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ImportanceAcute symptomatic seizures occurring within 7 days after ischemic stroke may be associated with an increased mortality and risk of epilepsy. It is unknown whether the type of acute symptomatic seizure influences this risk.ObjectiveTo compare mortality and risk of epilepsy following different types of acute symptomatic seizures.Design, Setting, and ParticipantsThis cohort study analyzed data acquired from 2002 to 2019 from 9 tertiary referral centers. The derivation cohort included adults from 7 cohorts and 2 case-control studies with neuroimaging-confirmed ischemic stroke and without a history of seizures. Replication in 3 separate cohorts included adults with acute symptomatic status epilepticus after neuroimaging-confirmed ischemic stroke. The final data analysis was performed in July 2022.ExposuresType of acute symptomatic seizure.Main Outcomes and MeasuresAll-cause mortality and epilepsy (at least 1 unprovoked seizure presenting >7 days after stroke).ResultsA total of 4552 adults were included in the derivation cohort (2547 male participants [56%]; 2005 female [44%]; median age, 73 years [IQR, 62-81]). Acute symptomatic seizures occurred in 226 individuals (5%), of whom 8 (0.2%) presented with status epilepticus. In patients with acute symptomatic status epilepticus, 10-year mortality was 79% compared with 30% in those with short acute symptomatic seizures and 11% in those without seizures. The 10-year risk of epilepsy in stroke survivors with acute symptomatic status epilepticus was 81%, compared with 40% in survivors with short acute symptomatic seizures and 13% in survivors without seizures. In a replication cohort of 39 individuals with acute symptomatic status epilepticus after ischemic stroke (24 female; median age, 78 years), the 10-year risk of mortality and epilepsy was 76% and 88%, respectively. We updated a previously described prognostic model (SeLECT 2.0) with the type of acute symptomatic seizures as a covariate. SeLECT 2.0 successfully captured cases at high risk of poststroke epilepsy.Conclusions and RelevanceIn this study, individuals with stroke and acute symptomatic seizures presenting as status epilepticus had a higher mortality and risk of epilepsy compared with those with short acute symptomatic seizures or no seizures. The SeLECT 2.0 prognostic model adequately reflected the risk of epilepsy in high-risk cases and may inform decisions on the continuation of antiseizure medication treatment and the methods and frequency of follow-up.

Despite several studies investigating the relationship between blood-oxygen-level-dependent functional MRI (BOLD-fMRI) and neuroelectric activity, our understanding is rather incomplete. For instance, the canonical hemodynamic response function (HRF) is commonly used, regardless of brain region, frequency of electric activity and functional networks. We studied this relationship between BOLD-fMRI and electroencephalography (EEG) signal of the human brain in detail using simultaneous fMRI and EEG in healthy awake human subjects at rest. Signals from EEG sensors were filtered into different frequency bands and reconstructed it in the three-dimensional source space. The correlation of the time courses of the two modalities were quantified on a voxel-by-voxel basis on full-brain level as well as separately for each resting state network, with different temporal shifts and EEG frequency bands. We found highly significant correlations between the BOLD-fMRI signal and simultaneously measured EEG, yet with varying time-lags for different frequency bands and different resting state networks. Additionally, we found significant negative correlations with a much longer delay in the fMRI BOLD signal. The positive correlations were mostly around 6-8 seconds delayed in the BOLD time course while the negative correlations were noticed with a BOLD delay of around 20 to 26 seconds. These positive and negative correlation patterns included the commonly reported alpha and gamma bands but also extend in other frequency bands giving characteristic profiles for different resting state networks. Our results confirm recent works that suggest that the relationship between the two modalities is rather brain region / network-specific than a global function and suggest that applying a global canonical HRF for electrophysiological data is probably insufficient to account for the different spatial and temporal dynamics of different brain networks. Moreover, our results suggest that the HRF also varies in different frequency bands giving way to further studies investigating cross-frequency coupling and its interplay with resting state networks.

