Anisotropic EEG/MEG volume conductor modeling based on Diffusion Tensor Imaging

Die vorliegende Arbeit befasst sich mit der Volumenleitermodellierung auf Basis der Finiten Elemente für EEG/MEG Untersuchungen unter Einbeziehung von Anistropieinformation, die mit Hilfe der Magnetresonanzdiffusionstensorbildgebung (MR-DTI) gewonnen wurde. Im ersten Teil der Arbeit wurde der Einfluss unvollständig bestimmter Wichtungsparamter (b-Matrix) auf die zu rekonstruierenden Diffusionstensoren untersucht. Die Unvollständigkeit bezieht sich dabei auf die Tatsache, dass im Allgemeinen nur die starken Diffusionsgradienten zur Berechnung der b-Matrix herangezogen werden. Es wurde gezeigt, dass besonders bei Aufnahmen mit hoher räumlicher Auflösung der Anteil der Bildgradienten an der b-Matrix nicht mehr vernachlässigbar ist. Weiterhin wurde gezeigt, wie man die b-Matrizen korrekt analytisch bestimmt und damit einen systematischen Fehler vermeidet. Für den Fall, dass nicht ausreichend Informationen zur Verfügung stehen um die analytische Bestimmung durchzuführen, wurde eine Lösung vorgeschlagen, die es mit Hilfe von Phantommessungen ermöglicht eine parametrisierte b-Matrix zu bestimmen. Der zweite Teil widmet sich der Erstellung hochaufgelöster realistischer Volumenleitermodelle detailliert beschrieben. Besonders die Transformation der Diffusionstensordaten in Leitfähigkeitstensoren. Zudem wurde eine Vorgehensweise beschrieben, die es erlaubt, einen T1-gewichteten MR-Datensatz vollautomatisch in fünf verschiedene Gewebesegmente (weiches Gewebe, graue und weiße Substanz, CSF und Schädelknochen) zu unterteilen. Der dritte Teil der Arbeit befasst sich mit dem Einfluss der anisotropen Leitfähigkeit in der weißen Hirnsubstanz auf EEG und MEG unter Verwendung eines Tier- sowie eines Humanmodells. Um den Einfluss der verschiedenen Methoden der Transformation von DTI Daten in Leitfähigkeitsdaten zu untersuchen, wurden verschiedenen Modelle sowohl mit gemessener als auch mit künstlicher Anisotropie erstellt. In der Tiermodellstudie wurden EEG und in der Humanmodellstudie EEG und MEG Simulationen sowohl mit den anisotropen Modellen als auch mit einem isotropen Modell durchgeführt und miteinander verglichen. Dabei wurde gefunden, dass sowohl der topographische Fehler (RDM) als auch der Magnitudenfehler stark durch das Einbeziehen von Anisotropieinformationen beeinflusst wird. Es wurde auch gezeigt, dass sowohl die Position als auch die Orientierung einer dipolaren Quelle in Bezug auf das anisotrope Segment einen großen Effekt auf die untersuchten Fehlermaße hat.In this work anisotropic electric tissue properties determined by means of diffusion tensor imaging were modeled into high resolution finite element volume conductors. In first part of the work the influence of not considering imaging gradient in the calculation of the b-matrices on the correct determination of diffusion tensor data is shown and it was found that especially with high resolution imaging protocols the contributions of the imaging gradients is not negligible. It was also shown how correct b-matrices considering all applied gradients can be calculated correctly. For the case that information about the sequence are missing an experimental approach of determining a parameterized b-matrix using phantom measurements is proposed. In the second part the procedure of generating anisotropic volume conductor models is regarded. The main focus of this part was to facilitate the derivation of anisotropy information from DTI measurements and the inclusion of this information into an anisotropic volume conductor. It was shown, that it is possible to generate a sophisticated high resolution anisotropic model without any manual steps into five different tissue layers. The third part studied the influence of anisotropic white matter employing an animal as well as a human model. To compare the different ways of converting the anisotropy information from DTI into conductivity information, different models were investigated, having artificial as well as measured anisotropy. In the animal study the EEG and in the human study the EEG and MEG forward solution was studies using the anisotropic models and compared to the solution derived using an isotropic model. It was found that both, the topography error (RDM) as well as the magnitude error (MAG), are significantly affected if anisotropy is considered in the volume conductor. It was also shown, that the position as well as the orientation of the dipole with respect to white matter has a large effect on the amount of the error quantities. Finally, it is claimed that if one uses high resolution volume conductor models for EEG/MEG studies, the anisotropy has to be considered, since the average error of neglecting anisotropy is larger than the accuracy which can be achieved using such models

Real-Time MEG Source Localization Using Regional Clustering

With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject’s reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements.Deutsche Forschungsgemeinschaft (grant Ba 4858/1-1)National Institutes of Health (U.S.) (grants 5R01EB009048 and 2P41EB015896)Universitätsschule Jena (J21)German Academic Exchange Servic

Simulation of tangential and radial electric brain activity: different sensitivity in EEG and MEG

Based on the main direction of the neuronal currents with respect to the local skull curvature, it is common to distinguish between tangential brain activity originating mainly from the walls of the sulci and radial brain activity originating mainly from the gyri or the bottom of the sulci. It is well known that MEG is more sensitive to tangential activity while EEG is sensitive to both radial and tangential activity. Thus, it is surprising that studies in epileptic patients report cases were spikes are visible in MEG but not in EEG. Recently, it was discussed that a lower sensitivity of MEG to background activity might be the reason for the spike visibility in MEG but not in EEG. Consequently, we analyze the signalto-noise ratio (SNR) of simulated spikes at varying orientations and with varying background activity in realistic head models. For a fixed realistic background activity, we find a higher SNR for spikes in the MEG as long as the spike orientation is not more than 30 degrees deviating from the tangential direction. Vice versa the SNR for spikes in the EEG is higher as long as the spike orientation is not more than 45 degrees deviating from the radial direction. Our simulations provide a possible explanation for the experimentally observed differences in EEG and MEG signals

Neural distinctiveness of fatigue and low sleep quality in multiple sclerosis

Background and purpose Fatigue and low sleep quality in multiple sclerosis (MS) are closely related symptoms. Here, the associations between the brain's functional connectivity (FC) and fatigue and low sleep quality were investigated to determine the degree of neural distinctiveness of these symptoms. Method A hundred and four patients with relapsing–remitting MS (age 38.9 ± 10.2 years, 66 females) completed the Modified Fatigue Impact Scale and the Pittsburgh Sleep Quality Index and underwent resting-state functional magnetic resonance imaging. FC was analyzed using independent-component analysis in sensorimotor, default-mode, fronto-parietal and basal-ganglia networks. Multiple linear regression models allowed us to test the association between FC and fatigue and sleep quality whilst controlling for one another as well as for demographic, disease-related and imaging variables. Results Higher fatigue correlated with lower sleep quality (r = 0.54, p < 0.0001). Higher fatigue was associated with lower FC of the precentral gyrus in the sensorimotor network, the precuneus in the posterior default-mode network and the superior frontal gyrus in the left fronto-parietal network, independently of sleep quality. Lower sleep quality was associated with lower FC of the left intraparietal sulcus in the left fronto-parietal network, independently of fatigue. Specific associations were found between fatigue and the sensorimotor network's global FC and between low sleep quality and the left fronto-parietal network's global FC. Conclusion Despite the high correlation between fatigue and low sleep quality in the clinical picture, our findings clearly indicate that, on the neural level, fatigue and low sleep quality in MS are associated with decreased FC in distinct functional brain networks