of the Variational Bayesian (VB) framework which approximates the true posterior density with a factorised density. The fidel ..."
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Cited by 55 (14 self)
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We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models with Autoregressive (AR) error processes. We make use of the Variational Bayesian (VB) framework which approximates the true posterior density with a factorised density. The fidelity of this approximation is verified via Gibbs sampling. The VB approach provides a natural extension to previous Bayesian analyses which have used Empirical Bayes. VB has the advantage of taking into account the variability of hyperparameter estimates with little additional computational effort. Further, VB allows for automatic selection of the order of the AR process. Results are shown on simulated data and on data from an eventrelated fMRI experiment. 1
Constrained linear basis sets for HRF modelling using Variational Bayes
 Neuroimage
, 2004
"... 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 ..."
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Cited by 52 (3 self)
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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. [NeuroImage (2003)] 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.
Modelling functional integration: a comparison of structural equation and dynamic causal models
 NeuroImage
, 2004
"... The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper we review two different approaches to modelling ef ..."
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Cited by 44 (2 self)
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The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper we review two different approaches to modelling effective connectivity from fMRI data, Structural Equation Models (SEMs) and Dynamic Causal Models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM a distinction is made between the ‘neuronal level ’ and the ‘hemodynamic level’. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics which in turn cause changes in the observed hemodynamics. In SEM changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.
Detecting and adjusting for artifacts in fMRI time series data
 NeuroImage
, 2005
"... We present a new method to detect and adjust for noise and artifacts in functional MRI time series data. We note that the assumption of stationary variance, which is central to the theoretical treatment of fMRI time series data, is often violated in practice. Sporadic events such as eye, mouth, or a ..."
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Cited by 38 (5 self)
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We present a new method to detect and adjust for noise and artifacts in functional MRI time series data. We note that the assumption of stationary variance, which is central to the theoretical treatment of fMRI time series data, is often violated in practice. Sporadic events such as eye, mouth, or arm movements can increase noise in a spatially global pattern throughout an image, leading to a nonstationary noise process. We derive a restricted maximum likelihood (ReML) algorithm that estimates the variance of the noise for each image in the time series. These variance parameters are then used to obtain a weighted least squares estimate of the regression parameters of a linear model. We apply this approach to a typical fMRI experiment with a block design and show that the noise estimates strongly vary across different images and that our method detects and appropriately weights images that are affected by artifacts. Furthermore, we show that the noise process has a global spatial distribution and that the variance increase is multiplicative rather than additive. The new algorithm results in significantly increased sensitivity in the ability to detect regions of activation. The new method may be particularly useful for studies that involve special populations (e.g., children or elderly) where sporadic, artifactgenerating events are more likely.
Joint DetectionEstimation of Brain Activity in fMRI: An Extended Model Accounting for Serial Correlation
, 2005
"... Analysis of functional magnetic resonance imaging (fMRI) data focuses essentially on two questions: first, a detection problem that studies which parts of the brain are activated by a given stimulus and, second, an estimation problem that investigates the temporal dynamic of the brain response durin ..."
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Cited by 36 (7 self)
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Analysis of functional magnetic resonance imaging (fMRI) data focuses essentially on two questions: first, a detection problem that studies which parts of the brain are activated by a given stimulus and, second, an estimation problem that investigates the temporal dynamic of the brain response during activations. Up to now, these questions have been addressed independently. However, the activated areas need to be known prior to the analysis of the temporal dynamic of the response. Similarly, a typical shape of the response has to be assumed a priori for detection purpose. This situation motivates the need for new methods in neuroimaging data analysis that are able to go beyond this unsatisfactory tradeoff. The present paper raises a novel detectionestimation approach to perform these two tasks simultaneously in regionbased analysis. In the Bayesian framework, the detection of brain activity is achieved using a mixture of two Gaussian distributions as a prior model on the “neural ” response levels, whereas the hemodynamic impulse response is constrained to be smooth enough in the time domain with a Gaussian prior. All parameters of interest, as well as hyperparameters, are estimated from the posterior distribution using Gibbs sampling and posterior mean estimates. Results obtained both on simulated and real fMRI data demonstrate first that our approach can segregate activated and nonactivated voxels in a given region of interest (ROI) and, second, that it can provide spatial activation maps without any assumption on the exact shape of the Hemodynamic Response Function (HRF), in contrast to standard modelbased analysis.