The Anatomo-Electrical Network Underlying Hypermotor Seizures

Stereo-EEG localization of midline onset seizures on scalp EEG

PURPOSE

The objective of this study was to describe the sEEG-defined seizure onset zone (SOZ), seizure semiology, presurgical evaluations, surgical intervention and outcome in patients with midline onset noninvasive phase I monitoring.

METHODS

A single center sEEG database was reviewed to identify patients with seizures onset predominantly involving midline electrodes (FZ, CZ, PZ, OZ) on scalp EEG. Data abstracted included clinical factors, seizure semiology graded into lobar segmentation, imaging and electrographic findings, sEEG plan, interventions, and outcome.

RESULTS

Twelve patients were identified (8 males, median age of sEEG 28 years) out of 100 cases of sEEG performed from January 2015-September 2019. "Frontal lobe" seizure semiology was the most common. sEEG-defined SOZ were frontal (5), diffuse (1), multifocal (1), frontal and insular (1), frontal and cingulate (1), insular (1), cingulate (1), and mesial temporal (1). CZ and/or FZ scalp EEG changes were present for all patients with SOZ involving the frontal, cingulate, and insular regions. PZ/OZ scalp involvement was present in one patient with mesial temporal SOZ. Four patients underwent a definitive resective or ablative surgery, and the remaining patients underwent a palliative intervention. Of those with follow-up information available, 8/11 had seizure reduction by ≥ 50%, including 4 with an Engel I outcome. No clinical factors were associated with outcome.

CONCLUSIONS

SOZ for midline onset seizures from noninvasive phase I monitoring was most commonly in the frontal, cingulate, and insular regions. A complex cortical network between these regions may explain overlap in semiology and scalp EEG findings. While the number rendered seizure-free was limited, a significant proportion experienced a reasonably favorable outcome justifying use of sEEG to identify surgical options in these patients.

Outcome following surgery for insulo-opercular epilepsies

OBJECTIVE

The purpose of this study was to evaluate the clinical outcome in patients with medically refractory epilepsy who had undergone resective or ablative surgery for suspected insulo-opercular epileptogenic foci.

METHODS

The prospectively maintained database of patients undergoing epilepsy surgery was reviewed, and all patients who underwent insulo-opercular surgery for medically refractory epilepsy with a minimum of 12 months of postoperative follow-up were identified, excluding those who had insulo-opercular resection in combination with temporal lobectomy. The presurgical electroclinicoradiological data, stereo-EEG (SEEG) findings, resection/ablation patterns, surgical pathology, postoperative seizure outcome, and neurological complications were analyzed.

RESULTS

Of 407 patients undergoing epilepsy surgery in a 5-year period at the Amrita Advanced Centre for Epilepsy, 24 patients (5.9%) who underwent exclusive insulo-opercular interventions were included in the study. Eleven (46%) underwent surgery on the right side, 12 (50%) on the left side, and the operation was bilateral in 1 (4%). The mean age at surgery was 24.5 ± 12.75 years. Onset of seizures occurred on average at 10.6 ± 9.7 years of life. Characteristic auras were identified in 66% and predominant seizure type was hypermotor (15.4%), automotor (15.4%), hypomotor (11.5%), or a mixed pattern. Seventy-five percent of the seizures recorded on scalp video-EEG occurred during sleep. The 3T MRI results were normal in 12 patients (50%). Direct single-stage surgery was undertaken in 5 patients, and SEEG followed by intervention in 19. Eighteen patients (75%) underwent exclusive resective surgery, 4 (16.7%) underwent exclusive volumetric radiofrequency ablation, and 2 (8.3%) underwent staged radiofrequency ablation and resective surgery. Immediate postoperative neurological deficits occurred in 10/24 (42%), which persisted beyond 12 postoperative months in 3 (12.5%). With a mean follow-up of 25.9 ± 14.6 months, 18 patients (75%) had Engel class I outcome, 3 (12.5%) had Engel class II, and 3 (12.5%) had Engel class III or IV. There was no statistically significant difference in outcomes between MRI-positive versus MRI-negative cases.

CONCLUSIONS

Surgery for medically refractory epilepsy in insulo-opercular foci is less common and remains a challenge to epilepsy surgery centers. Localization is aided significantly by a careful study of auras and semiology followed by EEG and imaging. The requirement for SEEG is generally high. Satisfactory rates of seizure freedom were achievable independent of the MRI lesional/nonlesional status. Morbidity is higher for insulo-opercular epilepsy surgery compared to other focal epilepsies; hence, the practice and development of minimally invasive strategies for this subgroup of patients undergoing epilepsy surgery is perhaps most important.

Patterns of hypometabolism in frontal lobe epilepsy originating in different frontal regions

Objectives

Analysis of FDG‐PET imaging commonly shows that hypometabolism extends into extra‐epileptogenic zones (extra‐EZ). This study investigates the distribution patterns of hypometabolism in frontal lobe epilepsy (FLE) originating in different frontal regions.

