Value of stereo-electroencephalogram in reoperation of patients with pharmacoresistant epilepsy: a single center, retrospective study

Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain

OBJECTIVE

The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zone (EZ).

METHODS

HFOs were detected in patients with focal epilepsy who underwent SEEG. Subsequently, HFOs within the seizure-onset and early spread zones were defined as pathological HFOs, whereas others were defined as physiological. Three features of HFOs were identified at the channel level, namely, morphological repetition, rhythmicity, and phase-amplitude coupling (PAC). A machine-learning (ML) classifier was then built to distinguish two HFO types at the channel level by application of the above-mentioned features, and the contributions were quantified. Further verification of the characteristics and classifier performance was performed in relation to various conscious states, imaging results, EZ location, and surgical outcomes.

RESULTS

Thirty-five patients were included in this study, from whom 166  104 pathological HFOs in 255 channels and 53 374 physiological HFOs in 282 channels were entered into the analysis pipeline. The results revealed that the morphological repetitions of pathological HFOs were markedly higher than those of the physiological HFOs; this was also observed for rhythmicity and PAC. The classifier exhibited high accuracy in differentiating between the two forms of HFOs, as indicated by an area under the curve (AUC) of 0.89. Both PAC and rhythmicity contributed significantly to this distinction. The subgroup analyses supported these findings.

SIGNIFICANCE

The suggested HFO features can accurately distinguish between pathological and physiological channels substantially improving its usefulness in clinical localization.

PLAIN LANGUAGE SUMMARY

In this study, we computed three quantitative features associated with HFOs in each SEEG channel and then constructed a machine learning-based classifier for the classification of pathological and physiological channels. The classifier performed well in distinguishing the two channel types under different levels of consciousness as well as in terms of imaging results, EZ location, and patient surgical outcomes.

Association between Removal of High-Frequency Oscillations and the Effect of Epilepsy Surgery: A Meta-Analysis

BACKGROUND

 High-frequency oscillations (HFOs) are spontaneous electroencephalographic (EEG) events that occur within the frequency range of 80 to 500 Hz and consist of at least four distinct oscillations that stand out from the background activity. They can be further classified into "ripples" (80-250 Hz) and "fast ripples" (FR; 250-500 Hz) based on different frequency bands. Studies have indicated that HFOs may serve as important markers for identifying epileptogenic regions and networks in patients with refractory epilepsy. Furthermore, a higher extent of removal of brain regions generating HFOs could potentially lead to improved prognosis. However, the clinical application criteria for HFOs remain controversial, and the results from different research groups exhibit inconsistencies. Given this controversy, the aim of this study was to conduct a meta-analysis to explore the utility of HFOs in predicting postoperative seizure outcomes by examining the prognosis of refractory epilepsy patients with varying ratios of HFO removal.

METHODS

 Prospective and retrospective studies that analyzed HFOs and postoperative seizure outcomes in epilepsy patients who underwent resective surgery were included in the meta-analysis. The patients in these studies were grouped based on the ratio of HFOs removed, resulting in four groups: completely removed FR (C-FR), completely removed ripples (C-Ripples), mostly removed FR (P-FR), and partial ripples removal (P-Ripples). The prognosis of patients within each group was compared to investigate the correlation between the ratio of HFO removal and patient prognosis.

RESULTS

 A total of nine studies were included in the meta-analysis. The prognosis of patients in the C-FR group was significantly better than that of patients with incomplete FR removal (odds ratio [OR] = 6.62; 95% confidence interval [CI]: 3.10-14.15; p < 0.00001). Similarly, patients in the C-Ripples group had a more favorable prognosis compared with those with incomplete ripples removal (OR = 4.45; 95% CI: 1.33-14.89; p = 0.02). Patients in the P-FR group had better prognosis than those with a majority of FR remaining untouched (OR = 6.23; 95% CI: 2.04-19.06; p = 0.001). In the P-Ripples group, the prognosis of patients with a majority of ripples removed was superior to that of patients with a majority of ripples remaining untouched (OR = 8.14; 95% CI: 2.62-25.33; p = 0.0003).

CONCLUSIONS

 There is a positive correlation between the greater removal of brain regions generating HFOs and more favorable postoperative seizure outcomes. However, further investigations, particularly through clinical trials, are necessary to justify the clinical application of HFOs in guiding epilepsy surgery.

Seizure-free outcome and safety of repeated epilepsy surgery for persistent or recurrent seizures

OBJECTIVE

Reoperation may be an option for select patients with unsatisfactory seizure control after their first epilepsy surgery. The aim of this study was to describe the seizure-free outcome and safety of repeated epilepsy surgery in our tertiary referral center.

