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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.

We aimed to identify reorganization processes of episodic memory networks in patients with left and right temporal lobe epilepsy (TLE) due to hippocampal sclerosis as well as their relations to neuropsychological memory performance. We investigated 28 healthy subjects, 12 patients with left TLE (LTLE) and 9 patients with right TLE (RTLE) with hippocampal sclerosis by means of functional magnetic resonance imaging (fMRI) using a face-name association task, which combines verbal and non-verbal memory functions. Regions-of-interest (ROIs) were defined based on the group results of the healthy subjects. In each ROI, fMRI activations were compared across groups and correlated with verbal and non-verbal memory scores. The face-name association task yielded activations in bilateral hippocampus (HC), left inferior frontal gyrus (IFG), left superior frontal gyrus (SFG), left superior temporal gyrus, bilateral angular gyrus (AG), bilateral medial prefrontal cortex and right anterior temporal lobe (ATL). LTLE patients demonstrated significantly less activation in the left HC and left SFG, whereas RTLE patients showed significantly less activation in the HC bilaterally, the left SFG and right AG. Verbal memory scores correlated with activations in the left and right HC, left SFG and right ATL and non-verbal memory scores with fMRI activations in the left and right HC and left SFG. The face-name association task can be employed to examine functional alterations of hippocampal activation during encoding of both verbal and non-verbal material in one fMRI paradigm. Further, the left SFG seems to be a convergence region for encoding of verbal and non-verbal material., Highlights • Memory networks in patients with hippocampal sclerosis were compared to controls. • Verbal and nonverbal memory correlated with activations in both HC and left SFG • Patients with left hippocampal sclerosis activated less in the left HC and left SFG. • Patients with right-sided lesion activated less in both HC, left SFG and right AG. • Left SFG seems to be convergence region for encoding of verbal + nonverbal material.

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.

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.

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.

We previously reported nonlinear correlations between verbal episodic memory performance and BOLD signal in memory fMRI in healthy subjects. The purpose of the present study was to examine this observation in patients with left mesial temporal lobe epilepsy (mTLE) who often experience memory decline and need reliable prediction tools before epilepsy surgery with hippocampectomy. Fifteen patients with left mTLE (18–57 years, nine females) underwent a verbal memory fMRI paradigm. Correlations between BOLD activity and neuropsychological data were calculated for the i) hippocampus (HC) as well as ii) extrahippocampal mTL structures. Memory performance was systematically associated with activations within the right HC as well as with activations within the left extrahippocampal mTL regions (amygdala and parahippocampal gyrus). As hypothesized, the analyses revealed cubic relationships, with one peak in patients with marginal memory performance and another peak in patients with very good performance. The nonlinear correlations between memory performance and activations might reflect the compensatory recruitment of neural resources to maintain memory performance in patients with ongoing memory deterioration. The present data suggest an already incipient preoperative reorganization process of verbal memory in non-amnesic patients with left mTLE by simultaneously tapping the resources of the right HC and left extrahippocampal mTL regions. Thus, in the preoperative assessment, both neuropsychological performance and memory fMRI should be considered together.

Neuroticism is one of the “Big Five” personality factors and is characterized by a tendency to experience negative affect. We aimed to investigate how neuroticism influences the neural correlates for processing of emotional facial expressions. 68 healthy participants were presented with emotional dynamic facial stimuli, i.e. happy, neutral or angry, during functional MRI. Brain activations for the contrasts emotional vs. neutral, happy vs. neutral and angry vs. neutral were correlated with individuals’ neuroticism scores as obtained by the NEO Five Factor Inventory questionnaire and additionally investigated for gender differences. The bilateral medial temporal gyrus (MTG) was identified as key region in the processing of emotional faces and activations within this region correlated with individual neuroticism scores. Although female participants showed significantly stronger activation differences between emotional and neutral facial expressions in the left MTG, the correlation between activation and neuroticism scores did not show any significant gender differences. Our results offer for the first time a biological correlate within the face processing network for enhanced reactivity of neurotic individuals to emotional facial expressions which occurs similarly for both male and female participants.

