Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

Network perspectives on epilepsy using EEG/MEG source connectivity

The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience

Spatial Detection of Multiple Movement Intentions from SAM-Filtered Single-Trial MEG for a high performance BCI

The objective of this study is to test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single trial magneto-encephalographic (MEG) signals. This study was performed using motor execution and motor imagery movements to achieve a potential high performance Brain-Computer interface (BCI). Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification for natural movement intentions was performed offline; Genetic Algorithm based Mahalanobis Linear Distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation. Through SAM imaging, strong and distinct event related desynchronisation (ERD) associated with sustaining, and event related synchronisation (ERS) patterns associated with ceasing of hand movements were observed in the beta band (15 - 30 Hz). The right and left hand ERD/ERS patterns were observed on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these cortical areas of high activity to correspond with the motor tasks as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51 ± 2.43 %) as well as motor imagery sessions (GA-MLD: 89.69 ± 3.34%). Thus, multiple movement intentions can be reliably detected from SAM-based spatially-filtered single trial MEG signals. MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control. This may prove tremendously helpful for patients suffering from various movement disorders to improve their quality of life

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

On mapping epilepsy : magneto- and electroencephalographic characterizations of epileptic activities

Epilepsy is one of the most common neurological disorder, affecting up to 10 individuals per 1000 persons. The disorder have been known for several thousand years, with the first clinical descriptions dating back to ancient times. Nonetheless, characterization of the dynamics underlying epilepsy remains largely unknown. Understanding these patophysiological processes requires unifying both a neurobiological perspective, as well as a technically advanced neuroimaging perspective. The incomplete insight into epilepsy dynamics is reflected by the insufficient treatment options. Approximately 30% of all patients do not respond to anti-epileptic drugs (AEDs) and thus suffers from recurrent seizures despite adequate pharmacological treatments. These pharmacoresistant patients often undergo epilepsy surgery evaluations. Epilepsy surgery aims to resect the part of the brain that generates the epileptic seizure activity (seizure onset zone, SOZ). Nonetheless, up to 50% of all patients relapse after surgery. This can be due to incomplete mapping of both the SOZ and of other structures that might be involved in seizure initiation and propagation. Such cortical and subcortical structures are collectively referred to as the epileptic network. Historically, epilepsy was considered to be either a generalized disorder involving the entire brain, or a highly localized, focal, disorder. The modern technological development of both structural and functional neuroimaging has drastically altered this view. This development has made significant contributions to the now prevailing view that both generalized and focal epilepsies arise from more or less widespread pathological network pathways. Visualization of these pathways play an important role in the presurgical planning. Thus, both improved characterization and understanding of such pathways are pivotal in improvement of epilepsy diagnostics and treatments. It is evident that epilepsy research needs to stand on two legs: Both improved understanding of pathological, neurobiological and neurophysiological process, and improved neuroimaging instrumentation. Epilepsy research do not only span from visualization to understanding of neurophysiological processes, but also from cellular, neuronal, microscopic processes, to dynamical, large-scale network processes. It is well known that neurons involved in epileptic activities exhibit specific, pathological firing patterns. Genetic mutations resulting in neuronal ion channel defects can cause severe, and even lethal, epileptic syndromes in children, clearly illustrating a role for neuron membrane properties in epilepsy. However, cellular processes themselves cannot explain how epileptic seizures can involve, and propagate across, large cortical areas and generate seizure-specific symptomatologies. A strict cellular perspective can neither explain epilepsy-associated pathological interactions between larger distant regions in between seizures. Instead, the dynamical effects of cellular synchronization across both mesoscopic and macroscopic scales also need to be considered. Today, the only means to study such effects in human subjects are by combinations of neuroimaging modalities. However, as all measurement techniques, these exhibit individual limitations that affect the kind of information that can be inferred from these. Thus, once more we reach the conclusion that epilepsy research needs to rest upon both a neurophysiological/neurobiological leg, and a technical/instrumentational leg. In accordance with this necessity of a dual approach to epilepsy, this thesis covers both neurophysiological aspects of epileptic activity development, as well as functional neuroimaging instrumentation development with focus on epileptic activity detection and localization. Part 1 (neurophysiological part) is concerned with the neurophysiological dynamical changes that underlie development of so called interictal epileptiform discharges (IEDs) with special focus on the role of low-frequency oscillations. To this aim, both conventional magnetoencephalography (MEG) and intracranial electroencephalography (iEEG) with neurostimulation is analyzed. Part 2 (instrumentation part) is concerned with development of cutting-edge, novel on-scalp magnetoencephalography (osMEG) within clinical epilepsy evaluations and research with special focus on IEDs. The theses cover both modeling of osMEG characteristics, as well as the first-ever osMEG recording of a temporal lobe epilepsy patient