Methods

Sixty‐four patients with FLE were grouped by EZ localization according to Brodmann areas (BAs): Group 1 (the frontal motor and premotor area), BAs 4, 6, and 8; Group 2 (the inferior frontal gyrus and opercular area), BAs 44, 45, and 47; Group 3 (the dorsal prefrontal area), BAs 9, 10, 11, and 46; and Group 4 (the medial frontal and anterior cingulate gyrus), BAs 32 and 24. Regions of extra‐EZ hypometabolism were statistically analyzed between FLE groups and healthy controls. Correlation analysis was performed to identify relationships between the intensity of hypometabolism and clinical characteristics.

Results

Significant hypometabolism in the ipsilateral (Groups 1 and 4) or bilateral (Groups 2 and 3) anterior insulae was found. Groups 1 and 4 presented with limited distribution of extra‐EZ hypometabolism, whereas Groups 2 and 3 showed widely distributed extra‐EZ hypometabolism in the rectus gyrus, cingulate gyrus, and other regions. Additionally, the intensity of hypometabolism was correlated with epilepsy duration in Groups 2 and 3.

Conclusions

All FLE groups showed hypometabolism in the anterior insula. In addition, distinct patterns of extra‐EZ hypometabolism were identified for each FLE group. This quantitative FDG‐PET analysis expanded our understanding of the topography of epileptic networks and can guide EZ localization in the future.

Neural networks underlying hyperkinetic seizures: A quantitative PET and SEEG study

OBJECTIVE

Hyperkinetic seizures (HKS) are characterized by complex movements that commonly occur during seizures arising from diverse cortical structures. A common semiology network may exist and analyzing the anatomo-electrical mechanisms would facilitate presurgical evaluation. Here, quantitative positron emission tomography (PET) and stereoelectroencephalography (SEEG) analysis was used to explore the underlying mechanism of HKS.

METHODS

We retrospectively collected patients with epilepsy with HKS between 2014 and 2019. The interictal PET data of patients with epilepsy with HKS were compared with those of 25 healthy subjects using statistical parametric mapping to identify regions with significant hypometabolism. Then, regions of interest (ROI) for SEEG analysis were identified based on the results of PET analysis. Patients in which the ROIs were covered by intracerebral electrodes were selected for further analysis. Stereoelectroencephalography -clinical correlations with latency measurements were analyzed, and we also performed coherence analysis among ROIs both before and during HKS.

RESULTS

Based on the inclusion criteria, 27 patients were analyzed. In the PET analysis, significant hypometabolism was observed in the ipsilateral dorsoanterior insular lobe, bilateral mesial frontal lobes (supplementary motor area/middle cingulate cortex, SMA/MCC), and the bilateral heads of the caudate nuclei in patients with HKS compared with the control group (p < 0.001). We selected dorsoanterior insula and SMA/MCC as ROIs for SEEG analysis. Eight patients with 23 HKS events were selected for further analysis. There was a linear correlation between the ictal involvement of both the dorsoanterior insula and SMA/MCC with the onset of HKS. Stereoelectroencephalography analysis indicated alpha range activity seemed more often associated with dorsoanterior insula and SMA/MCC involvement during HKS.

CONCLUSIONS

The dorsoanterior insular lobe, mesial frontal lobes (SMA/MCC), and the bilateral heads of the caudate nuclei were probably involved in the generation of HKS. The SEEG analysis further indicated that the occurrence of HKS might be partly associated with synchronized rhythmical alpha activity between dorsoanterior insula and SMA/MCC.

Insular interictal positron emission tomography hypometabolism in patients with ictal asystole

We aimed to explore brain area(s) involved in the generation of ictal asystole (IA) by analyzing the interictal positron emission tomography (PET) metabolism of patients with IA recorded by video-electroencephalography or video-stereo-electroencephalography. We identified in our cohort of focal epilepsy patients who had undergone presurgical evaluation those who had a recorded period of IA of more than 3 s. We investigated the anatomometabolic changes (interictal ¹⁸ F-fluorodeoxyglucose PET) of these patients in comparison with (1) healthy subjects with similar age and sex distribution (n = 19) using whole-brain voxel-based analysis (p-voxel < .001, p-cluster < .05, uncorrected) and (2) patients without IA with similar age and seizure onset zone (n = 55). We found 12 patients with IA. Epilepsy was mainly temporal (four right temporal mesial, four bitemporal, two left temporal lateral, one right temporal lateral, and one right temporal "plus"). Seven patients had negative magnetic resonance imaging. Whole-brain statistical analysis of PET imaging was performed at the voxel level, showing that in comparison to healthy subjects and to epileptic patients without IA, a hypometabolism in the right posterior insula characterized epileptic patients with IA. Our study suggests involvement of the right posterior insula-a part of the central autonomic network-in the pathophysiological mechanism of IA.

