Biomedical subjects

Stefan J Kiebel

Publications and source records attributed to Stefan J Kiebel.

10 recordsLinked to original sources

PubMed2006

Dynamic causal modeling: a generative model of slice timing in fMRI.

Dynamic causal modeling (DCM) of functional magnetic resonance imaging (fMRI) data allows one to make inferences about the architecture of distributed networks in the brain, in terms of effective connectivity. fMRI data are usually acquired using echo planar imaging (EPI). EPI sequences typically acquire slices at different times over a few seconds. DCM, in its original inception, was not informed about these slice timings and assumed that all slices were acquired simultaneously. It has been shown that DCM can cope with slice timing differences of up to 1 s. However, many fMRI studies employ a repetition time (TR) of 3 to 5 s, which precludes a straightforward DCM of these data. We show that this limitation can be overcome easily by including slice timing in the DCM. Using synthetic data we show that the extended DCM furnishes veridical posterior means, even if there are large slice-timing differences. Model comparisons show that, in general, the extended DCM out-performs the original model. We contrast the modeling of slice timing, in the context of DCM, with the less effective approach of 'slice-timing correction', prior to modeling. We apply our procedure to real data and show that slice timings are important parameters. We conclude that, generally, one should use DCM with slice timing.

Humans↗

PubMed2006

Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.

Dynamical causal modeling (DCM) of evoked responses is a new approach to making inferences about connectivity changes in hierarchical networks measured with electro- and magnetoencephalography (EEG and MEG). In a previous paper, we illustrated this concept using a lead field that was specified with infinite prior precision. With this prior, the spatial expression of each source area, in the sensors, is fixed. In this paper, we show that using lead field parameters with finite precision enables the data to inform the network's spatial configuration and its expression at the sensors. This means that lead field and coupling parameters can be estimated simultaneously. Alternatively, one can also view DCM for evoked responses as a source reconstruction approach with temporal, physiologically informed constraints. We will illustrate this idea using, for each area, a 4-shell equivalent current dipole (ECD) model with three location and three orientation parameters. Using synthetic and real data, we show that this approach furnishes accurate and robust conditional estimates of coupling among sources and their orientations.

Auditory Perception↗

PubMed2006

Dynamic causal modeling of evoked responses in EEG and MEG.

Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.

Auditory Pathways↗

PubMed2005

Applications of random field theory to electrophysiology.

The analysis of electrophysiological data often produces results that are continuous in one or more dimensions, e.g., time-frequency maps, peri-stimulus time histograms, and cross-correlation functions. Classical inferences made on the ensuing statistical maps must control family wise error (FWE) when searching across the map's dimensions. In this paper, we borrow multiple comparisons procedures, established in neuroimaging, and apply them to electrophysiological data. These procedures use random field theory (RFT) to adjust p-values from statistics that are functions of time and/or frequency. This RFT adjustment for continuous statistical processes plays the same role as a Bonnferonni adjustment in the context of discrete statistical tests. Here, by analysing the time-frequency decompositions of single channel EEG data we show that RFT adjustments can be used in the analysis of electrophysiological data and illustrate the advantages of this method over existing approaches.

Animals↗

PubMed2005

Parametric analysis of oscillatory activity as measured with EEG/MEG.

We assess the suitability of conventional parametric statistics for analyzing oscillatory activity, as measured with electroencephalography/magnetoencephalography (EEG/MEG). The approach we consider is based on narrow-band power time-frequency decompositions of single-trial data. The ensuing power measures have a chi(2)-distribution. The use of the general linear model (GLM) under normal error assumptions is, therefore, difficult to motivate for these data. This is unfortunate because the GLM plays a central role in classical inference and is the standard estimation and inference framework for neuroimaging data. The key contribution of this work is to show that, in many circumstances, one can appeal to the central limit theorem and assume normality for generative models of power. If this is not appropriate, one can transform the data to render the error terms approximately normal. These considerations allow one to analyze induced and evoked oscillations using standard frameworks like statistical parametric mapping. We establish the validity of parametric tests using synthetic and real data and compare its performance to established nonparametric procedures.

Algorithms↗

PubMed2004

Brain responses to the acquired moral status of faces.

We examined whether neural responses associated with judgments of socially relevant aspects of the human face extend to stimuli that acquire their significance through learning in a meaningful interactive context, specifically reciprocal cooperation. During fMRI, subjects made gender judgments on faces of people who had been introduced as fair (cooperators) or unfair (defector) players through repeated play of a sequential Prisoner's Dilemma game. To manipulate moral responsibility, players were introduced as either intentional or nonintentional agents. Our behavioral (likebility ratings and memory performance) as well as our imaging data confirm the saliency of social fairness for human interactions. Relative to neutral faces, faces of intentional cooperators engendered increased activity in left amygdala, bilateral insula, fusiform gyrus, STS, and reward-related areas. Our data indicate that rapid learning regarding the moral status of others is expressed in altered neural activity within a system associated with social cognition.