METHODS

Thirty-eight patients with focal refractory epilepsy, who underwent repeated epilepsy surgeries and had a minimum follow-up time of 12 months after reoperation, were included. Systematic reevaluation, including comprehensive neuroimaging and noninvasive (n = 38) and invasive (n = 25, 66%) video-electroencephalography monitoring, was performed. Multimodal 3D resection maps were created for individual patients to allow personalized reoperation.

RESULTS

The median time between the first operation and reoperation was 74 months (range 5-324 months). The median age at reoperation was 34 years (range 1-74 years), and the median follow-up was 38 months (range 13-142 months). Repeat MRI after the first epilepsy surgery showed an epileptogenic lesion in 24 patients (63%). The reoperation was temporal in 18 patients (47%), extratemporal in 9 (24%), and multilobar in 11 (29%). The reoperation was left hemispheric in 24 patients (63%), close to eloquent cortex in 19 (50%), and distant from the initial resection in 8 (21%). Following reoperation, 27 patients (71%) became seizure free (Engel class I), while 11 (29%) continued to have seizures. There were trends toward better outcome in temporal lobe epilepsy and for unilobar resections adjacent to the initial surgery, but there was no difference between MRI lesional and nonlesional patients. In all subgroups, Engel class I outcome was at least 50%. Perioperative complications occurred in 4 patients (11%), with no fatalities.

CONCLUSIONS

Reoperation for refractory focal epilepsy is an effective and safe option in patients with persistent or recurrent seizures after initial epilepsy surgery. A thorough presurgical reevaluation is essential for favorable outcome.

Brain-clinical signatures for vagus nerve stimulation response

AIM

Vagus nerve stimulation (VNS) is a valuable treatment for drug-resistant epilepsy (DRE) without the indication of surgical resection. The clinical heterogeneity of DRE has limited the optimal indication of choice and diagnosis prediction. The study aimed to explore the correlations of brain-clinical signatures with the clinical phenotype and VNS responsiveness.

METHODS

A total of 89 DRE patients, including VNS- (n = 44) and drug-treated (n = 45) patients, were retrospectively recruited. The brain-clinical signature consisted of demographic information and brain structural deformations, which were measured using deformation-based morphometry and presented as Jacobian determinant maps. The efficacy and presurgical differences between these two cohorts were compared. Then, the potential of predicting VNS response using brain-clinical signature was investigated according to the different prognosis evaluation approaches.

RESULTS

The seizure reduction was higher in the VNS-treated group (42.50%) as compared to the drug-treated group (12.09%) (p = 0.11). Abnormal imaging representation, showing encephalomalacia (p_corrected  = 0.03), was commonly observed in the VNS-treated group (p = 0.04). In the patients treated with VNS, the mild/subtle brain abnormalities indicated higher seizure frequency (p = 0.03) and worse VNS response (p = 0.04). The partial least square regression analysis showed a moderate prediction potential of brain-clinical signature for VNS response (p < 0.01). The increase in the pre-VNS seizure frequency and structural etiology could indicate a worse prognosis (higher McHugh classification).

CONCLUSION

The brain-clinical signature illustrated its clinical potential in predicting the VNS response, which might allow clinicians to personalize treatment decisions for DRE patients.

Metabolic phenotyping of hand automatisms in mesial temporal lobe epilepsy

Purpose

Hand automatisms (HA) are common clinical manifestations in mesial temporal lobe epilepsy. However, the location of the symptomatogenic zone (EZ) in HA as well as the networks involved, are still unclear. To have a better understanding of HA underlying mechanisms, we analyzed images from interictal [¹⁸F] fluorodeoxyglucose-positron emission tomography (FDG-PET) in patients with mesial temporal lobe epilepsy (mTLE).

Methods

We retrospectively recruited 79 mTLE patients and 18 healthy people that substituted the control group for the analysis. All patients underwent anterior temporal lobectomy and were seizure-free. Based on the semiology of the HA occurrence, the patients were divided into three subgroups: patients with unilateral HA (Uni-HA), with bilateral HA (Bil-HA) and without HA (None-HA). We performed the intergroup comparison analysis of the interictal FDG-PET images and compared the functional connectivity within metabolic communities.

