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98
Deconvolution of impulse response in eventrelated BOLD fMRI. NeuroImage 9
, 1999
"... The temporal characteristics of the BOLD response in sensorimotor and auditory cortices were measured in subjects performing finger tapping while listening to metronome pacing tones. A repeated trial paradigm was used with stimulus durations of 167 ms to 16 s and intertrial times of 30 s. Both corti ..."
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Cited by 105 (2 self)
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The temporal characteristics of the BOLD response in sensorimotor and auditory cortices were measured in subjects performing finger tapping while listening to metronome pacing tones. A repeated trial paradigm was used with stimulus durations of 167 ms to 16 s and intertrial times of 30 s. Both cortical systems were found to be nonlinear in that the response to a long stimulus could not be predicted by convolving the 1s response with a rectangular function. In the shorttime regime, the amplitude of the response varied only slowly with stimulus duration. It was found that this character was predicted with a modification to Buxton’s balloon model. Wiener deconvolution was used to deblur the response to concatenated short episodes of finger tapping at different temporal separations and at rates from 1 to 4 Hz. While the measured response curves were distorted by overlap between the individual episodes, the deconvolved response at each rate was found to agree well with separate scans at each of the individual rates. Thus, although the impulse response cannot predict the response to fully overlapping stimuli, linear deconvolution is effective when the stimuli are separated by at least 4 s. The deconvolution filter must be measured for each subject using a shortstimulus paradigm. It is concluded that deconvolution may be effective in diminishing the hemodynamically imposed temporal blurring and may have potential applications in quantitating responses in eventrelated fMRI. � 1999 Academic Press
Characterizing the hemodynamic response: Effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. NeuroImage 11: 735–759
, 2000
"... Rapidpresentation eventrelated functional MRI (ERfMRI) allows neuroimaging methods based on hemodynamics to employ behavioral task paradigms typical of cognitive settings. However, the sluggishness of the hemodynamic response and its variance provide constraints on how ERfMRI can be applied. In ..."
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Cited by 72 (9 self)
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Rapidpresentation eventrelated functional MRI (ERfMRI) allows neuroimaging methods based on hemodynamics to employ behavioral task paradigms typical of cognitive settings. However, the sluggishness of the hemodynamic response and its variance provide constraints on how ERfMRI can be applied. In a series of two studies, estimates of the hemodynamic response in or near the primary visual and motor cortices were compared across various paradigms and sampling procedures to determine the limits of ERfMRI procedures and, more generally, to describe the behavior of the hemodynamic response. The temporal profile of the hemodynamic response was estimated across overlapping events by solving a set of linear equations within the general linear model. No
Imaging brain dynamics using independent component analysis
 Proceedings of the IEEE
"... The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signal ..."
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Cited by 50 (22 self)
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The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain. Keywords—Blind source separation, EEG, fMRI, independent component analysis.
Bayesian fMRI time series analysis with spatial priors
 NeuroImage
, 2005
"... We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. F ..."
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Cited by 38 (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 (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, using a computationally efficient Variational Bayes framework, we are able to let the data determine the optimal amount of smoothing. We assume an arbitrary order AutoRegressive (AR) model for the errors. Our model generalizes earlier work on voxelwise estimation of GLMAR models and inference in GLMs using Posterior Probability Maps (PPMs). Results are shown on simulated data and on data from an eventrelated fMRI experiment.
Finding the Self? An EventRelated fMRI Study
"... & Researchers have long debated whether knowledge about the self is unique in terms of its functional anatomic representation within the human brain. In the context of memory function, knowledge about the self is typically remembered better than other types of semantic information. But why does this ..."
