Adham Elshahabi

Differences Between MEG and High-Density EEG Source Localizations Using a Distributed Source Model in Comparison to fMRI

AbstractElectroencephalography (EEG) and magnetoencephalography (MEG) are widely used to localize brain activity and their spatial resolutions have been compared in several publications. While most clinical studies demonstrated higher accuracy of MEG source localization, simulation studies suggested a more accurate EEG than MEG localization for the same number of channels. However, studies comparing real MEG and EEG data with equivalent number of channels are scarce. We investigated 14 right-handed healthy subjects performing a motor task in MEG, high-density-(hd-) EEG and fMRI as well as a somatosensory task in MEG and hd-EEG and compared source analysis results of the evoked brain activity between modalities with different head models. Using individual head models, hd-EEG localized significantly closer to the anatomical reference point obtained by fMRI than MEG. Source analysis results were least accurate for hd-EEG based on a standard head model. Further, hd-EEG and MEG localized more medially than fMRI. Localization accuracy of electric source imaging is dependent on the head model used with more accurate results obtained with individual head models. If this is taken into account, EEG localization can be more accurate than MEG localization for the same number of channels.

Effect of temporal resolution and serial autocorrelations in event-related functional MRI.

To assess the impact of colored noise on statistics in event‐related functional MRI (fMRI) (visual stimulation using checkerboards) acquired by simultaneous multislice imaging enabling repetition times (TRs) between 2.64 to 0.26 s.

Magnetoencephalography Reveals a Widespread Increase in Network Connectivity in Idiopathic/Genetic Generalized Epilepsy

Idiopathic/genetic generalized epilepsy (IGE/GGE) is characterized by seizures, which start and rapidly engage widely distributed networks, and result in symptoms such as absences, generalized myoclonic and primary generalized tonic-clonic seizures. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in IGE/GGE patients using diffusion tensor imaging and voxel-based morphometry. Changes have also been reported in functional networks during generalized spike wave discharges. However, network function in the resting-state without epileptiforme discharges has been less well studied. We hypothesize that resting-state networks are more representative of the underlying pathophysiology and abnormal network synchrony. We studied functional network connectivity derived from whole-brain magnetoencephalography recordings in thirteen IGE/GGE and nineteen healthy controls. Using graph theoretical network analysis, we found a widespread increase in connectivity in patients compared to controls. These changes were most pronounced in the motor network, the mesio-frontal and temporal cortex. We did not, however, find any significant difference between the normalized clustering coefficients, indicating preserved gross network architecture. Our findings suggest that increased resting state connectivity could be an important factor for seizure spread and/or generation in IGE/GGE, and could serve as a biomarker for the disease.

Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm

The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges. 15 healthy controls (visual stimulation paradigm) and three patients with epilepsy (EEG-fMRI) were measured with EPI-fMRI. We calculated the functional connectivity degree (FCD) by determining the total number of connections of a given voxel above a predefined threshold based on Pearson correlation. FCD could capture hemodynamic changes relative to stimulus onset in controls. A significant effect of TR and window size was observed on FCD estimates. At a conventional TR of 2.6 s, FCD values were marginal compared to FCD values using sub-seconds TRs achievable with multiband (MB) fMRI. Concerning window sizes, a specific maximum of FCD values (inverted u-shape behavior) was found for each TR, indicating a limit to the possible gain in FCD for increasing window size. In patients, a dynamic FCD change was found relative to the onset of epileptiform EEG patterns, which was compatible with their clinical semiology. Our findings indicate that dynamic FCD transients are better detectable with sub-second TR than conventional TR. This approach was capable of capturing neuronal connectivity across various regions of the brain, indicating a potential to study the temporal characteristics of interictal epileptiform discharges and seizures in epilepsy patients or other brain diseases with brief events.

Functional dynamics underlying near-threshold perception of facial emotions: a magnetoencephalography investigation.

Conscious perception of emotional valence of faces has been proposed to involve top-down and bottom-up information processing. Yet, the underlying neuronal mechanisms of these two processes and the implementation of their cooperation is still unclear. We hypothesized that the networks activated during the interaction of top-down and bottom-up processes are the key substrates responsible for perception. We assessed the participation of neural networks involved in conscious perception of emotional stimuli near the perceptual threshold using a visual-backward-masking paradigm in 12 healthy individuals using magnetoencephalography. Providing visual stimulation near the perceptual threshold enabled us to compare correctly and incorrectly recognized facial emotions and assess differences in top-down modulation for these stimuli using coherence analysis. We found a fronto-parietal network oscillating in the lower gamma band and exerting top-down control as determined by the causality measure of phase slope index. We demonstrated that correct recognition of facial emotions involved high-beta and low-gamma activity in parietal networks, Incorrect recognition was associated with enhanced coupling in the gamma band between left frontal and right parietal regions. Our results indicate that fronto-parietal control of the perception of emotional face stimuli relies on the right-hemispheric dominance of synchronized gamma band activity.

