Reconstruction of electric fields and source distributions in EEG brain imaging

In this thesis, three different approaches are developed for the estimation of focal brain activity using EEG measurements. The proposed approaches have been tested and found feasible using simulated data. First, we develop a robust solver for the recovery of focal dipole sources. The solver uses a weighted dipole strength penalty term (also called weighted L1,2 norm) as prior information in order to ensure that the sources are sparse and focal, and that both the source orientation and depth bias are reduced. The solver is based on the truncated Newton interior point method combined with a logarithmic barrier method for the approximation of the penalty term. In addition, we use a Bayesian framework to derive the depth weights in the prior that are used to reduce the tendency of the solver to favor superficial sources. In the second approach, vector field tomography (VFT) is used for the estimation of underlying electric fields inside the brain from external EEG measurements. The electric field is reconstructed using a set of line integrals. This is the first time that VFT has been used for the recovery of fields when the dipole source lies inside the domain of reconstruction. The benefit of this approach is that we do not need a mathematical model for the sources. The test cases indicated that the approach can accurately localize the source activity. In the last part of the thesis, we show that, by using the Bayesian approximation error approach (AEA), precise knowledge of the tissue conductivities and head geometry are not always needed. We deliberately use a coarse head model and we take the typical variations in the head geometry and tissue conductivities into account statistically in the inverse model. We demonstrate that the AEA results are comparable to those obtained with an accurate head model.Open Acces

Imaging functional and structural networks in the human epileptic brain

Epileptic activity in the brain arises from dysfunctional neuronal networks involving cortical and subcortical grey matter as well as their connections via white matter fibres. Physiological brain networks can be affected by the structural abnormalities causing the epileptic activity, or by the epileptic activity itself. A better knowledge of physiological and pathological brain networks in patients with epilepsy is critical for a better understanding the patterns of seizure generation, propagation and termination as well as the alteration of physiological brain networks by a chronic neurological disorder. Moreover, the identification of pathological and physiological networks in an individual subject is critical for the planning of epilepsy surgery aiming at resection or at least interruption of the epileptic network while sparing physiological networks which have potentially been remodelled by the disease. This work describes the combination of neuroimaging methods to study the functional epileptic networks in the brain, structural connectivity changes of the motor networks in patients with localisation-related or generalised epilepsy and finally structural connectivity of the epileptic network. The combination between EEG source imaging and simultaneous EEG-fMRI recordings allowed to distinguish between regions of onset and propagation of interictal epileptic activity and to better map the epileptic network using the continuous activity of the epileptic source. These results are complemented by the first recordings of simultaneous intracranial EEG and fMRI in human. This whole-brain imaging technique revealed regional as well as distant haemodynamic changes related to very focal epileptic activity. The combination of fMRI and DTI tractography showed subtle changes in the structural connectivity of patients with Juvenile Myoclonic Epilepsy, a form of idiopathic generalised epilepsy. Finally, a combination of intracranial EEG and tractography was used to explore the structural connectivity of epileptic networks. Clinical relevance, methodological issues and future perspectives are discussed

Expanding the applicability of magnetoencephalography

Magnetoencephalography (MEG) offers a unique way to non-invasively monitor the neural activity in the human brain. MEG is based on measuring the very weak magnetic fields generated by the electric currents in the active neurons. Such measurements allow, with certain limitations, estimating the underlying current distribution and thus the locations and time courses of the neural generators with an excellent temporal resolution. The aim of this Thesis was to advance MEG to certain realms that have been considered difficult or even impossible for it. Specifically, the included studies contributed to the modelling of the neural generators, detection of activity in the deep brain areas, analysis of oscillatory activity, and characterisation of neural states related to bistable perception. Estimating the sources of MEG signals is non-trivial as multiple current constellations can give rise to the same observed magnetic fields. As a new solution to this problem, we introduced an automatic Bayesian tracking algorithm that recovers the locations and time courses of a set of focal neural current sources from MEG data. The majority of MEG experiments have concentrated on brain signals originating in the neocortex due to the rapid decrease of the MEG signals as a function increasing source depth. Here, we demonstrated that neural activity deep in the brainstem can be detected and accurately localised by MEG in favourable conditions. We also explored the utility of stochastic resonance in varying the salience of a cognitive stimulus, and showed that the detection accuracy of visually-presented words correlated better with the amplitudes of the late than early responses. The temporal resolution provided by MEG was exploited in novel ways. We showed that oscillatory 20-Hz signals from the primary and secondary somatosensory cortex were transiently phase-locked in response to a stimulus, possibly signifying functional connectivity. We also introduced a frequency-tagging method employing dynamical noise to separate brain activations elicited by different parts of a visual scene: monitoring these rhythmic signals with MEG enabled us to probe the neural engagement in the early visual brain areas during bistable perception and thus to link subjective perceptual states to brain states

IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Peer reviewe