Biomedical subjects

F Kruggel

Publications and source records attributed to F Kruggel.

At least 19 recordsLinked to original sources

PubMed2001

Hemodynamic and electroencephalographic responses to illusory figures: recording of the evoked potentials during functional MRI.

The feasibility of recording event-related potentials (ERP) during functional MRI (fMRI) scanning using higher level cognitive stimuli was studied. Using responses to illusory figures in a visual oddball task, evoked potentials were obtained with their expected configurations and latencies. A rapid stimulation scheme using randomly varied trial lengths was employed, and class-wise characteristics of the hemodynamic response were obtained by a nonlinear analysis of the fMRI time series. Implications and limitations of conducting combined ERP-fMRI experiments using higher level cognitive stimuli are discussed. EEG/fMRI results revealed a sequential activation of striate and extrastriate occipital cortex along the ventral path of object processing for Kanizsa figures. Interestingly, Kanizsa figures activated the human motion area MT. Targets resulted in activations of frontal and parietal cortex which were not activated for standard stimuli.

Adult↗

PubMed2001

Improvement of source localization by dynamical systems based modeling (DSBM).

Recently, we have proposed a new concept for analyzing EEG/MEG data (Uhl et al. 1998), which leads to a dynamical systems based modeling (DSBM) of neurophysiological data. We report the application of this approach to four different classes of simulated noisy data sets, to investigate the impact of DSBM-filtering on source localization. An improvement is demonstrated of up to above 50% of the distance between simulated and estimated dipole positions compared to principal component filtered and unfiltered data. On a noise level on which two underlying dipoles cannot be resolved from the unfiltered data, DSBM allows for an extraction of the two sources.

Algorithms↗

PubMed2001

Correlation between cortical theta activity and hippocampal volumes in health, mild cognitive impairment, and mild dementia.

Cognitive decline is known to be associated with both increased theta power over frontal regions and hippocampal atrophy. The aim of this study was to reveal the relation between these parameters in groups with mild dementia, mild cognitive impairment, and healthy control subjects. The authors examined a preliminary randomly selected sample of 39 right-handed subjects joining the Leipzig Longitudinal Study of the Aged, consisting of 17 normal elderly subjects, 12 patients with mild cognitive impairment, and 10 patients with mild dementia assessed by Clinical Dementia Rating. All subjects were between 75 and 85 years old (mean age, 78 years; standard deviation, 2.78 years) and underwent EEG and brain MRI. Mean spectral power densities were calculated, and hippocampal body volume was measured. Significant negative linear correlations between theta power over frontal regions and hippocampal volumes were found. The results support the assumption about a relationship between hippocampal atrophy and theta power, and may be helpful for a better understanding of the course of Alzheimer's disease.

Aged↗

PubMed2001

Three-dimensional texture analysis of MRI brain datasets.

A method is proposed for three-dimensional (3-D) texture analysis of magnetic resonance imaging brain datasets. It is based on extended, multisort co-occurrence matrices that employ intensity, gradient and anisotropy image features in a uniform way. Basic properties of matrices as well as their sensitivity and dependence on spatial image scaling are evaluated. The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial classification tasks for pathologic findings in brain datasets.

Brain↗

PubMed2001

Segmentation of large brain lesions.

This paper describes a region-growing algorithm for the segmentation of large lesions in T1-weighted magnetic resonance (MR) images of the head. The algorithm involves a gray level similarity criterion to expand the region and a size criterion to prevent from over-growing outside the lesion. The performance of the algorithm is evaluated and validated on a series of pathologic three-dimensional MR images of the head.

Algorithms↗

PubMed2000

Recording of the event-related potentials during functional MRI at 3.0 Tesla field strength.

The feasibility of recording event-related potentials (ERP) during functional MRI (fMRI) scanning was studied. Using an alternating checkerboard stimulus in a blocked presentation, visually evoked potentials were obtained with their expected configuration and latencies. A clustered echoplanar imaging protocol was applied to observe the hemodynamic response due to the visual stimulus interleaved with measuring ERPs. Influences of the electrode/amplifier set up on MRI scanning and the scanning process on the recording of electrophysiological signals are reported and discussed. Artifacts overlaid on the electrophysiological recordings were corrected by post hoc filtering methods presented here. Implications and limitations of conducting combined ERP/fMRI experiments using higher-level cognitive stimuli are discussed. Magn Reson Med 44:277-282, 2000.

Adult↗

PubMed2000

Nonlinear regression of functional MRI data: an item recognition task study.