Correction: Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm

[This corrects the article DOI: 10.1371/journal.pone.0190480.].

Unravelling the brain networks driving spike-wave discharges in genetic generalized epilepsy-common patterns and individual differences

Genetic generalized epilepsies (GGEs) are characterized by generalized spike‐wave discharges (GSWDs) in electroencephalography (EEG) recordings without underlying structural brain lesions. The origin of the epileptic activity remains unclear, although several studies have reported involvement of thalamus and default mode network (DMN). The aim of the current study was to investigate the networks involved in the generation and temporal evolution of GSWDs to elucidate the origin and propagation of the underlying generalized epileptic activity.

Increased Functional MEG Connectivity as a Hallmark of MRI-Negative Focal and Generalized Epilepsy

Epilepsy is one of the most prevalent neurological diseases with a high morbidity. Accumulating evidence has shown that epilepsy is an archetypical neural network disorder. Here we developed a non-invasive cortical functional connectivity analysis based on magnetoencephalography (MEG) to assess commonalities and differences in the network phenotype in different epilepsy syndromes (non-lesional/cryptogenic focal and idiopathic/genetic generalized epilepsy). Thirty-seven epilepsy patients with normal structural brain anatomy underwent a 30-min resting state MEG measurement with eyes closed. We only analyzed interictal epochs without epileptiform discharges. The imaginary part of coherency was calculated as an indicator of cortical functional connectivity in five classical frequency bands. This connectivity measure was computed between all sources on individually reconstructed cortical surfaces that were surface-aligned to a common template. In comparison to healthy controls, both focal and generalized epilepsy patients showed widespread increased functional connectivity in several frequency bands, demonstrating the potential of elevated functional connectivity as a common pathophysiological hallmark in different epilepsy types. Furthermore, the comparison between focal and generalized epilepsies revealed increased network connectivity in bilateral mesio-frontal and motor regions specifically for the generalized epilepsy patients. Our study indicated that the surface-based normalization of MEG sources of individual brains enables the comparison of imaging findings across subjects and groups on a united platform, which leads to a straightforward and effective disclosure of pathological network characteristics in epilepsy. This approach may allow for the definition of more specific markers of different epilepsy syndromes, and increased MEG-based resting-state functional connectivity seems to be a common feature in MRI-negative epilepsy syndromes.

P3. Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy

Objective Dynamic causal modeling (DCM) is a method to non-invasively assess effective connectivity between brain regions. This is especially interesting in epilepsy where the identification of epileptic network dynamics is of great clinical interest. ‘Musicogenic epilepsy’ is a rare reflex epilepsy syndrome in which seizures can be specifically elicited by musical stimuli and thus represents a unique possibility to investigate complex human brain networks and test connectivity analysis tools. We investigated the temporal sequence of epileptic activity spread in a case of musicogenic epilepsy using DCM for fMRI, high-density (hd-) EEG and MEG and validated results with intracranial EEG recordings. Methods A patient with musicogenic seizures triggered by Rap music was examined using hd-EEG/fMRI and simultaneous 256-channel hd-EEG/MEG to characterize the epileptogenic focus and propagation effects using source analysis techniques and DCM. Results were validated with invasive EEG recordings. Results We recorded one seizure with hd-EEG/fMRI and four auras with hd-EEG/MEG. Consistent across all modalities, we observed activations in the right mesio-temporal region as well as bilateral mesial frontal regions ( Fig. 1 ) at seizure onset. Effective connectivity analysis of fMRI and hd-EEG/MEG indicated that right mesio-temporal neuronal activity drives changes in the frontal areas consistently in all three modalities ( Fig. 2 ), i.e. seizures seem to originate in the right mesio-temporal region and propagate to the frontal region. These results could be confirmed by invasive EEG recordings, which clearly identified the seizure onset zone in the right hippocampus with fast propagation of seizure activity to the mesial frontal lobes. Conclusions Using DCM for fMRI, hd-EEG and MEG we were able to correctly localize focus and propagation of epileptic activity and thereby characterize the underlying epileptic network in a patient with musicogenic epilepsy. The concordance between all three functional modalities validated by invasive monitoring is noteworthy, both for musicogenic seizures as well as for effective connectivity analysis in general.