Biomedical subjects

Christian F Beckmann

Publications and source records attributed to Christian F Beckmann.

10 recordsLinked to original sources

PubMed2006

Scanning for the scanner: FMRI of audition by read-out omissions from echo-planar imaging.

Echo-planar imaging (EPI) generates considerable acoustic noise by rapidly oscillating gradients. In functional magnetic resonance imaging (FMRI), unshielded EPI sounds activate the auditory system inasmuch as it is responsive. Instead of attenuating EPI noise, our goal was to utilize it for auditory FMRI by omitting read-outs from the pulse sequence's gradient train. Read-out gradient pulses are the primary noise determinant of EPI introducing its peak sound level and fundamental frequency peak which inversely relates to twice the echo spacing. Using model-driven analyses, we demonstrate that withholding read-outs from EPI is suited to reliably evoke hemodynamic blood oxygenation level-dependent (BOLD) signal modulations bilaterally in the auditory cortex of normal hearing subjects (n=60). To investigate the utility of EPI read-out omissions for auditory FMRI at an individual subject's level, we compare traditional Family-Wise-Error-Rate (FWER)-corrected maximum height thresholding to spatial mixture modeling (SMM). With the latter, appropriate bilateral auditory activations were confirmed in 95% of the individuals, whereas FWER-based voxel thresholding detected such activations in up to 72%. We illustrate the applicability of this novel EPI modification for clinical diagnostic purposes and report on a patient with bilateral large vestibular aqueducts (LVAs) and severe binaural sensorineural hearing loss (SNHL). In this particular case, read-out omissions from EPI were used to assert residual audition prior to cochlear implantation (CI). Requiring no specific task compliance or sophisticated stimulation equipment other than the scanner on its own, FMRI by read-out omissions lends itself to auditory investigations and to quickly probe audition.

Acoustic Stimulation↗

PubMed2006

Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming.

This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing.

Brain Mapping↗

PubMed2005

Investigations into resting-state connectivity using independent component analysis.

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.

Brain↗

PubMed2005

Color of scents: chromatic stimuli modulate odor responses in the human brain.

Color has a profound effect on the perception of odors. For example, strawberry-flavored drinks smell more pleasant when colored red than green and descriptions of the "nose" of a wine are dramatically influenced by its color. Using functional magnetic resonance imaging, we demonstrate a neurophysiological correlate of these cross-modal visual influences on olfactory perception. Subjects were scanned while exposed either to odors or colors in isolation or to color-odor combinations that were rated on the basis of how well they were perceived to match. Activity in caudal regions of the orbitofrontal cortex and in the insular cortex increased progressively with the perceived congruency of the odor-color pairs. These findings demonstrate the neuronal correlates of olfactory response modulation by color cues in brain areas previously identified as encoding the hedonic value of smells.

Adult↗

PubMed2005

Variability in fMRI: a re-examination of inter-session differences.

We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708-734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. Finally, we show that the use of different first-level preprocessing, time-series statistics, and registration analysis methodologies can give significantly different inter-session analysis results.

Adult↗

PubMed2005

Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.

Algorithms↗

PubMed2004

Multilevel linear modelling for FMRI group analysis using Bayesian inference.

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 top-level 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 information-theoretic 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.

Bayes Theorem↗

PubMed2004

Advances in functional and structural MR image analysis and implementation as FSL.

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).

Bayes Theorem↗

PubMed2004

Probabilistic independent component analysis for functional magnetic resonance imaging.

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.

Algorithms↗

PubMed2003

General multilevel linear modeling for group analysis in FMRI.

This article discusses general modeling of multisubject and/or multisession FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made equivalent to a single complete mixed-effects model (where parameters of interest at the group level are estimated directly from all of the original single sessions' time series data) if the (co-)variance at the second level is set equal to the sum of the (co-)variances in the single-level form, using the BLUE with known covariances. This result has significant implications for group studies in FMRI, since it shows that the group analysis requires only values of the parameter estimates and their (co-)variance from the first level, generalizing the well-established "summary statistics" approach in FMRI. The simple and generalized framework allows different prewhitening and different first-level regressors to be used for each subject. The framework incorporates multiple levels and cases such as repeated measures, paired or unpaired t tests and F tests at the group level; explicit examples of such models are given in the article. Using numerical simulations based on typical first-level covariance structures from real FMRI data we demonstrate that by taking into account lower-level covariances and heterogeneity a substantial increase in higher-level Z score is possible.

Algorithms↗