OBJECTIVE Genetic generalized epilepsy is characterized by aberrant neuronal dynamics and subtle structural alterations. We evaluated whether a combination of magnetic and electrical neuronal signals and cortical thickness would provide complementary information about network pathology in GGE. We also investigated if these imaging phenotypes were present in healthy siblings of the patients to test for genetic influence. METHODS In this cross-sectional study, we analyzed five minutes of resting-state data acquired using electroencephalography (EEG) and magnetoencephalography (MEG) in patients, their siblings, and controls, matched for age and sex. We computed source-reconstructed power and connectivity in six frequency bands (1-40 Hz) and cortical thickness (derived from magnetic resonance imaging (MRI)). Group differences were assessed using permutation analysis of linear models for each modality separately and jointly for all modalities using a non-parametric combination. RESULTS Patients with GGE (n = 23) had higher power than controls (n = 35) in all frequencies, with a more posterior focus in MEG than EEG. Connectivity was also increased, particularly in frontotemporal and central regions in theta (strongest in EEG) and low beta frequencies (strongest in MEG), which was eminent in the joint EEG/MEG analysis. EEG showed weaker connectivity differences in higher frequencies, possibly related to drug effects. The inclusion of cortical thickness reinforced group differences in connectivity and power. Siblings (n = 18) had functional and structural patterns intermediate between those of patients and controls. SIGNIFICANCE EEG detected increased connectivity and power in GGE similar to MEG, but with different spectral sensitivity, highlighting the importance of theta and beta oscillations. Cortical thickness reductions in GGE corresponded to functional imaging patterns. Our multimodal approach extends the understanding of the resting-state in GGE and points to genetic underpinnings of the imaging markers studied, providing new insights into the causes and consequences of epilepsy.

Summary Objective 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. Methods We examined 12 patients with GGE and GSWDs using EEG–functional magnetic resonance imaging (fMRI) and identified involved brain areas on the basis of a classical general linear model (GLM) analysis. The activation time courses of these areas were further investigated to reveal their temporal sequence of activations and deactivations. Dynamic causal modeling (DCM) was used to determine the generator of GSWDs in GGE. Results We observed activity changes in the thalamus, DMN, dorsal attention network (DAN), salience network (SN), basal ganglia, dorsolateral prefrontal cortex, and motor cortex with supplementary motor area, however, with a certain heterogeneity between patients. Investigation of the temporal sequence of activity changes showed deactivations in the DMN and DAN and activations in the SN and thalamus preceding the onset of GSWDs on EEG by several seconds. DCM analysis indicated that the DMN gates GSWDs in GGE. Significance The observed interplay between DMN, DAN, SN, and thalamus may indicate a downregulation of consciousness. The DMN seems to play a leading role as a driving force behind these changes. Overall, however, there were also clear differences in activation patterns between patients, reflecting a certain heterogeneity in this cohort of GGE patients.

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.

PURPOSE 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. METHODS T-values within the visual cortex obtained with analysis tools that assume a first-order autoregressive plus white noise process (AR(1)+w) with a fixed AR coefficient versus higher-order AR models with spatially varying AR coefficients were compared. In addition, dependency of T-values on correction of physiological noise (respiration, heart rate) was evaluated. RESULTS Optimal statistical power was obtained for a TR of 0.33 s, but T-values as obtained by AR(1)+w models were strongly dependent on the predefined AR coefficients in fMRI with short TRs which required higher-order AR models to achieve stable statistics. Direct estimation of AR coefficients revealed the highest values within the default mode network while physiological noise had little influence on statistics in cortical structures. CONCLUSION Colored noise in event-related fMRI obtained at short TRs originates mainly from neural sources and calls for more sophisticated correction of serial autocorrelations which cannot be achieved with standard methods relying on AR(1)+w models with globally fixed AR coefficients. Magn Reson Med 76:1805-1813, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

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.

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.

