Reliability of visual review of intracranial electroencephalogram in identifying the seizure onset zone: A systematic review and implications for the accuracy of automated methods.

Visual review of intracranial electroencephalography (iEEG) is often an essential component for defining the zone of resection for epilepsy surgery. Unsupervised approaches using machine and deep learning are being employed to identify seizure onset zones (SOZs). This prompts a more comprehensive understanding of the reliability of visual review as a reference standard. We sought to summarize existing evidence on the reliability of visual review of iEEG in defining the SOZ for patients undergoing surgical workup and understand its implications for algorithm accuracy for SOZ prediction. We performed a systematic literature review on the reliability of determining the SOZ by visual inspection of iEEG in accordance with best practices. Searches included MEDLINE, Embase, Cochrane Library, and Web of Science on May 8, 2022. We included studies with a quantitative reliability assessment within or between observers. Risk of bias assessment was performed with QUADAS-2. A model was developed to estimate the effect of Cohen kappa on the maximum possible accuracy for any algorithm detecting the SOZ. Two thousand three hundred thirty-eight articles were identified and evaluated, of which one met inclusion criteria. This study assessed reliability between two reviewers for 10 patients with temporal lobe epilepsy and found a kappa of .80. These limited data were used to model the maximum accuracy of automated methods. For a hypothetical algorithm that is 100% accurate to the ground truth, the maximum accuracy modeled with a Cohen kappa of .8 ranged from .60 to .85 (F-2). The reliability of reviewing iEEG to localize the SOZ has been evaluated only in a small sample of patients with methodologic limitations. The ability of any algorithm to estimate the SOZ is notably limited by the reliability of iEEG interpretation. We acknowledge practical limitations of rigorous reliability analysis, and we propose design characteristics and study questions to further investigate reliability.

Mechanistic Analysis of Micro-Neurocircuits Underlying the Epileptogenic Zone in Focal Cortical Dysplasia Patients.

We aim to explore the microscopic neurophysiology of focal cortical dysplasia (FCD) induced epileptogenesis in specific macroscopic brain regions, therefore mapping a micro-macro neuronal network that potentially indicates the epileptogenic mechanism. Epileptic and relatively non-epileptic temporal neocortex specimens were resected from FCD and mesial temporal lobe epilepsy (mTLE) patients, respectively. Whole-cell patch-clamping was performed on cells from the seizure onset zone (SOZ) and non-SOZ inside the epileptogenic zone (EZ) of FCD patients, as well as the non-epileptic neocortex of mTLE patients. Microscopic data were recorded, including membrane characteristics, spontaneous synaptic activities, and evoked action potentials. Immunohistochemistry was also performed on parvalbumin-positive (PV+) interneurons. We found that SOZ interneurons exhibited abnormal neuronal expression and distribution as well as reduced overall function compared with non-SOZ and mTLE interneurons. The SOZ pyramidal cells experienced higher excitation but lower inhibition than the mTLE controls, whereas the non-SOZ pyramidal cells exhibited intermediate excitability. Action potential properties of both types of neurons also suggested more synchronized neuronal activity inside the EZ, particularly inside the SOZ. Together, our research provides evidence for a potential neurocircuit underlying SOZ epileptogenesis and non-SOZ seizure susceptibility. Further investigation of this microscopic network may promote understanding of the mechanism of FCD-induced epileptogenesis.

Mapping whole brain seizure network recruitment with optogenetic kindling.

Epilepsy is often labeled as a network disorder, though a common view of seizures holds that they initiate in a singular onset zone before expanding contiguously outward. A recent report by Choy et al. (Choy M, Dadgar-Kiani E, Cron GO, Duffy BA, Schmid F, Edelman BJ, Asaad M, Chan RW, Vahdat S, Lee JH. Neuron 2021 Oct 23: S0896-6273(21)00778-9.) leverages new tools to study whole brain dynamics during epileptic seizures originating in the hippocampus. Cell-type-specific kindling and functional imaging revealed how various brain regions were recruited to seizures and uncovered a novel form of migrating seizure core.

Virtual epileptic patient brain modeling: Relationships with seizure onset and surgical outcome.

OBJECTIVE: The virtual epileptic patient (VEP) is a large-scale brain modeling method based on virtual brain technology, using stereoelectroencephalography (SEEG), anatomical data (magnetic resonance imaging [MRI] and connectivity), and a computational neuronal model to provide computer simulations of a patient's seizures. VEP has potential interest in the presurgical evaluation of drug-resistant epilepsy by identifying regions most likely to generate seizures. We aimed to assess the performance of the VEP approach in estimating the epileptogenic zone and in predicting surgical outcome. METHODS: VEP modeling was retrospectively applied in a cohort of 53 patients with pharmacoresistant epilepsy and available SEEG, T1-weighted MRI, and diffusion-weighted MRI. Precision recall was used to compare the regions identified as epileptogenic by VEP (EZVEP ) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZC ). In 28 operated patients, we compared the VEP results and clinical analysis with surgical outcome. RESULTS: VEP showed a precision of 64% and a recall of 44% for EZVEP detection compared to EZC . There was a better concordance of VEP predictions with clinical results, with higher precision (77%) in seizure-free compared to non-seizure-free patients. Although the completeness of resection was significantly correlated with surgical outcome for both EZC and EZVEP , there was a significantly higher number of regions defined as epileptogenic exclusively by VEP that remained nonresected in non-seizure-free patients. SIGNIFICANCE: VEP is the first computational model that estimates the extent and organization of the epileptogenic zone network. It is characterized by good precision in detecting epileptogenic regions as defined by a combination of visual analysis and EI. The potential impact of VEP on improving surgical prognosis remains to be exploited. Analysis of factors limiting the performance of the actual model is crucial for its further development.