Aberrant Metabolic Patterns Networks in Insular Epilepsy

Introduction: Insular epilepsy is clinically challenging. This study aimed to map cerebral metabolic networks in insular epilepsy and investigate their graph-theoretic properties, with the goal of elucidating altered metabolic network architectures that underlie interictal hypometabolism.

Aims: Fluorine-18-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) imaging was performed in 17 individuals with a stereoelectroencephalography (SEEG) confirmed diagnosis of insula epilepsy and 14 age- and sex-matched healthy comparison individuals. Metabolic covariance networks were mapped for each group and graph theoretical analyses of these networks were undertaken. For each pair of regions comprising a whole-brain parcellation, regionally-averaged FDG uptake values were correlated across individuals to estimate connection weights.

Results: Correlation in regionally-averaged FDG uptake values in the insular epilepsy group was substantially increased for several pairs of regions compared to the healthy comparison group, particularly for the opercular cortex and subcortical structures. This effect was less prominent in brainstem structures. Metabolic covariance networks in the epilepsy group showed reduced small-worldness as well as altered nodal properties in the ipsilateral hemisphere, compared to the healthy comparison group.

Conclusions: Cerebral glucose metabolism in insular epilepsy is marked by a lack of normal regional heterogeneity in metabolic patterns, resulting in metabolic covariance networks that are more tightly coupled between regions than healthy comparison individuals. Metabolic networks in insular epilepsy exhibit altered topological properties and evidence of potentially compensatory formation of aberrant local connections. Taken together, these results demonstrate that insular epilepsy is a systemic neurological disorder with widespread disruption to cerebral metabolic networks.

Orbitofrontal epilepsy: distinct neuronal networks underlying electroclinical subtypes and surgical outcomes

OBJECTIVE

The aim of this study was to characterize the clinical and electrophysiological findings of epilepsy originating from the orbitofrontal cortex (OFC) as well as its surgical outcomes.

METHODS

The authors retrospectively reviewed 27 consecutive cases of patients with drug-resistant orbitofrontal epilepsy (OFE) who underwent tailored resective surgery after a detailed presurgical workup. Demographic features, seizure semiology, imaging characteristics, resection site, pathological results, and surgical outcomes were analyzed. Patients were categorized according to semiology. The underlying neural network was further explored through quantitative FDG-PET and ictal stereo-electroencephalography (SEEG) analysis at the group level. FDG-PET studies between the semiology group and the control group were compared using a voxel-based independent t-test. Ictal SEEG was quantified by calculating the energy ratio (ER) of high- and low-frequency bands. An ER comparison between the anterior cingulate cortex (ACC) and the amygdala was performed to differentiate seizure spreading patterns in groups with different semiology.

RESULTS

Scalp electroencephalography (EEG) and MRI were inconclusive to a large extent. Patients were categorized into the following 3 semiology groups: the frontal group (n = 14), which included patients with hyperactive automatisms with agitated movements; the temporal group (n = 11), which included patients with oroalimentary or manual automatisms; and the other group (n = 2), which included patients with none of the abovementioned or indistinguishable manifestations. Patients in the frontal and temporal groups (n = 23) or in the frontal group only (n = 14) demonstrated significant hypometabolism mainly across the ipsilateral OFC, ACC, and anterior insula (AI), while patients in the temporal group (n = 9) had hypometabolism only in the OFC and AI. The ER results (n = 15) suggested distinct propagation pathways that allowed us to differentiate between the frontal and temporal groups. Pathologies included focal cortical dysplasia, dysembryoplastic neuroepithelial tumor, cavernous malformation, glial scar, and nonspecific findings. At a minimum follow-up of 12 months, 19 patients (70.4%) were seizure free, and Engel class II, III, and IV outcomes were observed in 4 patients (14.8%), 3 patients (11.1%), and 1 patient (3.7%), respectively.

CONCLUSIONS

The diagnosis of OFE requires careful presurgical evaluation. Based on their electrophysiological and metabolic evidence, the authors propose that varied semiological patterns could be explained by the extent of involvement of a network that includes at least the OFC, ACC, AI, and temporal lobe. Tailored resections for OFE may lead to a good overall outcome.

Investigation of networks underlying hyperkinetic seizures utilizing ictal SPECT

Objective

To study neural networks involved in hyperkinetic seizures (HKS) using ictal SPECT.

Methods

We retrospectively identified 18 patients with HKS evaluated at the Cleveland Clinic between 2005 and 2015 with video-EEG monitoring and ictal SPECT. Semiology was confirmed by the consensus of 2 epileptologists' independent reviews and classified as type 1, 2, or 3 HKS. SPECT data were analyzed by 2 independent physicians using a z score of 1.5. Ictal hyperperfusion patterns for each group were analyzed visually and with SPM. Spatial normalization to Montreal Neurological Institute space for each patient’s data was performed, followed by flipping of data from patients with left-sided ictal onset to the right side. Finally, an average z score map for each group was calculated.

