Zoomed MRI Guided by Combined EEG/MEG Source Analysis: A Multimodal Approach for Optimizing Presurgical Epilepsy Work-up and its Application in a Multi-focal Epilepsy Patient Case Study

Abstract

In recent years, the use of source analysis based on electroencephalography (EEG) and magnetoencephalography (MEG) has gained considerable attention in presurgical epilepsy diagnosis. However, in many cases the source analysis alone is not used to tailor surgery unless the findings are confirmed by lesions, such as, e.g., cortical malformations in MRI. For many patients, the histology of tissue resected from MRI negative epilepsy shows small lesions, which indicates the need for more sensitive MR sequences. In this paper, we describe a technique to maximize the synergy between combined EEG/MEG (EMEG) source analysis and high resolution MRI. The procedure has three main steps: (1) construction of a detailed and calibrated finite element head model that considers the variation of individual skull conductivities and white matter anisotropy, (2) EMEG source analysis performed on averaged interictal epileptic discharges (IED), (3) high resolution (0.5 mm) zoomed MR imaging, limited to small areas centered at the EMEG source locations. The proposed new diagnosis procedure was then applied in a particularly challenging case of an epilepsy patient: EMEG analysis at the peak of the IED coincided with a right frontal focal cortical dysplasia (FCD), which had been detected at standard 1 mm resolution MRI. Of higher interest, zoomed MR imaging (applying parallel transmission, ‘ZOOMit’) guided by EMEG at the spike onset revealed a second, fairly subtle, FCD in the left fronto-central region. The evaluation revealed that this second FCD, which had not been detectable with standard 1 mm resolution, was the trigger of the seizures.

The effect of stimulation type, head modeling, and combined EEG and MEG on the source reconstruction of the somatosensory P20/N20 component

“…In our study, we individualized skull and brain conductivity but kept other conductivity parameters fixed at their standard constant value. Furthermore, in Aydin et al (2017) and Ramon, Garguilo, Fridgeirsson, and Haueisen, (2014), it was shown that the dura might also play a vital role, at least for EEG forward modeling. Dura has not been modeled explicitly in our head models, only implicitly by influencing our bulk calibration values.…”

“…As an outlook, based on the results of this study and on the first clinical success in presurgical epilepsy diagnosis (Aydin et al, 2015(Aydin et al, , 2017, we plan to further push forward combined EEG/MEG source analysis using detailed individualized realistic head models. To do so, our new software framework Duneuro (Nüßing, Wolters, Brinck, & Engwer, 2016;Engwer et al, 2007;Piastra et al, 2018) will enable even more accurate individualization and calibration in a more automatic way.…”

Simulating individually targeted transcranial electric stimulation for experimental application

“…Due to the complexity of the problem, the comparability between transcranial stimulation in humans and intracranial stimulation in (anesthetized) animals or in vitro recordings is limited. Due to varying tissue conductivities (Aydin et al, 2017Huang et al, 2017), effects of network electric activity (Francis et al, 2003;see Reato et al, 2013) and the effective state of the stimulated neuronal population in the behaving human participant (Alagapan et al, 2016;Neuling et al, 2013), already relatively weak electric fields might potentially modulate neuronal activity in specific cases, compared to isolated neurons or neural populations. Still, in line with previous modeling results (Datta et al, 2012;Dmochowski et al, 2011;Opitz et al, 2015;Wagner et al, 2016aWagner et al, , 2014 the present results indicate that neural modulation, in principle is possible when applying tES in human participants.…”

A Potts‐mixture spatiotemporal joint model for combined magnetoencephalography and electroencephalography data

“…Zakharova, Karpov & Bugaevskii () discuss the inverse problem and develop a solution within the context of noninvasive preoperative methods for the localization of sources which can guide the outcome of brain surgery. Aydin et al () develop an approach for source localization based on combined MEG and EEG data where the source localization is guided by a detailed and calibrated finite element head model that considers the variation of individual skull conductivities and white matter anisotropy. Lim et al () develop a method for sparse EEG/MEG source estimation based on the group lasso that has been adapted to take advantage of functionally defined regions of interest for the definition of physiologically meaningful groups within a functionally based common space.…”