Multimodal Integration: fMRI, MRI, EEG, MEG

This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specically, we look at correlative analysis, decomposition techniques, equivalent dipole tting, distributed sources modeling, beamforming, and Bayesian methods. Due to difculties in assessing ground truth of a combined signal in any realistic experiment difculty further confounded by lack of accurate biophysical models of BOLD signal we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difculties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research

Brain source imaging: from sparse to tensor models

International audienceA number of application areas such as biomedical engineering require solving an underdetermined linear inverse problem. In such a case, it is necessary to make assumptions on the sources to restore identifiability. This problem is encountered in brain source imaging when identifying the source signals from noisy electroencephalographic or magnetoencephalographic measurements. This inverse problem has been widely studied during the last decades, giving rise to an impressive number of methods using different priors. Nevertheless, a thorough study of the latter, including especially sparse and tensor-based approaches, is still missing. In this paper, we propose i) a taxonomy of the algorithms based on methodological considerations, ii) a discussion of identifiability and convergence properties, advantages, drawbacks, and application domains of various techniques, and iii) an illustration of the performance of selected methods on identical data sets. Directions for future research in the area of biomedical imaging are eventually provided

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

신경전자기 신호원의 고유특성을 고려한 신호원 복원 알고리즘

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 계산과학전공, 2013. 2. 정현교.뇌전도 및 뇌자도를 이용한 신경전자기 신호원 영상법은 분포전류원 모델의 경우, 추가적인 정보와 제한조건이 주어져야만 유일한 신호원을 복원할 수 있는 역문제이다. 본 학위 논문에서는 뇌전도 및 뇌자도를 이용한 신호원 영상법의 정확도를 향상시키기 위한 새로운 방법을 제안한다. 뇌자도는 대뇌피질상에 존재하는 반지름 방향의 신호원에 둔감한 반면 뇌전도는 뇌자도에 비해 상대적으로 방향성에 큰 영향을 받지 않는 것으로 알려져 있다. 이러한 신호원 고유의 방향 특성은 현재까지 분포전류원 모델의 신호원 추정에 적용되지 않았다. 본 학위 논문에서는 뇌전도와 뇌자도를 동시 측정한 경우에 대해 신호원의 방향성을 고려해 대뇌피질 상에 존재하는 신호원을 복원하는 방법을 제안하였다. 기존의 뇌전도/뇌자도 신호원 영상법을 통해 복원된 신호원은 실제 신호원과 비교했을 때 한점에 집중되거나 넓은 영역에 퍼져 있다. 따라서 다양한 분포 형태를 가진 신호원의 경우 기존 복원법을 통해서는 신호원의 분포 형태를 추정하기 힘들다는 단점이 있었다. 본 학위 논문에서는 신호원의 최대값을 추정해 이러한 한계를 극복하여 신호원의 분포를 복원할 수 있는 새로운 신호원 영상법을 제안하였다. 제안된 방법들을 다양한 상황의 시뮬레이션을 통해 정확도를 평가했으며 간질환자의 데이터에 적용해 수술로 제거된 뇌부위와 뇌자도를 이용해 복원된 신호원의 위치와 분포영역을 비교하였다. 그 결과, 본 논문에서 제안한 방법들은 기존 방법에 비해 뇌자도 및 뇌전도의 국지화 정확도를 향상시켰 수 있었으며 앞으로 뇌영역 활성부위를 추정하는 의학 분야 및 역문제 연구에서 널리 사용될 것으로 기대된다.The functional imaging of neuroelectromagnetic sources of electroencephalographic (EEG) and magnetoencephalographic (MEG) based on distributed source models requires additional information and constraints on the source in order to overcome the ill-posedness and to obtain a plausible solution. In this dissertation, we present two methods to enhance accuracy of MEG and EEG source reconstruction. We propose a new cortical source imaging algorithm for integrating simultaneously recorded EEG and MEG, which takes into account the different sensitivity characteristics of the two modalities with respect to cortical source orientations. It is well known that MEG cannot reliably detect neuronal sources with radial orientation, whereas EEG is relatively less dependent on the source orientations than MEG. However, this intrinsic difference has not previously been taken into account in the integrative cortical source imaging using simultaneously recorded EEG and MEG data. On the other hands, most imaging algorithms explicitly favor either spatially more focal or diffuse current source patterns. Naturally, in a situation where both focal and extended sources are present or the source is arbitrary distributed, such reconstruction algorithms may yield inaccurate estimate. The other algorithm proposed in this dissertation improves accuracy of bio-electromagnetic source estimation regardless the extension of source distribution. The additional maximum amplitude constraint does successively enhance the localization accuracy in EEG/MEG source imaging. The proposed approaches are validated through numerical simulations and applied to practical epilepsy measurements and compared to the resection region. From the extensive analysis, it will be shown that the proposed approaches can enhance the source localization accuracy considerably, compared to the conventional approaches. Therefore the proposed methods in this dissertation are expected to be a promising approach on the research of inverse problem and many clinical applications of EEG and MEG.Abstracts 1 Contents 3 List of Tables 5 List of Figures 6 List of Symbols 8 1. Introduction 9 1.1 Motivation and Aim 9 1.2 Overview of Chapters 14 2. Basics of Functional Neuroimaging 16 2.1 Functional Neuroimaging 16 2.2 Measurment of EEG and MEG 19 2.2.1 EEG 19 2.2.2 MEG 22 2.3 Anatomy of Human Brain 24 2.4 Generation of Neuroelectromagnetic Fields 29 3. Forward and Inverse Problems 31 3.1 Neuroelectromagnetic Forward Problem 31 3.1.1 Quasi-Static Approximation 31 3.1.2 Analytic Formulation 32 3.1.3 Numerical Approach 35 3.1.4 Linearization of Forward Problem 38 3.2 Neuroelectromagnetic Inverse Problem 39 3.2.1 Distributed Source Model 39 3.2.2 L2 Norm Mminimization Approach 40 3.2.3 L1 Norm Minimization Approach 42 4. Preprocessing and Quantitative Evalution Metrics 43 4.1 Preprosessing 43 4.2 Techniques of Quantification of Distributed Source 46 5. Algorithm Considering Directional Characteristics 56 5.1 Proposed Algorithm 56 5.2 Numerical Experiment of Proposed Method 63 6. Algorithm Considering the Maximum Current Density 70 6.1 Proposed Algorithm 70 6.2 Numerical Experiment of Proposed Method 72 6.3 Application to Localization of Epileptic Zone 84 7. Conclsion 89 References 92 Appendix A. Derivation of L2 Norm Minimization Problem 100 Appendix B. Derivation of Directional Inverse Operators 105 Appendix C. Derivation of L1 Norm Minimization Problem 107 Abstract (in Korean) 110Docto