A classical item-recognition task was used to examine human verbal working memory in an event-related functional MRI (fMRI) study. A highly flexible experimental design incorporated a broad variation of memory load and delay time. This design allows for only three or four repetitions per stimulus condition. In a first step, linear regression analysis was applied to locate functional activation in the fMRI data. As a second step, the time course of the hemodynamic response (HR) was analyzed using nonlinear regression, which served to quantify the dependency between HR shape properties and stimulation conditions in several regions-of-interest. On the basis of this study, a closer description of the frontoparietal network involved in verbal working memory was possible.

Adult↗

PubMed2000

Detection of fixed points in spatiotemporal signals by a clustering method.

We present a method to determine fixed points in spatiotemporal signals. The method combines a clustering algorithm and a nonlinear analysis method fitting temporal dynamics. A 144-dimensional simulated signal, similar to a Kueppers-Lortz instability, is analyzed and its fixed points are reconstructed.

Algorithms↗

PubMed2000

Probabilistic modeling of single-trial fMRI data.

This paper describes a probabilistic framework for modeling single-trial functional magnetic resonance (fMR) images based on a parametric model for the hemodynamic response and Markov random field (MRF) image models. The model is fitted to image data by maximizing a lower bound on the log likelihood. The result is an approximate maximum a posteriori estimate of the joint distribution over the model parameters and pixel labels. Examples show how this technique can used to segment two-dimensional (2-D) fMR images, or parts thereof, into regions with different characteristics of their hemodynamic response.

Brain↗

PubMed1999

Modeling the hemodynamic response in single-trial functional MRI experiments.

Today, most studies of cognitive processes using functional MRI (fMRI) experiments adopt a single-trial design. Highly flexible stimulation paradigms require new statistical models in which not only the activation amount but also the time course of the measured hemodynamic response is analyzed. Most previous approaches have been based on a linear regression context and have introduced hemodynamic model functions to improve the signal detection. In this report a nonlinear regression context is derived, from which shape parameters for the hemodynamic response are obtained per trial and per region of interest. These parameters allow the investigation of stimulus-induced shape variations of the hemodynamic response. By embedding the estimation into a robust statistical framework and rigorously analyzing the spatiotemporal interactions in the fMRI data, it is possible to derive statistically valid descriptions of single hemodynamic responses. The model, estimation algorithm, validation, and an example analysis from a single-trial fMRI study are reported. Magn Reson Med 42:787-797, 1999.

Algorithms↗

PubMed1999

Comparison of filtering methods for fMRI datasets.

When studying complex cognitive tasks using functional magnetic resonance imaging (fMRI) one often encounters weak signal responses. These weak responses are corrupted by noise and artifacts of various sources. Preprocessing of the raw data before the application of test statistics helps to extract the signal and can vastly improve signal detection. Artifact sources and algorithms to handle them are discussed. In an empirical approach targeted to yield an optimal recovery of the hemodynamic response, we implemented a test bed for baseline correction and noise-filtering methods. A known signal is modulated onto foreground patches obtained from event-related fMRI experiments. Quantitative performance measures are defined to optimize the characteristics of a given filter and to compare their results. Marked improvements in the sensitivity and selectivity are achieved by optimized filtering. Examples using real data underline the usefulness of this preprocessing sequence.

Algorithms↗

PubMed1999

Alignment of magnetic-resonance brain datasets with the stereotactical coordinate system.

Neuroanatomical and neurofunctional studies are often referenced to high-resolution magnetic-resonance brain datasets. For the analysis of the cortical surface, mapping of functional information on to the cortex or visualization, it is necessary to remove the outer surfaces of the brain. For intersubject comparison, it is useful to align the dataset with a coordinate system and introduce a spatial normalization. We describe an image processing chain that combines all of these steps in an interaction-free procedure. We report on a period of 2 years of routine application of this procedure, with >250 successfully processed datasets from healthy subjects and patients with various forms of brain damage.

Brain Diseases↗

PubMed1999

Combining electrophysiological and hemodynamic measures of the auditory oddball.

The neural mechanisms of deviancy and target detection were investigated by combining high density event-related potential (ERP) recordings with functional magnetic resonance imaging (fMRI). ERP and fMRI responses were recorded using the same paradigm and the same subjects. Unattended deviants elicited a mismatch negativity (MMN) in the ERP. In the fMRI data, activations of transverse/superior temporal gyri bilateral were found. Attended deviants generated an MMN followed by an N2/P3b complex. For this condition, fMRI activations in both superior temporal gyri and the neostriatum were found. These activations were taken as neuroanatomical constraints for the localization of equivalent current dipoles. Inverse solutions for dipole orientation provide evidence for significant activation close to Heschl's gyri during deviancy processing in the 110-160-ms time interval (MMN), whereas target detection could be modeled by two dipoles in the superior temporal gyrus between 320 and 380 ms.