Dynamic imaging of coherent sources reveals different network connectivity underlying the generation and perpetuation of epileptic seizures

The concept of focal epilepsies includes a seizure origin in brain regions with hyper synchronous activity (epileptogenic zone and seizure onset zone) and a complex epileptic network of different brain areas involved in the generation, propagation, and modulation of seizures. The purpose of this work was to study functional and effective connectivity between regions involved in networks of epileptic seizures. The beginning and middle part of focal seizures from ictal surface EEG data were analyzed using dynamic imaging of coherent sources (DICS), an inverse solution in the frequency domain which describes neuronal networks and coherences of oscillatory brain activities. The information flow (effective connectivity) between coherent sources was investigated using the renormalized partial directed coherence (RPDC) method. In 8/11 patients, the first and second source of epileptic activity as found by DICS were concordant with the operative resection site; these patients became seizure free after epilepsy surgery. In the remaining 3 patients, the results of DICS / RPDC calculations and the resection site were discordant; these patients had a poorer post-operative outcome. The first sources as found by DICS were located predominantly in cortical structures; subsequent sources included some subcortical structures: thalamus, Nucl. Subthalamicus and cerebellum. DICS seems to be a powerful tool to define the seizure onset zone and the epileptic networks involved. Seizure generation seems to be related to the propagation of epileptic activity from the primary source in the seizure onset zone, and maintenance of seizures is attributed to the perpetuation of epileptic activity between nodes in the epileptic network. Despite of these promising results, this proof of principle study needs further confirmation prior to the use of the described methods in the clinical praxis

Characterization of ictal/non-ictal EEG patterns and Neuronal Networks in Childhood Absence Epilepsy

Childhood absence epilepsy (CAE) is one of the most common pediatric epilepsy syndromes found in children. It is associated with distinct seizure semiology and clear electroencephalographic (EEG) features. In CAE patients, differentiating EEG ictal and non-ictal generalised spikes and waves discharges (GSWDs) is however difficult, since these events have an identical appearance. The differentiation of these two events is very important in a clinical setting as it has a direct effect on diagnosis and management strategies of patients. This study focuses on differentiating ictal and non-ictal discharges at sensor and source level using only surface EEG. Twelve CAE patients having both ictal and non-ictal discharges were selected for this study. For all levels of analysis, frequency ranges of 1-30 Hz containing four important frequency bands (delta, theta, alpha and beta) were used. At sensor level, spectral analysis and functional connectivity (FC) based on imaginary part of coherency, were used to evaluate the spectral changes and channel connectivity at the surface, respectively. At source level, the onset zone for ictal and non-ictal discharges were reconstructed using the eLORETA algorithm, and FC was used again to analyse the neuronal networks. Furthermore, we gave a detailed mathematical background of the EEG, forward and inverse problem, along with the mathematical foundation for the eLORETA algorithm. Additionally, for the first time we prove the correctness of the eLORETA algorithm based on the correct regularization problem. At sensor level, ictal discharges showed significantly higher power compared to non-ictal discharges, followed by FC depicting a desynchronization of channel connections (weaker connectivity) for ictal discharges. At source level, a fascinating observation was that ictal and non-ictal discharges have the same source or onset zone in the brain. However, ictal discharges had a stronger source power compared to non-ictal discharges. FC at source level revealed that the connectivity between certain brain regions and the seeds of interest (source maximum and thalamus) was stronger for ictal discharges, compared to non-ictal discharges. This study clearly shows the significant differences between ictal and non-ictal discharges at sensor and source level using only surface EEG. This study would be a great interest to clinicians, since it could be the potential foundation for future diagnostics research for CAE patients