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V2. Detection of epileptic activity in absence of EEG interictal epileptic discharges
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Society Proceedings / Clinical Neurophysiology 126 (2015) e63–e170
Orals (V) – Functional Imaging V1. Electric source imaging (ESI) with 10–10 electrodes and individual MRI in presurgical epilepsy monitoring (BESA-Research and BESA-MRI)—R. Schulz a, M. Scherg b, F.G. Woermann c, C.G. Bien a (a Epilepsiezentrum Bethel, Bielefeld, Germany, b BESA GmbH, Gräfelfing, Germany, c Gesellschaft für Epilepsieforschung, Bielefeld, Germany) Introduction: Electric source imaging (ESI) using high-density scalp EEG with at least 128 channels has been reported to accurately localize interictal epileptiform activity. The precision of ESI with 10–10 electrodes has been debated. Method: In a pilot study of 10 patients with well defined epileptogenic lesions (focal cortical dysplasia N = 4, tumor N = 2, unspecified N = 2, cavernoma N = 1, proliferative oligodendroglial hyperplasia N = 1) with focal epilepsy (4 frontal, 2 temporal, 1 parietal, 1 temporoparietal, 1 temporooccipital) we used the presurgical routine of 37–41 scalp electrodes (10–10 system, including inferior electrodes F11, F12, P11, P12) for the source analysis of interictal epileptiform potentials with BESA-Research and for coregistration with the individual MRI (BESA-MRI). Results: 9–209 spikes were averaged (mean: 65.9, median: 21, range 9–209; no spikes in 1 patient with FCD so that no source analysis was possible). The location of ESI was 0–1 cm to the lesion in 8 patients, 3–4 cm to the lesion in 1 patient. Conclusion: ESI shows promising results also with a presurgical routine scalp EEG electrode placement. Prospective studies with lesional epilepsies could further define the precision and reliability of this protocol (Plummer et al., 2008; Mégevand et al., 2014). References Plummer C, Harvey AS, Cook M. EEG source localization in focal epilepsy: where are we now? Epilepsia 2008;49:201–18. Mégevand P, Spinelli L, Genetti M, Brodbeck V, Momjian S, Schaller K, et al. Electric source imaging of interictal activity accurately localizes the seizure onset zone. J Neurol Neurosurg Psychiatry 2014;85:38–43. doi:10.1016/j.clinph.2015.04.079
V2. Detection of epileptic activity in absence of EEG interictal epileptic discharges—F. Pittau a, M. Genetti b, G. Birot b, M. Tomescu b, S. Baldini b, S. Vulliémoz a, C. Michel b, M. Seeck a (a University Hospitals and Faculty of Medicine of Geneva, EEG and Epilepsy Unit, Neurology Department, Geneva, Switzerland, b University of Geneva, Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Geneva, Switzerland) Rationale: Aim: to assess if the EEGs of epileptic patients with any detectable spikes contain the voltage epileptic map, specific of the individual patient. Methods: Fifteen patients with pharmaco-resistant focal epilepsy were included. Six minutes of EEG with spikes and six minutes without any detectable spikes were selected from long-term monitoring recording during wakeful resting state (EEG 31-channels, ref: FCz). Resting-state EEG from 48 healthy control subjects were also recorded and corrected for artifacts. For the EEG of each patient, we calculated the averaged spike and its voltage map. We fitted the spike map on (i) EEG of patient with visible spikes (ii) EEG of the same patient without any visible spike and (iii) EEGs of the 48 controls. The amount of presence of the voltage epileptic map was characterized using two criteria: mean correlation and Global Explained Variance (GEV). For these criteria statistical differences between (1) controls and EEG with spikes, and (2) controls and EEG without spikes were evaluated using z-scores.