Adult↗

PubMed2004

The MR detection of neuronal depolarization during 3-Hz spike-and-wave complexes in generalized epilepsy.

Previously, an analysis of activations observed in a patient with idiopathic generalized epilepsy using electroencephalogram-correlated functional magnetic resonance imaging (MRI) during runs of 3-Hz generalized spike-wave discharge (GSWD) was presented by Salek-Haddadi. Time-locked, bilateral, thalamic blood oxygenation level-dependent increases were reported to be accompanied by widespread, symmetric, cortical deactivation with a frontal maximum. In light of recent investigations into MRI detection of the magnetic field perturbations caused by neuronal current loops during depolarization, we revisited the analysis of the data of Salek-Haddadi as a preliminary search for a neuroelectric signal. We modeled the MRI response as the sum of a fast signal and a slower signal and demonstrated significant MRI activity at a time scale of the order of 30 ms associated with GSWDs. Further work is necessary before firm conclusions may be drawn about the nature of this signal.

Cerebral Cortex↗

PubMed2004

Statistical parametric mapping for event-related potentials: I. Generic considerations.

In this paper, we frame the strategy and motivations behind developments in statistical parametric mapping (SPM) for the analysis of electroencephalogram (EEG) data. This work deals specifically with SPM procedures for the analysis of event-related potentials (ERP). We place these developments in the larger context of integrating electrophysiological and hemodynamic measurements of evoked brain responses through the fusion of EEG and fMRI data. In this paper, we consider some fundamental issues when selecting an appropriate statistical model that enables diverse questions to be asked of the data and at the same time retains maximum sensitivity. The three key issues addressed in this paper are as follows: (i) should multivariate or mass univariate analyses be adopted, (ii) should time be treated as an experimental factor or as a dimension of the measured response variable, and (iii) how to form appropriate explanatory variables in a hierarchical observation model. We review the relative merits of the different options and explain the rationale for our choices. In brief, we motivate a mass univariate approach in terms of sensitivity to region-specific responses. This involves modeling responses at each voxel or space bin separately. In contradistinction, we treat time as an experimental factor to enable inferences about temporally distributed responses that encompass multiple time bins. In a companion paper, we develop statistical models of ERPs in the time domain that follow from the heuristics established here and illustrate the approach using simulated and real data.

Analysis of Variance↗

PubMed2004

Statistical parametric mapping for event-related potentials (II): a hierarchical temporal model.

In this paper, we describe a temporal model for event-related potentials (ERP) in the context of statistical parametric mapping (SPM). In brief, we project channel data onto a two-dimensional scalp surface or into three-dimensional brain space using some appropriate inverse solution. We then treat the spatiotemporal data in a mass-univariate fashion. This implicitly factorises the model into spatial and temporal components. The key contribution of this paper is the use of observation models that afford an explicit distinction between observation error and variation in the expression of ERPs. This distinction is created by employing a two-level hierarchical model, in which the first level models the ERP effects within-subject and trial type, while the second models differences in ERP expression among trial types and subjects. By bringing the analysis of ERP data into a classical hierarchical (i.e., mixed effects) framework, many apparently disparate approaches (e.g., conventional P300 analyses and time-frequency analyses of stimulus-locked oscillations) can be reconciled within the same estimation and inference procedure. Inference proceeds in the normal way using t or F statistics to test for effects that are localised in peristimulus time or in some time-frequency window. The use of F statistics is an important generalisation of classical approaches, because it allows one to test for effects that lie in a multidimensional subspace (i.e., of unknown but constrained form). We describe the analysis procedures, the underlying theory and compare its performance to established techniques.

Analysis of Variance↗

PubMed2003

A heuristic for the degrees of freedom of statistics based on multiple variance parameters.

In neuroimaging, data are often modeled using general linear models. Here, we focus on GLMs with error covariances which are modeled as a linear combination of multiple variance/covariance components. Each of these components is weighted by one variance parameter. In many analyses variance parameters are estimated using restricted maximum likelihood (ReML). Most classical approaches assume the error covariance matrix can be factorized into a single variance parameter and a nonspherical correlation matrix. In this context, the F test based on a single variance parameter, with a suitable correction to the degrees of freedom, is the standard inference tool. This correction can also be adapted to models with multiple variance parameters. However, this extension overlooks the uncertainty about the variance parameter estimates and P values tend to be underestimated. Here, we show how one can overcome this problem to render the F test more exact. This issue is important, because serial correlations in fMRI time series are generally modeled using multiple variance parameters. Another application is to hierarchical linear models, which are used for modeling multisubject data. To illustrate our approach, we apply it to some typical modeling scenarios in fMRI data analysis.

Algorithms↗