Review on solving the inverse problem in EEG source analysis

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided

Combined EEG and MEG source analysis of epileptiform activity using calibrated realistic finite element head models

In dieser Arbeit wird eine neue Pipeline, welche die komplementären Informationen der Elektroenzephalographie (EEG) und Magnetoenzephalographie (MEG) berücksichtigen kann, vorgestellt und experimentell sowie methodisch analysiert. Um das Vorwärtsproblem zu lösen, wird ein hochrealistisches Finite-Elemente-Kopfmodell aus individuell gemessenen T1-gewichteten, T2-gewichteten und Diffusion-Tensor (DT)-MRIs generiert. Dafür werden die Kompartments Kopfhaut, spongioser Schädel, kompakter Schädel, Liquor Cerebrospinalis (CSF), graue Substanz und weiße Substanz segmentiert und ein individuelles Kopfmodell erstellt. Um eine sehr akkurate Quellenanalyse zu garantieren werden die individuelle Kopfform, die Anisotropie der weißen Substanz und die individuell kalibrierte Schädelleitfähigkeiten berücksichtigt. Die Anisotropie der weißen Substanz wird anhand der gemessenen DT-MRI Daten berechnet und in das segmentierte Kopfmodell integriert. Da sich die Leitfähigkeit des schwach-leitenden Schädels für verschiedene Probanden sehr stark unterscheidet und diese die Ergebnisse der EEG Quellenanalyse stark beeinflusst, wird ein Fokus auf die Untersuchung der Schädelleitfähigkeit gelegt. Um die individuelle Schädelleitfähigkeit möglichst genau zu bestimmen werden simultan gemessene somatosensorische Potentiale und Felder der Probanden verwendet und ein Verfahren zur Kalibrierung der Schädelleitfähigkeit durchgeführt. Wie in dieser Studie gezeigt, können individuell generierte Kopfmodelle dazu verwendet werden um, in einem nicht-invasivem Verfahren, interiktale Aktivität für Patienten, welche an medikamentenresistenter Epilepsie leiden, mit einer sehr hohen Genauigkeit zu detektieren. Außerdem werden diese akkuraten Kopfmodelle dazu verwendet um die unterschiedlichen Sensitivitäten von EEG, MEG und einer kombinierten EEG und MEG (EMEG) Quellenanalyse in Bezug auf verschiedene Gewebeleitfähigkeiten zu untersuchen. Wie in dieser Studie gezeigt wird liefert eine kombinierte EMEG Quellenanalyse zuverlässigere und robustere Ergebnisse für die Lokalisierung epileptischer Aktivität als eine einfache EEG oder MEG Quellenanalyse. Zuletzt werden die Auswirkungen einer Spikemittelung sowie die Effekte verschiedener Signal-Rausch-Verhältnisse (SNRs) anhand verschiedener Teilmittelungen untersucht. Wie in dieser Arbeit gezeigt wird sind realistische Kopfmodelle mit anisotroper weißer Substanz und kalibrierter Schädelleitfähigkeit nicht nur für die EEG Quellenanalyse, sondern auch für die MEG und EMEG Quellenanalyse vorteilhaft. Durch die Anwendung dieser akkuraten Kopfmodelle konnte gezeigt werden, dass EMEG Quellenanalyse sehr gute Quellenrekonstruktionen auch schon zu Beginn des epileptischen Spikes liefert, wo nur eine sehr geringe SNR vorhanden ist. Da zu diesem Zeitpunkt noch keine Ausbreitung der epileptischen Aktivität eingesetzt hat ist die Lokalisation von frühen Quellen von besonderer Bedeutung. Während die EMEG Quellenanalyse auch Ausbreitungseffekte für spätere Zeitpunkte genau darstellen kann, können einfache EEG oder MEG Quellenanalysen diese nicht oder nur teilweise darstellen. Die Validierung der Ausbreitung wird anhand eines invasiv gemessenen Stereo-EEG durchgeführt. Durch die durchgeführten Spikemittelungen und die SNR Analyse wird verdeutlicht, dass durch eine Teilmittelung wichtige und exakte Informationen über den Mittelpunkt sowie die Größe des epileptischen Gewebes gewonnen werden können, welche weder durch eine einfachen noch einer "Grand-average" Lokalisation des Spikes erreichbar sind. Eine weitere Anwendung einer genauen EMEG Quellenanalyse ist die Bestimmung einer "region of interest" anhand von standardisierten MRT Messungen. Diese kleinen Gebiete werden dann