Stephan Lau

Tabu Search Optimization of Magnetic Sensor Systems for Magnetocardiography

This paper addresses the question of optimal sensor placement for magnetocardiographic field imaging. New magnetic sensor technologies allow less restrictive sensor positioning in this application. We develop a constraint framework for sensor positioning and use tabu search (TS) and particle swarm optimization (PSO) for finding an optimal set of sensors, whereby a new PSO algorithm is designed to fit the needs of our constraint framework. Numerical simulations are carried out with a three compartment boundary element torso model and a multi-dipole heart model. We find an optimal value of about 20 to 30 vectorial sensors and both TS and PSO yield similar sensor distributions. The comparison to sensors on regular grids shows that optimization of vectorial magnetic sensor setups may significantly improve reconstruction quality and that the number of sensors can be reduced.

Nonlinear interactions of high-frequency oscillations in the human somatosensory system.

OBJECTIVE The source of somatosensory evoked high-frequency activity at about 600 Hz is still not completely clear. Hence, we aimed to study the influence of double stimulation on the human somatosensory system by analyzing both the low-frequency activity and the high-frequency oscillations (HFOs) at about 600 Hz. METHODS We used median nerve stimulation at seven interstimuli intervals (ISIs) with a high time resolution between 2.4 and 4.8 ms to investigate the N15, N20 and superimposed HFOs. Simultaneously, the electroencephalogram and the magnetoencephalogram of 12 healthy participants were recorded. Subsequently, the source analysis of precortical and cortical dipoles was performed. RESULTS The difference computations of precortical dipole activation curves showed in both the low- and high-frequency range a correlation between the ISI and the latency of the second stimulus response. The cortical low-frequency response showed a similar behavior. Contrarily, in the second response of cortical HFOs this latency shift could not be confirmed. We found amplitude fluctuations that were dependent on the ISI in the low-frequency activity and the HFOs. These nonlinear interactions occurred at ISIs, which differ by one full HFO period (1.6 ms). CONCLUSIONS Low-frequency activity and HFOs originate from different generators. Precortical and cortical HFOs are independently generated. The amplitude fluctuations dependent on ISI indicate nonlinear interference between successive stimuli. SIGNIFICANCE Information processing in human somatosensory system includes nonlinearity.

Magnetoencephalography signals are influenced by skull defects.

OBJECTIVE Magnetoencephalography (MEG) signals had previously been hypothesized to have negligible sensitivity to skull defects. The objective is to experimentally investigate the influence of conducting skull defects on MEG and EEG signals. METHODS A miniaturized electric dipole was implanted in vivo into rabbit brains. Simultaneous recording using 64-channel EEG and 16-channel MEG was conducted, first above the intact skull and then above a skull defect. Skull defects were filled with agar gels, which had been formulated to have tissue-like homogeneous conductivities. The dipole was moved beneath the skull defects, and measurements were taken at regularly spaced points. RESULTS The EEG signal amplitude increased 2-10 times, whereas the MEG signal amplitude reduced by as much as 20%. The EEG signal amplitude deviated more when the source was under the edge of the defect, whereas the MEG signal amplitude deviated more when the source was central under the defect. The change in MEG field-map topography (relative difference measure, RDM(∗)=0.15) was geometrically related to the skull defect edge. CONCLUSIONS MEG and EEG signals can be substantially affected by skull defects. SIGNIFICANCE MEG source modeling requires realistic volume conductor head models that incorporate skull defects.

Skull Defects in Finite Element Head Models for Source Reconstruction from Magnetoencephalography Signals.

Magnetoencephalography (MEG) signals are influenced by skull defects. However, there is a lack of evidence of this influence during source reconstruction. Our objectives are to characterize errors in source reconstruction from MEG signals due to ignoring skull defects and to assess the ability of an exact finite element head model to eliminate such errors. A detailed finite element model of the head of a rabbit used in a physical experiment was constructed from magnetic resonance and co-registered computer tomography imaging that differentiated nine tissue types. Sources of the MEG measurements above intact skull and above skull defects respectively were reconstructed using a finite element model with the intact skull and one incorporating the skull defects. The forward simulation of the MEG signals reproduced the experimentally observed characteristic magnitude and topography changes due to skull defects. Sources reconstructed from measured MEG signals above intact skull matched the known physical locations and orientations. Ignoring skull defects in the head model during reconstruction displaced sources under a skull defect away from that defect. Sources next to a defect were reoriented. When skull defects, with their physical conductivity, were incorporated in the head model, the location and orientation errors were mostly eliminated. The conductivity of the skull defect material non-uniformly modulated the influence on MEG signals. We propose concrete guidelines for taking into account conducting skull defects during MEG coil placement and modeling. Exact finite element head models can improve localization of brain function, specifically after surgery.