Purpose: To evaluate the hypothesis that the entire hippocampus might be affected by susceptibility artifacts. Previous studies described susceptibility artifacts in the amygdala and the anterior hippocampus. Materials and Methods: We investigated 20 subjects with a verbal memory paradigm aiming at testing two different TEs (45 vs. 64 msec) at 1.5 T for hippocampal blood oxygenation level-dependent (BOLD) activity. T2* maps were calculated from the normalized mean echo-planar imaging (EPI) of the two echo times (TEs). Results: Within the hippocampal region of interest (ROI), the amount of suprathreshold voxels was significantly higher at TE = 64 msec compared to TE = 45 msec. When corrected for multiple comparisons (family-wise error [FWE] in a small volume of interest, P < 0.05) we no longer found significant activations at TE = 45 msec, while a significant number of voxels remained after the small volume correction (P < 0.05, FWE) within the ROI at TE = 64 msec. Conclusion: Although a shorter TE demonstrates advantages, a TE of 45 msec leads to a significant loss of BOLD signal detection in memory functional magnetic resonance imaging (fMRI) studies when compared to 64 msec. We assume that the hippocampal region, even the anterior part, is not strongly affected by susceptibility gradients. J. Magn. Reson. Imaging 2013;37:1064–1071. © 2012 Wiley Periodicals, Inc.

ZusammenfassungEpilepsien gehören zu den häufigsten neurologischen Erkrankungen. Die Therapie erfolgt in erster Linie symptomatisch durch Medikamente, die anhand des Epilepsiesyndroms, der Nebenwirkungen und individuellen Patientenbedürfnisse ausgewählt werden. Das Ziel ist die Anfallsfreiheit. Es sollte zunächst ein Medikament erster Wahl in ausreichend hoher Dosierung in Monotherapie gegeben werden, bei Kombinationstherapien ist darauf zu achten, nicht wirksame Medikamente wieder abzusetzen und unwirksame hohe Dosierungen zu vermeiden. Bei idiopathischen generalisierten Epilepsien sind viele der verfügbaren Anti-konvulsiva nicht (ausreichend) wirksam. Mittel der ersten Wahl sind Valproat, Ethosuximid und Lamotrigin. Bei fokalen Epilepsien ist grundsätzlich jedes Antikonvulsivum einsetzbar. Mittel der ersten Wahl sind aufgrund der guten Verträglichkeit und des günstigen Inter-aktionspotenzials Lamotrigin und Levetiracetam. Zu den nicht medikamentösen Therapieoptionen gehören die Epilepsiechirurgie, bei der das epileptogene Gewebe operativ entfernt wird, sowie die Hirnstimulation (Vagusnervstimulation, tiefe Hirnstimulation).

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.

Objective Episodic memory processes can be investigated using different functional MRI (fMRI) paradigms. The purpose of the present study was to examine correlations between neuropsychological memory test scores and BOLD signal changes during fMRI scanning using three different memory tasks. Method Twenty-eight right-handed healthy subjects underwent three paradigms, (a) a word pair, (b) a space-labyrinth, and (c) a face-name association paradigm. These paradigms were compared for their value in memory quantification and lateralization by calculating correlations between the BOLD signals in the mesial temporal lobe and behavioral data derived from a neuropsychological test battery. Results As expected, group analysis showed left-sided activation for the verbal, a tendency to right-sided activation for the spatial, and bilateral activation for the face-name paradigm. No linear correlations were observed between neuropsychological data and activation in the temporo-mesial region. However, we found significant u-shaped correlations between behavioral memory performance and activation in both the verbal and the face-name paradigms, that is, BOLD signal changes were greater not only among participants who performed best on the neuropsychological tests, but also among the poorest performers. The figural learning task did not correlate with the activations in the space-labyrinth paradigm at all. Conclusions We interpreted the u-shaped correlations to be due to compensatory hippocampal activations associated with low performance when people try unsuccessfully to remember presented items. Because activation levels did not linearly increase with memory performance, the latter cannot be quantified by fMRI alone, but only be used in conjunction with neuropsychological testing.