Genetic generalized epilepsy (GGE) is conceptualized as a brain disorder involving distributed bilateral networks. To study these networks, simultaneous EEG-fMRI measurements can be used. However, inside-MRI EEG suffers from strong MR-related artifacts; it is not established whether EEG-based metrics in EEG-fMRI resting-state measurements are suitable for the analysis of group differences at source-level. We evaluated the impact of the inside-MR measurement condition on statistical group comparisons of EEG on source-level power and functional connectivity in patients with GGE versus healthy controls. We studied the cross-modal spatial relation of statistical group differences in seed-based FC derived from EEG and parallel fMRI. We found a significant increase in power and a frequency-specific change in functional connectivity for the inside MR-scanner compared to the outside MR-scanner condition. For power, we found reduced group difference between GGE and controls both in terms of statistical significance as well as effect size. Group differences for ImCoh remained similar both in terms of statistical significance as well as effect size. We found increased seed-based FC for GGE patients from the thalamus to the precuneus cortex region in fMRI, and in the theta band of simultaneous EEG. Our findings suggest that the analysis of EEG functional connectivity based on ImCoh is suitable for MR-EEG, and that relative group difference in a comparison of patients with GGE against controls are preserved. Spatial correspondence of seed-based FC group differences between the two modalities was found for the thalamus.

ObjectivePresurgical high‐density electric source imaging (hdESI) of interictal epileptic discharges (IEDs) is only used by few epilepsy centers. One obstacle is the time‐consuming workflow both for recording as well as for visual review. Therefore, we analyzed the effect of (a) an automated IED detection and (b) the number of IEDs on the accuracy of hdESI and time‐effectiveness.MethodsIn 22 patients with pharmacoresistant focal epilepsy receiving epilepsy surgery (Engel 1) we retrospectively detected IEDs both visually and semi‐automatically using the EEG analysis software Persyst in 256‐channel EEGs. The amount of IEDs, the Euclidean distance between hdESI maximum and resection zone, and the operator time were compared. Additionally, we evaluated the intra‐individual effect of IED quantity on the distance between hdESI maximum of all IEDs and hdESI maximum when only a reduced amount of IEDs were included.ResultsThere was no significant difference in the number of IEDs between visually versus semi‐automatically marked IEDs (74 ± 56 IEDs/patient vs 116 ± 115 IEDs/patient). The detection method of the IEDs had no significant effect on the mean distances between resection zone and hdESI maximum (visual: 26.07 ± 31.12 mm vs semi‐automated: 33.6 ± 34.75 mm). However, the mean time needed to review the full datasets semi‐automatically was shorter by 275 ± 46 min (305 ± 72 min vs 30 ± 26 min, P < 0.001).The distance between hdESI of the full versus reduced amount of IEDs of the same patient was smaller than 1 cm when at least a mean of 33 IEDs were analyzed. There was a significantly shorter intraindividual distance between resection zone and hdESI maximum when 30 IEDs were analyzed as compared to the analysis of only 10 IEDs (P < 0.001).SignificanceSemi‐automatized processing and limiting the amount of IEDs analyzed (~30–40 IEDs per cluster) appear to be time‐saving clinical tools to increase the practicability of hdESI in the presurgical work‐up.

Background: Developmental dyslexia is a neurodevelopmental disorder characterized by significant difficulties in reading and spelling. Despite lacking routine neuroimaging markers for dyslexia, recent resting-state electroencephalography (EEG) studies have detected atypical functional connectivity in children with dyslexia compared to controls. These methods are based on measures of EEG data at a sensor-level, but it remains unclear if routine clinical resting-state EEG can be used to detect source-level differences in power or functional connectivity (FC) between children with dyslexia and controls. It is also unknown if differences in these EEG metrics correlate with difficulties in reading and spelling. Methods: Using retrospective data, we investigated the source-reconstructed power and FC of 70 children with recently diagnosed dyslexia and 50 typically developing controls. We analyzed 50 seconds of awake resting-state routine clinical EEG in five frequency bands (1-29 Hz) using power, imaginary part of coherency (ImCoh), and weighted phase lag index (wPLI). Additionally, we calculated correlations between power or FC and IQ, reading, and spelling performance. Results: Children with dyslexia had a decrease in theta FC in left temporo-parieto-occipital regions and an increase in alpha FC in left fronto-temporo-parietal regions. A decrease of theta FC was observed for right parieto-occipital regions and an increase of alpha FC in right inferior fronto-temporal regions. Furthermore, children with dyslexia demonstrated lower power in delta and theta within the left parieto-occipital regions. An age-stratified sub-analysis indicated that children with dyslexia in 5th-8th school grades exhibit greater alpha FC mainly in left fronto-temporo-parietal regions. Finally, lower scores in spelling showed a positive and significant association to theta power within left parieto-occipital regions in dyslexia. Conclusions: Significant group differences in power and FC in the theta-alpha range in left cortical language and visual regions, as well as in multiple resting-state networks (RSNs), suggest abnormal oscillations as a pathophysiological sign of dyslexia reading and spelling deficits. These findings demonstrate the potential of source-reconstructed clinical routine EEG data to inform clinicians about brain network alterations in neurodevelopmental disorders such as dyslexia.