Low HRV entropy is strongly associated with myocardial infarction

Abstract Heart rate variability (HRV) is a marker of autonomous activity in the heart. An important application of HRV measures is the stratification of mortality risk after myocardial infarction. Our hypothesis is that the information entropy of HRV, a non-linear approach, is a suitable measure for this assessment. As a first step, to evaluate the effect of myocardial infarction on the entropy, we compared the entropy to standard HRV parameters. The entropy was estimated by compressing the tachogram with Bzip2. For univariate comparison, statistical tests were used. Multivariate analysis was carried out using automatically generated decision trees. The classification rate and the simplicity of the decision trees were the two evaluation criteria. The findings support our hypothesis. The meanNN-normalized entropy is reduced in patients with myocardial infarction with very high significance. One entropy parameter alone exceeds the discrimination strength of multivariate standards-based trees.

Optimal Magnetic Sensor Vests for Cardiac Source Imaging

Magnetocardiography (MCG) non-invasively provides functional information about the heart. New room-temperature magnetic field sensors, specifically magnetoresistive and optically pumped magnetometers, have reached sensitivities in the ultra-low range of cardiac fields while allowing for free placement around the human torso. Our aim is to optimize positions and orientations of such magnetic sensors in a vest-like arrangement for robust reconstruction of the electric current distributions in the heart. We optimized a set of 32 sensors on the surface of a torso model with respect to a 13-dipole cardiac source model under noise-free conditions. The reconstruction robustness was estimated by the condition of the lead field matrix. Optimization improved the condition of the lead field matrix by approximately two orders of magnitude compared to a regular array at the front of the torso. Optimized setups exhibited distributions of sensors over the whole torso with denser sampling above the heart at the front and back of the torso. Sensors close to the heart were arranged predominantly tangential to the body surface. The optimized sensor setup could facilitate the definition of a standard for sensor placement in MCG and the development of a wearable MCG vest for clinical diagnostics.

Spatiotemporal correlation analyses: a new procedure for standardisation of DC magnetocardiograms.

Abstract There is a lack of standard methods for the analysis of magnetocardiograms (MCGs). MCG signals have a shape similar to the ECG (P wave, QRS complex, T wave). High-quality multichannel recordings can indicate even slight disturbances of de- and repolarisation. The purpose of our study was to apply a new approach in the analysis of signal-averaged DC-MCGs. DC-MCGs (31-channel) were recorded in 182 subjects: 110 patients after myocardial infarction and 72 controls. Spatiotemporal correlation analysis of the QRS complex and T wave patterns throughout the entire heart cycle was used to analyse homogeneity of de- and repolarisation. These plots were compared to standard ECG analyses (electrical axis, Q wave, ST deviation, T polarity and shape). Spatiotemporal correlation analyses seem to be applicable in assessing the course of electrical repolarisation with respect to homogeneity. MCG provided all diagnostic information contained in common ECG recordings at high significance levels. The ECG patterns were included in 5/8 of our parameters for electrical axis, 6/8 for Q-wave, 7/8 for ST deviation and 5/8 for T-polarity based on two time series of correlation coefficients. We conclude that our spatiotemporal correlation approach provides a new tool for standardised analysis of cardiac mapping data such as MCG.