Dynamic causal modeling (DCM) is a method to non-invasively assess effective connectivity between brain regions. 'Musicogenic epilepsy' is a rare reflex epilepsy syndrome in which seizures can be elicited by musical stimuli and thus represents a unique possibility to investigate complex human brain networks and test connectivity analysis tools. We investigated effective connectivity in a case of musicogenic epilepsy using DCM for fMRI, high-density (hd-) EEG and MEG and validated results with intracranial EEG recordings. A patient with musicogenic seizures was examined using hd-EEG/fMRI and simultaneous '256-channel hd-EEG'/'whole head MEG' to characterize the epileptogenic focus and propagation effects using source analysis techniques and DCM. Results were validated with invasive EEG recordings. We recorded one seizure with hd-EEG/fMRI and four auras with hd-EEG/MEG. During the seizures, increases of activity could be observed in the right mesial temporal region as well as bilateral mesial frontal regions. Effective connectivity analysis of fMRI and hd-EEG/MEG indicated that right mesial temporal neuronal activity drives changes in the frontal areas consistently in all three modalities, which was confirmed by the results of invasive EEG recordings. Seizures thus seem to originate in the right mesial temporal lobe and propagate to mesial frontal regions. 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 epileptic activity spread as well as for effective connectivity analysis in general.

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.T-values within the visual cortex obtained with analysis tools that assume a first-order autoregressive plus white noise process (AR(1)+w) with a fixed AR coefficient versus higher-order AR models with spatially varying AR coefficients were compared. In addition, dependency of T-values on correction of physiological noise (respiration, heart rate) was evaluated.Optimal statistical power was obtained for a TR of 0.33 s, but T-values as obtained by AR(1)+w models were strongly dependent on the predefined AR coefficients in fMRI with short TRs which required higher-order AR models to achieve stable statistics. Direct estimation of AR coefficients revealed the highest values within the default mode network while physiological noise had little influence on statistics in cortical structures.Colored noise in event-related fMRI obtained at short TRs originates mainly from neural sources and calls for more sophisticated correction of serial autocorrelations which cannot be achieved with standard methods relying on AR(1)+w models with globally fixed AR coefficients. Magn Reson Med 76:1805-1813, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

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.

Electroencephalography (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.

Introduction Idiopathic generalized epilepsy (IGE) is a subtype of epilepsy presumed to have a genetic etiology and characterized by generalized seizures, i.e. seizures starting and rapidly engaging distributed networks. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in idiopathic generalized epilepsy patients using diffusion tensor imaging and voxel based morphometry. Changes were also reported in functional networks during generalized spike wave discharges. In this study we wanted to investigate the IGE brain networks during the discharge-free intervals using Magnetoencephalography (MEG). To study the characteristics of the network, we used a graph theoretical approach. Methods We recorded resting state MEG data from 13 (9 females, mean age 38.6 years) IGE patients and 19 (11 females, mean age 38.5 years) healthy controls. Epileptic discharges were identified and marked by a neurologist and excluded from the analysis. Source localization of the resting state activity was performed using dynamic imaging of coherent sources (DICS) beamforming. Connectivity was estimated between all sources in the gray matter using the imaginary part of coherence. Another low-resolution network was generated by averaging connections between sources grouped by anatomical labeling. Graph measures were calculated on both the high resolution and low resolution networks. Results Network connectivity was significantly increased in IGE patients in the high-resolution networks in beta1 ( p = 0.003) and beta2 ( p = 0.0003) bands as compared to controls. In the low-resolution networks, connectivity in beta1 and beta2 was significantly higher in patients than in control subjects ( p = 0.005 and p = 0.0003 respectively). The further analysis of nodal strength using cluster-based statistics yielded significant clusters in the beta1 and beta2 bands. In the beta1 band, three significant clusters were found with higher nodal strength in IGE patients than in controls ( p = 0.004, p = 0.011 and p = 0.029 respectively). In the beta2 band, four significant clusters were found ( p = 0.0004, p = 0.002, p = 0.005 and p = 0.034 respectively) (see Fig. 1 ). We found no significant clusters of higher connectivity in controls compared to IGE patients in any of the frequency bands. Using Network Based Statistics, we found significant subnetworks with higher connectivity in IGE patients. The differences were found in alpha, beta1 and beta2 bands (see Fig. 2 ). We did not find any significant correlation between global connectivity and the age of participants. Conclusions Using graph theoretical network analysis, we found a widespread increase in connectivity in IGE patients compared to controls. These changes were more pronounced in the motor network and the mesio-frontal cortex. We did not, however, find any significant difference in clustering coefficient and characteristic path length which are measures that represent architectural differences in the network. Our findings suggest that an increased resting state connectivity in idiopathic generalized epilepsy could be a mechanism of seizure generation and represents a functional brain imaging endophenotype of the disease.