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Cited by 35 (4 self)
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& Researchers have long debated whether knowledge about the self is unique in terms of its functional anatomic representation within the human brain. In the context of memory function, knowledge about the self is typically remembered better than other types of semantic information. But why does this memorial effect emerge? Extending previous research on this topic (see Craik et al., 1999), the present study used eventrelated functional magnetic resonance imaging to investigate potential neural substrates of selfreferential processing. Participants were imaged while making judgments about trait adjectives under three experimental conditions (selfrelevance, otherrelevance, or case judgment). Relevance judgments, when compared to case judgments, were accompanied by activation of the left inferior frontal cortex and the anterior cingulate. A separate region of the medial prefrontal cortex was selectively engaged during selfreferential processing. Collectively, these findings suggest that selfreferential processing is functionally dissociable from other forms of semantic processing within the human brain. &
The effect of normal aging on the coupling of neural activity to the bold hemodynamic response. Neuroimage
, 1999
"... The use of functional neuroimaging to test hypotheses regarding agerelated changes in the neural substrates of cognitive processes relies on assumptions regarding the coupling of neural activity to neuroimaging signal. Differences in neuroimaging signal response between young and elderly subjects c ..."
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Cited by 31 (0 self)
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The use of functional neuroimaging to test hypotheses regarding agerelated changes in the neural substrates of cognitive processes relies on assumptions regarding the coupling of neural activity to neuroimaging signal. Differences in neuroimaging signal response between young and elderly subjects can be mapped directly to differences in neural response only if such coupling does not change with age. Here we examined spatial and temporal characteristics of the BOLD fMRI hemodynamic response in primary sensorimotor cortex in young and elderly subjects during the performance of a simple reaction time task. We found that 75 % of elderly subjects (n � 20) exhibited a detectable voxelwise relationship with the behavioral paradigm in this region as compared to 100 % young subjects (n � 32). The median number of suprathreshold voxels in the young subjects was greater than four times that of the elderly subjects. Young subjects had a slightly greater signal:noise per voxel than the elderly subjects that was attributed to a greater level of noise per voxel in the elderly subjects. The evidence did not support the idea that the greater head motion observed in the elderly was the cause of this greater voxelwise noise. There were no significant differences between groups in either the shape of the hemodynamic response or in its the withingroup variability, although the former evidenced a near significant trend. The overall finding that some aspects of the hemodynamic coupling between neural activity and BOLD fMRI signal change with age cautions against simple interpretations of the results of imaging studies that compare young and elderly subjects.
Detecting Latency Differences in EventRelated BOLD Responses: Application To Words versus . . .
 NEUROIMAGE
, 2002
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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 28 (11 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 25 (2 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.
Stochastic designs in eventrelated fMRI
 Neuroimage
, 1999
"... This article considers the efficiency of eventrelated fMRI designs in terms of the optimum temporal pattern of stimulus or trial presentations. The distinction between ‘‘stochastic’ ’ and ‘‘deterministic’ ’ is used to distinguish between designs that are specified in terms of the probability that a ..."
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Cited by 25 (0 self)
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This article considers the efficiency of eventrelated fMRI designs in terms of the optimum temporal pattern of stimulus or trial presentations. The distinction between ‘‘stochastic’ ’ and ‘‘deterministic’ ’ is used to distinguish between designs that are specified in terms of the probability that an event will occur at a series of time points (stochastic) and those in which events always occur at prespecified time (deterministic). Stochastic designs may be ‘‘stationary,’ ’ in which the probability is constant, or nonstationary, in which the probabilities change with time. All these designs can be parameterized in terms of a vector of occurrence probabilities and a prototypic design matrix that embodies constraints (such as the minimum stimulus onset asynchrony) and the model of hemodynamic responses. A simple function of these parameters is presented and used to compare the relative efficiency of different designs. Designs with slow modulation of occurrence probabilities are generally more efficient than stationary designs. Interestingly the most efficient design is a conventional block design. A critical point, made in this article, is that the most efficient design for one effect may not be the most efficient for another. This is particularly important when considering evoked responses and the differences among responses. The most efficient designs for evoked responses, as opposed to differential responses, require trialfree periods during which baseline levels can be attained. In the context of stochastic, rapidpresentation designs this is equivalent to the inclusion of ‘‘null events.’’