Corrigendum to “Magnetoencephalography signals are influenced by skull defects” [Clin. Neurophysiol. 125 (2014) 1653–1662]

Corrigendum to ‘‘Magnetoencephalography signals are influenced by skull defects” [Clin. Neurophysiol. 125 (2014) 1653–1662] S. Lau a,b,c,d,⇑, L. Flemming , J. Haueisen a,b a Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, P.O. Box 100565, D-98684 Ilmenau, Germany Biomagnetic Center, Department of Neurology, Jena University Hospital, Erlanger Allee 101, D-07747 Jena, Germany NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, Parkville, 3010, Australia Department of Medicine – St. Vincent’s Hospital, The University of Melbourne, Fitzroy 3057, Australia Department of Traumatology and Orthopedics, Robert-Koch-Hospital, Jenaer Straße 66, D-99510 Apolda, Germany

Influence of volume conductor modeling on source reconstruction in magnetoencephalography and electroencephalography

Summary form only given. The function and structure of the human brain is immensely complex and, at the same time, the key to understanding human behavior and many of todays prevailing diseases. In most cases, this system cannot be investigated directly, but only non-invasively from outside the head. Although several non-invasive measurement modalities are available, only magnetoencephalography (MEG) and electroencephalography (EEG) provide information with a high temporal resolution. In order to reconstruct the neuronal activity underlying measured EEG and MEG data both the forward problem (computing the electromagnetic field due to given sources) and the inverse problem (finding the best fitting sources to explain given data) have to be solved. The forward problem involves a source model and a model with the conductivities of the head. The conductivity model can be as simple as a homogeneously conducting sphere or as complex as a finite element model consisting of millions of elements, each with a different anisotropic conductivity tensor. The question is addressed how complex the employed forward model should be, and, more specifically, the influence of anisotropic volume conduction and the influence of conductivity inhomogeneities are evaluated. For this purpose high resolution finite element models of the rabbit and the human head are employed in combination with individual conductivity tensors to quantify the influence of white matter anisotropy on the solution of the forward and inverse problem in EEG and MEG. Although the current state of the art in the analysis of this influence of brain tissue anisotropy on source reconstruction does not yet allow a final conclusion, the results available indicate that the expected average source localization error due to anisotropic white matter conductivity might be within the principal accuracy limits of current inverse procedures. However, in some percent of the cases a considerably larger localization error might occur. In contrast, dipole orientation and dipole strength estimation are influenced significantly by anisotropy. Skull conductivity inhomogeneities such as the spongy bone structure embedded in the compact bone or surgical holes or fontanels in infants have a non-negligible effect on the EEG and MEG forward and inverse problem solution. Especially when source positions are expected to be in the vicinity of the conductivity inhomogeneity and when a large difference with respect to the skull conductivity is indicated, the modeling approach should take the inhomogeneities into account. In conclusion, models taking into account tissue anisotropy and conductivity inhomogeneities information are expected to improve source estimation procedures. Depending on the question addressed, the complexity of the forward and inverse solution approach has to be chosen.

P888: Clustering of interictal events for improved fMRI specificity in high density EEG-fMRI studies

activity without severe motion restriction. Therefore, NIRS has been applied for exploring cerebral functions in various populations. Our future research goal is to objectively evaluate the perception of facial emotion in people with profound multiple disabilities. Thus, we examined neural correlates of facial expression changes in normal adults by using NIRS. Methods: 12 female adults participated in this experiment. The three cycles of the block design composed of 40-second rest period and 30-second task period were done in each condition for NIRS measurement. Participants were asked to view passively the fruit and face images. This experiment was done in the following conditions: 1) Face recognition: a fruit image in the rest period and a neutral face in the task period. 2) Eye-closed facial expression: neutral face images in the rest period and facial expression change into eyes closed face from eyes open face (neutral face) in the task period. 3) Happy facial expression: neutral face images in the rest period and facial expression change to a happy face from a neutral face in the task period. 4) Angry facial expression: neutral face images in the rest period and facial expression change to an angry face from a neutral face in the task period. NIRS was recorded from 66 channels in frontal and bilateral temporal area. Mean values of Oxy-Hb at each channel was compared between 10-second before and 30-second during task period in all conditions. Results: Regardless of condition, Oxy-Hb significantly increased in right temporal area. On the other hand, Oxy-Hb in superior frontal area significantly decreased under all facial expression conditions, especially angry and happy face. In left inferior frontal area, a decrease of Oxy-Hb was observed in only happy face. Conclusions: Oxy-Hb decrease in frontal area might be related to facial movement since such a phenomenon was robustly observed in angry and happy face images with larger motion. On the other hand, difference of positive and negative facial expressions might be able to explain by deactivation in left inferior frontal area.