Acoustic Stimulation↗

PubMed1999

Temporal properties of the hemodynamic response in functional MRI.

Today, most studies of cognitive processes using functional magnetic resonance imaging (fMRI) adopt an event-related experimental design. Highly flexible stimulation settings require new statistical models where not only the activation amount, but also the time course of the measured hemodynamic response is analyzed. It is possible to obtain statistically valid descriptions of single hemodynamic responses from a robust nonlinear estimation procedure. Focus is placed on the temporal behaviour of the hemodynamic response: relative temporal order, changes induced by modification of the experimental context, and interindividual differences. Example analyses from recent fMRI studies underline the usefulness of this approach.

Adult↗

PubMed1998

Modeling hemodynamic response for analysis of functional MRI time-series.

The standard Gaussian function is proposed for the hemodynamic modulation function (HDMF) of functional magnetic resonance imaging (fMRI) time-series. Unlike previously proposed parametric models, the Gaussian model accounts independently for the delay and dispersion of the hemodynamic responses and provides a more flexible and mathematically convenient model. A suboptimal noniterative scheme to estimate the hemodynamic parameters is presented. The ability of the Gaussian function to represent the HDMF of brain activation is compared with Poisson and Gamma models. The proposed model seems valid because the lag and dispersion values of hemodynamic responses rendered by the Gaussian model are in the ranges of their previously reported values in recent optical and fMR imaging studies. An extension of multiple regression analysis to incorporate the HDMF is presented. The detected activity patterns exhibit improvements with hemodynamic correction. The proposed model and efficient parameter estimation scheme facilitated the investigation of variability of hemodynamic parameters of human brain activation. The hemodynamic parameters estimated over different brain regions and across different stimuli showed significant differences. Measurement of hemodynamic parameters over the brain during sensory or cognitive stimulation may reveal vital information on physiological events accompanying neuronal activation and functional variability of the human brain, and should lead to the investigation of more accurate and complex models.

Brain↗

PubMed1998

fMRI signal restoration using a spatio-temporal Markov Random Field preserving transitions.

In fMRI studies, Gaussian filtering is usually applied to improve the detection of activated areas. Such lowpass filtering enhances the signal to noise ratio. However, undesirable secondary effects are a bias on the signal shape and a blurring in the spatial domain. Neighboring activated areas may be merged and the high resolution of the fMRI data compromised. In the temporal domain, activation and deactivation slopes are also blurred. We propose an alternative to Gaussian filtering by restoring the signal using a spatiotemporal Markov Random Field which preserves the shape of the transitions. We define some interaction between neighboring voxels which allows us to reduce the noise while preserving the signal characteristics. An energy function is defined as the sum of the interaction potentials and is minimized using a simulated annealing algorithm. The shape of the hemodynamic response is preserved leading to a better characterization of its properties. We demonstrate the use of this approach by applying it to simulated data and to data obtained from a typical fMRI study.

Algorithms↗

PubMed1998

Spatio-temporal fMRI analysis using Markov random fields.

Functional magnetic resonance images (fMRI's) provide high-resolution datasets which allow researchers to obtain accurate delineation and sensitive detection of activation areas involved in cognitive processes. To preserve the resolution of this noninvasive technique, refined methods are required in the analysis of the data. In this paper, we first discuss the widely used methods based on a statistical parameter map (SPM) analysis exposing the different shortcomings of this approach when considering high-resolution data. First, the often used Gaussian filtering results in a blurring effect and in delocalization of the activated area. Secondly, the SPM approach only considers false alarms due to noise but not rejections of activated voxels. We propose to embed the fMRI analysis problem into a Bayesian framework consisting of two steps: i) data restoration and ii) data analysis. We, therefore, propose two Markov random fields (MRF's) to solve these two problems. Results on three protocols (visual, motor and word recognition) are shown for two SPM approaches and compared with the proposed MRF approach.

Adult↗

PubMed1998

A new concept for EEG/MEG signal analysis: detection of interacting spatial modes.

We propose a new concept for analyzing EEG/MEG data. The concept is based on a projection of the spatiotemporal signal into the relevant phase space and the interpretation of the brain dynamics in terms of dynamical systems theory. The projection is obtained by a simultaneous determination of spatial modes and coefficients of differential equations. The resulting spatiotemporal model can be characterized by stationary points and corresponding potential field maps. Brain information processing can be interpreted by attraction and repulsion of spatial field distributions given by these stationary points. This allows an objective and quantitative characterization of the brain dynamics. We outline this concept and the underlying algorithm. Results of the application of this method to an event related potential (ERP) study of auditory memory processes are discussed.

Algorithms↗