Results

Visual analysis and SPM identified different patterns of ictal hyperperfusion in the 3 subtypes of HKS. Type 1 seizures showed hyperperfusion in a more anteriorly located network involving the anterior insula, orbitofrontal cortex, cingulate, and anterior perisylvian region and rostral midbrain. Type 2 seizures were associated with hyperperfusion in a more caudally located network involving the orbitofrontal cortex, cingulate (middle and posterior), basal ganglia, thalami, and cerebellum. Type 3 seizures showed a mixed pattern of SPECT hyperperfusion involving the temporal pole and anterior perisylvian region.

Conclusions

Each of the 3 different semiologic subtypes of HKS is associated with distinct patterns of hyperperfusion, providing further insight into the neural networks involved. This knowledge may inform placement of invasive EEG electrodes in patients with HKS semiology undergoing presurgical evaluation.

Superior Frontal Sulcus Focal Cortical Dysplasia Type II: An MRI, PET, and Quantified SEEG Study

Purpose: The superior frontal sulcus (SFS), located in the prefrontal and premotor cortex, is considered as one of the common locations of focal cortical dysplasia (FCD). However, the characteristics of seizures arising from this area are incompletely known. The primary purpose of this study was to investigate the clinical features and the epileptic networks of seizures originating from the SFS.

Methods: We included seventeen patients with type II FCD within the SFS. SFS was identified both visually and automatically. Semiological features were evaluated and grouped. Interictal 18FDG-PET imaging in all patients was compared to controls using statistical parametric mapping (SPM-PET). In those subjects with stereoelectroencephalography (SEEG), two different quantitative intracranial electroencephalography analyses were applied. Finally, the locations of the SFS-related hypometabolic regions and epileptogenic zones (EZs) were transformed into standard space for group analysis.

Results: We identified two semiological groups. Group 1 (9/17) showed elementary motor signs (head version and tonic posturing), while group 2 (8/17) exhibited complex motor behavior (fear, hypermotor, and ictal pouting). Based on SPM-PET, an SFS-supplementary motor area (SMA) epileptic propagation network was found in group 1, and an SFS-middle cingulate cortex (MCC)-pregenual anterior cingulate cortex (pACC) propagation network was discovered in group 2. Intracranial EEG analysis suggested similar affected structures with high epileptogenicity. The SFS-related hypometabolic regions and EZs in these groups showed a posterior-anterior spatial relationship.

Conclusions: Even though originating from the spatially restricted cortex, SFS seizures can be divided into two groups based on semiological features. The SFS-SMA and SFS-MCC-pACC epileptic propagation networks may play pivotal roles in the generation of different semiologies. The posterior-anterior spatial relationship of both hypometabolic regions and EZs provides potentially useful information for distinguishing different types of SFS seizures and surgical evaluation.

Electroclinical features of insulo-opercular epilepsy: an SEEG and PET study

Objective

To report clinical experience with presurgical evaluation in patients with insulo‐opercular epilepsy. Quantitative analysis on PET imaging and stereoelectroencephalography (SEEG) signals was used to summarize their electroclinical features.

Methods

Twenty‐two patients with focal epilepsy arising from the insular and/or opercular cortex according to SEEG were retrospectively analyzed. Presurgical noninvasive data were analyzed in detail. Interictal PET data of patients were then statistically compared with those of healthy controls to identify the interictal hypometabolic network. The epileptogenicity index (EI) of ictal SEEG signal was computed to identify areas of spread at the beginning of seizure onset.

Results

Focal tonic seizures of the face and/or neck (16/22, 73%) were the most prevalent early objective signs. Epileptic discharges in the interictal and ictal scalp‐EEG mostly showed an ipsilateral perisylvian distribution. Statistical analysis of interictal PET showed significant hypometabolism in the insular lobe, central operculum, supplementary motor area, middle cingulate cortex, bilateral caudate nuclei, and putamen. According to the EI analysis, insulo‐opercular epilepsy could be classified as insulo‐opercular epilepsy (50%), opercular epilepsy (41%), and insular cortex epilepsy (9%).

Significance

Clinical diagnosis of insulo‐opercular epilepsy is challenging because of its complex seizure semiology and nonlocalizing discharges on scalp‐EEG. A common hypometabolic network involving the insulo‐opercular cortex, mesial frontal cortex and subcortical nuclei may be involved in the organization of the insulo‐opercular epilepsy network. Furthermore, quantified SEEG analysis suggested that pure insular epilepsy is rare, and the close connection between insular and opercular cortex necessitates SEEG implantation to define the epileptogenic zone.