Results

Our analysis showed that the metabolic patterns varied among the different groups. The Uni-HA subgroup had significant differences in the extratemporal lobe brain areas, mostly in the ipsilateral supplementary motor area (SMA) and middle cingulate cortex (MCC) when compared to the healthy control group. The Bil-HA subgroup demonstrated that the bilateral SMA and MCC areas were differentially affected, whereas in the None-HA subgroup the differences were evident in limited brain areas. The metabolic network involving HA showed a constrained network embedding the SMA and MCC brain regions. Furthermore, the increased metabolic synchronization between SMA and MCC was significantly correlated with HA.

Conclusion

The metabolic pattern of HA was most conspicuous in SMA and MCC brain regions. Increased metabolic synchronization within SMA and MCC was considered as the major EZ of HA.

Metabolic pattern analysis allowed allocation of the symptomatogenic zone (EZ) and brain network of hand automatisms (HA) in mesial temporal lobe epilepsy (mTLE).

The involved network of bilateral HA was larger than the unilateral one, probably due to the occurrence of contralateral dystonic posturing.

Increased metabolic synchronization within supplementary motor area (SMA) and middle cingulate cortex (MCC) regions were engaged in the representation and modulation of HA, suggesting these regions as the EZ for HA.

Neuroimaging gradient alterations and epileptogenic prediction in focal cortical dysplasia Ⅲa

INTRODUCTION

Focal cortical dysplasia Type Ⅲa (FCD Ⅲa) is a highly prevalent temporal lobe epilepsy but the seizure outcomes are not satisfactory after epilepsy surgery. Hence, quantitative neuroimaging, epileptogenic alterations, as well as their values in guiding surgery are worth exploring.

METHODS

We examined 69 patients with pathologically verified FCD Ⅲa using multimodal neuroimaging and stereoelectroencephalography (SEEG). Among them, 18 received postoperative imaging which showed the extent of surgical resection and 9 underwent SEEG implantation. We also explored neuroimaging gradient alterations along with the distance to the temporal pole. Subsequently, the machine learning regression model was employed to predict whole-brain epileptogenicity. Lastly, the correlation between neuroimaging or epileptogenicity and surgical cavities was assessed.

RESULTS

FCD Ⅲa displayed neuroimaging gradient alterations on the temporal neocortex, morphology-signal intensity decoupling, low similarity of intra-morphological features and high similarity of intra-signal intensity features. The support vector regression model was successfully applied at the whole-brain level to calculate the continuous epileptogenic value at each vertex (mean-squared error = 13.8 ± 9.8).

CONCLUSION

Our study investigated the neuroimaging gradient alterations and epileptogenicity of FCD Ⅲa, along with their potential values in guiding suitable resection range and in predicting postoperative seizure outcomes. The conclusions from this study may facilitate an accurate presurgical examination of FCD Ⅲa. However, further investigation including a larger cohort is necessary to confirm the results.

The role of stereo-electroencephalography to localize the epileptogenic zone in children with nonlesional brain magnetic resonance imaging

OBJECTIVE

This study aimed to assess the clinical outcome and outcome predictive factors in pediatric epilepsy patients evaluated with stereo-electroencephalography (SEEG).

METHODS

Thirty-eight patients who underwent SEEG implantation at the Pediatric Epilepsy Center in New York Presbyterian Hospital between June 2014 and December 2019 were enrolled for retrospective chart review. Postoperative seizure outcomes were evaluated in patients with at least 12-months follow up. Meta-analysis was conducted via electronic literature search of data reported from 2000 to 2020 to evaluate significant surgical outcome predictors for SEEG evaluation in the pediatric population.

RESULTS

In the current case series of 25 postsurgical patients with long-term follow up, 16 patients (64.0%) were seizure free. An additional 7 patients (28.0%) showed significant seizure improvement and 2 patients (8.0%) showed no change in seizure activity. Patients with nonlesional magnetic resonance imaging (MRI) achieved seizure freedom in 50% (5/10) of cases. By comparison, 73% (11/15) of patients with lesional MRI achieved seizure freedom. Out of 12 studies, 158 pediatric patients were identified for inclusion in a meta-analysis of the effectiveness of SEEG. Seizure freedom was reported 54.4% (n = 86/158) of patients at last follow up. Among patients with nonlesional MRI, 45% (n = 24) achieved seizure freedom compared with patients with lesional MRI findings (61.2%, n:= 60) (p = 0.02). The risk for seizure recurrence was 2.15 times higher [95% confidence interval [CI] 1.06-4.37, p = 0.033] in patients diagnosed with nonlesional focal epilepsy compared to those with lesional epilepsy [ 1.49 (95% CI 1.06-2.114, p = 0.021].