später mit einer optimalen und höher aufgelösten MRT-Sequenz gemessen. Dank dieses optimierte Verfahren können auch sehr kleine FCDs entdeckt werden, welche auf dem standardisierten gemessenen MRT-Sequenzen nicht erkennbar sind. Die Pipeline, welche in dieser Arbeit entwickelt wird, kann auch für gesunde Probanden angewendet werden. In einer ersten Studie wird eine Quellenanalyse der somatosensorischen und auditorisch-induzierten Reize durchgeführt. Die gewonnen Daten werden mit anderen Studien vergleichen und mögliche Gemeinsamkeiten diskutiert. Eine weitere Anwendung der realistischen Kopfmodelle ist die Untersuchung von Volumenleitungseffekten in nicht-invasiven Hirnstimulationsmethoden wie transkranielle Gleichstromstimulation und transkranielle Magnetstromstimulation.In this thesis, a new experimental and methodological analysis pipeline for combining the complementary information contained in electroencephalography (EEG) and magnetoencephalography (MEG) is introduced. The forward problem is solved using high resolution finite element head models that are constructed from individual T1 weighted, T2 weighted and diffusion tensor (DT-) MRIs. For this purpose, scalp, skull spongiosa, skull compacta, cerebrospinal fluid, white matter (WM) and gray matter (GM) are segmented and included into the head models. In order to obtain highly accurate source reconstructions, the realistic geometry, tissue conductivity anisotropy (i.e., WM tracts) and individually estimated conductivity values are taken into account. To achieve this goal, the brain anisotropy is modeled using the information obtained from DT-MRI. A main focus is placed on the skull conductivity due to its high inter-individual variance and different sensitivities of EEG and MEG source reconstructions to it. In order to estimate individual skull conductivity values that fit best to the constructed head models, simultaneously acquired somatosensory evoked potential and field data measured for the same individuals are analyzed. As shown in this work, the constructed head models could be used to non-invasively localize interictal spike activity in patients suffering from pharmaco-resistant focal epilepsy with higher reliability. In addition, by using these advanced head models, tissue sensitivities of EEG, MEG and combined EEG/MEG (EMEG) are compared by means of altering the distinguished tissue types and their conductivities. Finally, the effects of spike averaging and signal-to-noise-ratios (SNRs) on source analysis are evaluated by localizing subaverages. The results obtained in this thesis demonstrate the importance of using anisotropic and skull conductivity calibrated realistic finite element models not only for EEG but also for MEG and EMEG source analysis. By employing such advanced finite element models, it is possible to demonstrate that EMEG achieves accurate source reconstructions at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. It is also shown that EMEG is able to reveal the propagation pathway at later time points in agreement with invasive stereo-EEG, while EEG or MEG alone reconstruct only parts of it. Spike averaging and SNR analysis reveal that subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations could supply. Moreover, it is shown that accurate source reconstructions obtained with EMEG can be used to determine a region of interest, and new MRI sequences that acquire high resolution images in this restricted area can detect FCDs that were not detectable with other MRI sequences. The pipelines proposed in this work are also tested for source analysis of somatosensory and auditory evoked responses measured from healthy subjects and the results are compared with the literature. In addition, the finite element head models are also used to assess the volume conductor effects on simulations of non-invasive brain stimulation techniques such as transcranial direct current and transcranial magnetic stimulation