Results: The patient-specific epileptic voltage map was significantly more represented in the spike-free EEGs of patients than in EEGs of healthy controls (GEV p = 0.029; mean correlation p = 0.032). This difference was more accentuated for the patient EEGs containing spikes (GEV p = 0.001, mean correlation (p < 0.001). Discussion: Scalp EEGs of patients with pharmaco-resistant epilepsy contains the epileptic voltage map (index of epileptic activity), even in absence of any detectable interictal epileptic discharges. This finding suggests that epileptic voltage map could be a new epileptic bio-marker (SPUM grant 140332 and 141165). doi:10.1016/j.clinph.2015.04.080
V3. Idiopathic generalized epilepsy shows widespread increased network connectivity—A. Elshahabi a,b, S. Klamer a, A.K. Sahib a, H. Lerche a, C. Braun b, N.K. Focke a (a Eberhard Karls Universität Tübingen, Hertie-Institut für klinische Hirnforschung, Tübingen, Germany, b Eberhard Karls Universität Tübingen, MEG Zentrum, Tübingen, Germany) 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
Society Proceedings / Clinical Neurophysiology 126 (2015) e63–e170
Orals (V) – Functional Imaging V1. Electric source imaging (ESI) with 10–10 electrodes and individual MRI in presurgical epilepsy monitoring (BESA-Research and BESA-MRI)—R. Schulz a, M. Scherg b, F.G. Woermann c, C.G. Bien a (a Epilepsiezentrum Bethel, Bielefeld, Germany, b BESA GmbH, Gräfelfing, Germany, c Gesellschaft für Epilepsieforschung, Bielefeld, Germany) Introduction: Electric source imaging (ESI) using high-density scalp EEG with at least 128 channels has been reported to accurately localize interictal epileptiform activity. The precision of ESI with 10–10 electrodes has been debated. Method: In a pilot study of 10 patients with well defined epileptogenic lesions (focal cortical dysplasia N = 4, tumor N = 2, unspecified N = 2, cavernoma N = 1, proliferative oligodendroglial hyperplasia N = 1) with focal epilepsy (4 frontal, 2 temporal, 1 parietal, 1 temporoparietal, 1 temporooccipital) we used the presurgical routine of 37–41 scalp electrodes (10–10 system, including inferior electrodes F11, F12, P11, P12) for the source analysis of interictal epileptiform potentials with BESA-Research and for coregistration with the individual MRI (BESA-MRI). Results: 9–209 spikes were averaged (mean: 65.9, median: 21, range 9–209; no spikes in 1 patient with FCD so that no source analysis was possible). The location of ESI was 0–1 cm to the lesion in 8 patients, 3–4 cm to the lesion in 1 patient. Conclusion: ESI shows promising results also with a presurgical routine scalp EEG electrode placement. Prospective studies with lesional epilepsies could further define the precision and reliability of this protocol (Plummer et al., 2008; Mégevand et al., 2014). References Plummer C, Harvey AS, Cook M. EEG source localization in focal epilepsy: where are we now? Epilepsia 2008;49:201–18. Mégevand P, Spinelli L, Genetti M, Brodbeck V, Momjian S, Schaller K, et al. Electric source imaging of interictal activity accurately localizes the seizure onset zone. J Neurol Neurosurg Psychiatry 2014;85:38–43. doi:10.1016/j.clinph.2015.04.079
V2. Detection of epileptic activity in absence of EEG interictal epileptic discharges—F. Pittau a, M. Genetti b, G. Birot b, M. Tomescu b, S. Baldini b, S. Vulliémoz a, C. Michel b, M. Seeck a (a University Hospitals and Faculty of Medicine of Geneva, EEG and Epilepsy Unit, Neurology Department, Geneva, Switzerland, b University of Geneva, Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Geneva, Switzerland) Rationale: Aim: to assess if the EEGs of epileptic patients with any detectable spikes contain the voltage epileptic map, specific of the individual patient. Methods: Fifteen patients with pharmaco-resistant focal epilepsy were included. Six minutes of EEG with spikes and six minutes without any detectable spikes were selected from long-term monitoring recording during wakeful resting state (EEG 31-channels, ref: FCz). Resting-state EEG from 48 healthy control subjects were also recorded and corrected for artifacts. For the EEG of each patient, we calculated the averaged spike and its voltage map. We fitted the spike map on (i) EEG of patient with visible spikes (ii) EEG of the same patient without any visible spike and (iii) EEGs of the 48 controls. The amount of presence of the voltage epileptic map was characterized using two criteria: mean correlation and Global Explained Variance (GEV). For these criteria statistical differences between (1) controls and EEG with spikes, and (2) controls and EEG without spikes were evaluated using z-scores.
Results: The patient-specific epileptic voltage map was significantly more represented in the spike-free EEGs of patients than in EEGs of healthy controls (GEV p = 0.029; mean correlation p = 0.032). This difference was more accentuated for the patient EEGs containing spikes (GEV p = 0.001, mean correlation (p < 0.001). Discussion: Scalp EEGs of patients with pharmaco-resistant epilepsy contains the epileptic voltage map (index of epileptic activity), even in absence of any detectable interictal epileptic discharges. This finding suggests that epileptic voltage map could be a new epileptic bio-marker (SPUM grant 140332 and 141165). doi:10.1016/j.clinph.2015.04.080
V3. Idiopathic generalized epilepsy shows widespread increased network connectivity—A. Elshahabi a,b, S. Klamer a, A.K. Sahib a, H. Lerche a, C. Braun b, N.K. Focke a (a Eberhard Karls Universität Tübingen, Hertie-Institut für klinische Hirnforschung, Tübingen, Germany, b Eberhard Karls Universität Tübingen, MEG Zentrum, Tübingen, Germany) 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