Biomedical subjects

Stefan Kiebel

Publications and source records attributed to Stefan Kiebel.

4 recordsLinked to original sources

PubMed2006

Bayesian estimation of cerebral perfusion using a physiological model of microvasculature.

Perfusion weighted MRI has proven very useful for deriving hemodynamic parameters such as CBF, CBV and MTT. These quantities are important diagnostically, e.g. in acute stroke, where they are used to delineate ischemic regions. Yet the standard method for estimating CBF based on singular value decomposition (SVD) has been demonstrated to underestimate (especially high) flow components and to be sensitive to delays in the arterial input function (AIF). Furthermore, the estimated residue functions often oscillate. This compromises their physiological interpretation/basis and makes estimation of related measures such as flow heterogeneity difficult. In this study, we estimate perfusion parameters based on a vascular model (VM) which represents heterogeneous capillary flow and explicitly leads to monotonically decreasing residue functions. We use a fully Bayesian approach to obtain posterior probability distributions for all parameters. In simulation studies, we show that the VM method has less bias in CBF estimates than the SVD based method for realistic SNRs. This also applies to cases where the AIF is delayed. We employ our method to estimate perfusion maps using data from (i) a healthy volunteer and (ii) from a stroke patient.

Bayes Theorem↗

PubMed2003

Variational Bayesian inference for fMRI time series.

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 event-related fMRI experiment.

Algorithms↗

PubMed2002

Visuomotor control within a distributed parieto-frontal network.

The aim of this functional magnetic resonance imaging study was to investigate differences in visuomotor control with increasing task complexity. Twelve right-handed volunteers were asked to perform their signature under different degrees of visual control: internally generated movement with closed eyes, signing with open eyes, tracking the line of the projected signature forwards, and tracking the line of the projected signature backwards. There was a gradual onset and disappearance of activation within a distributed network. Parietal, lateral and medial frontal brain areas were activated during all conditions, confirming the involvement of a parieto-frontal system. The weight of activation shifted with increasing task complexity. Internally generated movements activated predominantly the inferior parietal lobule and the ventral premotor cortex, as well as the rostral cingulate area, pre-supplementary motor area (pre-SMA) and SMA proper. Opening the eyes reduced SMA and cingulate activation and activated increasingly the occipito-parietal areas with higher task complexity. Visually guided movements produced an activation predominantly in the superior parietal lobule and dorsal premotor cortex. This study bridges human activation studies with the results of neurophysiological studies with monkeys. It confirms a gradual transition of visuomotor control with increasing task complexity within a distributed parieto-frontal network.

Adult↗

PubMed2002

Anatomically informed basis functions in multisubject studies.

We describe the use of anatomically informed basis functions (AIBF) in the analysis of multisubject functional imaging studies. AIBF are used to specify an anatomically informed spatial model that embodies anatomical knowledge for the statistical analysis of neuroimaging data. In a previous communication, we showed how AIBF can be used to incorporate prior anatomical constraints in single subject functional magnetic resonance image (fMRI) analyses to augment their anatomical precision. In this paper, we extend AIBF such that it can be applied to multisubject studies using fMRI or PET. The key concept is that, after spatial normalization, a canonical cortical surface can be used to generate a forward model of signal sources for all subjects. By estimating the hemodynamic signal in this canonical AIBF-space and then projecting it back into the voxel-space, one effectively extracts functional activity that is smooth, within and only within, the cortical sheet while attenuating other components unrelated to the physiological process of interest. The ensuing procedure can be considered as a highly non-stationary, anisotropic anatomically informed [de]convolution or smoothing. It is shown that this procedure offers various advantages compared to existing conventional methods for the analysis of multisubject studies, in particular it is more sensitive to underlying activations.

Algorithms↗