CONCLUSION

Evaluation by SEEG implantation in pediatric epilepsy is effective in localizing the epileptogenic zone with favorable outcome. Presence of a non-lesional brain MRI was associated with lower chances of seizure freedom. Further research is warranted to improve the efficacy of SEEG in localizing the epileptogenic zone in pediatric patients with non-lesional brain MRI.

Neuroimaging Phenotyping and Assessment of Structural-Metabolic-Electrophysiological Alterations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa

BACKGROUND

Focal cortical dysplasia IIIa (FCD IIIa) is a common histopathological finding in temporal lobe epilepsy. However, subtle alterations in the temporal neocortex of FCD IIIa renders presurgical diagnosis and definition of the resective range challenging.

PURPOSE

To explore neuroimaging phenotyping and structural-metabolic-electrophysiological alterations in FCD IIIa.

STUDY TYPE

Retrospective.

SUBJECTS

One hundred and sixty-seven subjects aged 4-39 years, including 64 FCD IIIa patients, 89 healthy controls and 14 FCD I patients as disease controls.

FIELD STRENGTH/SEQUENCE

3 T, fast-spin-echo T₂ -weighted fluid-attenuated inversion recovery (FLAIR), synthetic T₁ -weighted magnetization prepared rapid acquisition gradient echo (MPRAGE).

ASSESSMENT

Surface-based linear model was applied to reveal neuroimaging phenotyping in FCD IIIa and assess its relationship with clinical variables. Logistic regression was implemented to identify FCD IIIa patients. Epileptogenicity mapping (EM) was conducted to explore the structural-metabolic-electrophysiological alterations in temporal neocortex of FCD IIIa.

STATISTICAL TESTS

Student's t-test was applied to determine the significance of paired differences. Calibration curves were plotted to assess the goodness-of-fit (GOF) of the models, combined with the Hosmer-Lemeshow test.

RESULTS

FCD IIIa exhibited widespread hyperintensities in temporal neocortex, and these alterations correlated with disease duration (P_uncorrected  < 0.01). Machine learning model accurately identified 84.4% of FCD IIIa patients, 92.1% of healthy controls and 92.9% of FCD I patients. Cross-modality analysis showed a significant negative correlation between FLAIR hyperintensity and positron emission tomography hypometabolism P < 0.01). Furthermore, epileptogenic cortices were located predominantly in brain regions with FLAIR hyperintensity and hypometabolism.

DATA CONCLUSION

FCD IIIa exhibited widespread temporal neocortex FLAIR hyperintensity. Automated machine learning of neuroimaging patterns is conducive for accurate identification of FCD IIIa. The degree and distribution of morphological alterations related to the extent of metabolic and epileptogenic abnormalities, lending support to its potential value for reduction of the radiative and invasive approaches during presurgical workup.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Stereoelectroencephalography in epilepsy, cognitive neurophysiology, and psychiatric disease: safety, efficacy, and place in therapy

For patients with drug-resistant epilepsy, surgical intervention may be an effective treatment option if the epileptogenic zone (EZ) can be well localized. Subdural strip and grid electrode (SDE) implantations have long been used as the mainstay of intracranial seizure localization in the United States. Stereoelectroencephalography (SEEG) is an alternative approach in which depth electrodes are placed through percutaneous drill holes to stereotactically defined coordinates in the brain. Long used in certain centers in Europe, SEEG is gaining wider popularity in North America, bolstered by the advent of stereotactic robotic assistance and mounting evidence of safety, without the need for catheter-based angiography. Rates of clinically significant hemorrhage, infection, and other complications appear lower with SEEG than with SDE implants. SEEG also avoids unnecessary craniotomies when seizures are localized to unresectable eloquent cortex, found to be multifocal or nonfocal, or ultimately treated with stereotactic procedures such as laser interstitial thermal therapy (LITT), radiofrequency thermocoagulation (RF-TC), responsive neurostimulation (RNS), or deep brain stimulation (DBS). While SDE allows for excellent localization and functional mapping on the cortical surface, SEEG offers a less invasive option for sampling disparate brain areas, bilateral investigations, and deep or medial targets. SEEG has shown efficacy for seizure localization in the temporal lobe, the insula, lesional and nonlesional extra-temporal epilepsy, hypothalamic hamartomas, periventricular nodular heterotopias, and patients who have had prior craniotomies for resections or grids. SEEG offers a valuable opportunity for cognitive neurophysiology research and may have an important role in the study of dysfunctional networks in psychiatric disease and understanding the effects of neuromodulation.