Biomedical subjects

Jörn Kastner

Publications and source records attributed to Jörn Kastner.

3 recordsLinked to original sources

PubMed2004

Confidence limits of dipole source reconstruction results.

OBJECTIVE: Equivalent dipole models are widely used in electro-encephalo-graphic (EEG) and magneto-encephalo-graphic (MEG) source reconstruction. Despite their point-like definition, the best-fit solutions have a certain probability volume depending on the source position and orientation as well as on the actually used sensor set-up and the signal-to-noise ratio (SNR). In order to avoid the misleading impression of exact localization results, a measure of the SD of the dipole localization is desirable. METHODS: This measure can be obtained by performing a deviation scan around the best-fit positions, where the explainable field is determined and compared to the best-fit field. Using a linear approximation, confidence ellipsoids can then be computed and their axes and volumes can be determined by relating the field differences to the noise of the measured data. Test-dipoles inside of a 3 spherical shells volume conductor model were used to simulate EEG- and MEG-data with sources of known positions, orientations, and noise levels. Confidence ellipsoids were computed for these test-dipole solutions and deviation scans around the best-fit dipole positions were performed in order to compare the size and the shape of the confidence ellipsoids with the real error-hypersurface. SDs of repeated dipole localizations at different depths were computed to show the validity of the linear approximation over the whole eccentricity range. RESULTS: The size of the axes of the confidence ellipsoids is inversely proportional to the SNR of the measured data, thus the confidence volume is inversely proportional to the third power of the SNR. Good agreement between SDs of repeated dipole localizations and the confidence ellipsoids was found for both EEG- and MEG-cases. CONCLUSIONS: The new method adds a new and important dimension to dipole source reconstruction results by characterizing their reliability. It is also very helpful in deciding how many dipoles are necessary to explain the measured data, since superfluous dipoles exhibit rather large confidence volumes.

Algorithms↗

PubMed2004

Evaluation of sLORETA in the presence of noise and multiple sources.

The standardized Low Resolution Brain Electromagnetic Tomography method (sLORETA) can be used to compute statistical maps from EEG and MEG data that indicate the locations of the underlying source processes with low error. These maps are derived by performing a location-wise inverse weighting of the results of a Minimum Norm Least Squares (MNLS) analysis with their estimated variances. In this contribution, we evaluate the performance of the method under the presence of noise and with multiple, simultaneously active sources. It is shown that the sLORETA method localizes well, as compared to other linear approaches such as MNLS and LORETA. However, simultaneously active sources can only be separated if their fields are distinct enough and of similar strength. In the context of a strong or superficial source, weak or deep sources remain invisible, and nearby sources of similar orientation tend not to be separated but interpreted as one source located roughly in between.

Artifacts↗

PubMed2002

A standardized boundary element method volume conductor model.

OBJECTIVES: We used a 3-compartment boundary element method (BEM) model from an averaged magnetic resonance image (MRI) data set (Montreal Neurological Institute) in order to provide simple access to realistically shaped volume conductor models for source reconstruction, as compared to individually derived models. The electrode positions were transformed into the model's coordinate system, and the best fit dipole results were transformed back to the original coordinate system. The localization accuracy of the new approach was tested in a comparison with simulated data and with individual BEM models of epileptic spike data from several patients. METHODS: The standard BEM model consisted of a total of 4770 nodes, which describe the smoothed cortical envelope, the outside of the skull, and the outside of the skin. The electrode positions were transformed to the model coordinate system by using 3-5 fiducials (nasion, left and right preauricular points, vertex, and inion). The transformation consisted of an averaged scaling factor and a rigid transformation (translation and rotation). The potential values at the transformed electrode positions were calculated by linear interpolation from the stored transfer matrix of the outer BEM compartment triangle net. After source reconstruction the best fit dipole results were transformed back into the original coordinate system by applying the inverse of the first transformation matrix. RESULTS: Test-dipoles at random locations and with random orientations inside of a highly refined reference BEM model were used to simulate noise-free data. Source reconstruction results using a spherical and the standardized BEM volume conductor model were compared to the known dipole positions. Spherical head models resulted in mislocation errors at the base of the brain. The standardized BEM model was applied to averaged and unaveraged epileptic spike data from 7 patients. Source reconstruction results were compared to those achieved by 3 spherical shell models and individual BEM models derived from the individual MRI data sets. Similar errors to that evident with simulations were noted with spherical head models. Standardized and individualized BEM models were comparable. CONCLUSIONS: This new approach to head modeling performed significantly better than a simple spherical shell approximation, especially in basal brain areas, including the temporal lobe. By using a standardized head for the BEM setup, it offered an easier and faster access to realistically shaped volume conductor models as compared to deriving specific models from individual 3-dimensional MRI data.

Brain↗