Characterizing the seizure onset zone and epileptic network using EEG-fMRI in a rat seizure model.

Accurate epileptogenic zone (EZ) or seizure onset zone (SOZ) localization is crucial for epilepsy surgery optimization. Previous animal and human studies on epilepsy have reported that changes in blood oxygen level-dependent (BOLD) signals induced by epileptic events could be used as diagnostic markers for EZ or SOZ localization. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recording is gaining interest as a non-invasive tool for preoperative epilepsy evaluation. However, EEG-fMRI studies have reported inconsistent and ambiguous findings. Therefore, it remains unclear whether BOLD responses can be used for accurate EZ or SOZ localization. In this study, we used simultaneous EEG-fMRI recording in a rat model of 4-aminopyridine-induced acute focal seizures to assess the spatial concordance between individual BOLD responses and the SOZ. This was to determine the optimal use of simultaneous EEG-fMRI recording in the SOZ localization. We observed a high spatial consistency between BOLD responses and the SOZ. Further, dynamic BOLD responses were consistent with the regions where the seizures were propagated. These results suggested that simultaneous EEG-fMRI recording could be used as a noninvasive clinical diagnostic technique for localizing the EZ or SOZ and could be an effective tool for mapping epileptic networks.

Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging.

Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand-alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR-negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel-wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion-weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR-negative epilepsy patients.

Leveraging electrophysiologic correlates of word encoding to map seizure onset zone in focal epilepsy: Task-dependent changes in epileptiform activity, spectral features, and functional connectivity.

OBJECTIVE: Verbal memory dysfunction is common in focal, drug-resistant epilepsy (DRE). Unfortunately, surgical removal of seizure-generating brain tissue can be associated with further memory decline. Therefore, localization of both the circuits generating seizures and those underlying cognitive functions is critical in presurgical evaluations for patients who may be candidates for resective surgery. We used intracranial electroencephalographic (iEEG) recordings during a verbal memory task to investigate word encoding in focal epilepsy. We hypothesized that engagement in a memory task would exaggerate local iEEG feature differences between the seizure onset zone (SOZ) and neighboring tissue as compared to wakeful rest ("nontask"). METHODS: Ten participants undergoing presurgical iEEG evaluation for DRE performed a free recall verbal memory task. We evaluated three iEEG features in SOZ and non-SOZ electrodes during successful word encoding and compared them with nontask recordings: interictal epileptiform spike (IES) rates, power in band (PIB), and relative entropy (REN; a functional connectivity measure). RESULTS: We found a complex pattern of PIB and REN changes in SOZ and non-SOZ electrodes during successful word encoding compared to nontask. Successful word encoding was associated with a reduction in local electrographic functional connectivity (increased REN), which was most exaggerated in temporal lobe SOZ. The IES rates were reduced during task, but only in the non-SOZ electrodes. Compared with nontask, REN features during task yielded marginal improvements in SOZ classification. SIGNIFICANCE: Previous studies have supported REN as a biomarker for epileptic brain. We show that REN differences between SOZ and non-SOZ are enhanced during a verbal memory task. We also show that IESs are reduced during task in non-SOZ, but not in SOZ. These findings support the hypothesis that SOZ and non-SOZ respond differently to task and warrant further exploration into the use of cognitive tasks to identify functioning memory circuits and localize SOZ.

Auras in intractable frontal lobe epilepsy: Clinical characteristics, values, and limitations.

Auras are essential in preoperative evaluation and can provide valuable information for delineating seizure onset zones. Frontal lobe epilepsy (FLE) is the second most common focal epilepsy, while a few studies have focused on auras in FLE. To better understand FLE, we analyzed the clinical characteristics, values, and limitations of auras in FLE. The incidence rate of aura in FLE was 37.9% in our study. We included 54 patients and 76 auras in 11 categories were reported. The rate of auras in the decreasing order are as follows: autonomic aura; emotional aura; somatosensory aura; psychic aura; cephalic aura; abdominal aura; whole-body sensory aura, visual aura; auditory aura; and vestibular and unclassified aura. A significant number of aura types can be reported by FLE patients; autonomic aura was the most frequent category and somatosensory auras are most likely associated with the contralateral motor areas.

Injection of prophylactic lorazepam versus antiseizure drugs on the localization value of ictal SPECT studies and treatment-emergent adverse events: A single-center prospective study.

OBJECTIVE: We aimed to examine the impact of resumption of home antiseizure drugs alone (ASD-) compared with adjunct administration of scheduled intravenous (IV) lorazepam 2 mg every 6 h (ASD+) following ictal single-photon emission computed tomography (SPECT) injection on the localization value of SPECT studies and treatment-emergent adverse events (TEAEs). METHODS: We conducted a prospective study at Mayo Clinic inpatient epilepsy monitoring unit (EMU) between January 2018 and May 2020 in Jacksonville, Florida. The ASD- and ASD+ groups were compared for concordance of SPECT studies with the epilepsy surgical conference (ESC) consensus or intracranial electroencephalography (icEEG) findings as reference. Treatment-emergent adverse events, obtained from surveys at 24 h and one week postictal SPECT injection, were also compared between both groups. RESULTS: Twenty-two consecutive patients with temporal (eight patients, 36%) and extratemporal (14 patients, 64%) epilepsy were included: 12 ASD+ and 10 ASD-. The two groups were well matched with regard to clinical and ictal SPECT injection characteristics including the occurrence of seizure between ictal and interictal SPECT injections. The localization value of SPECT studies was similar in the two groups. Patients in the ASD+ group reported higher rates of dizziness and excessive sedation at 24 h (p-value = 0.008). Fourteen patients (64%) underwent icEEG monitoring. For the entire cohort, the localization concordance of SPECT analysis by statistical parametric mapping (SPM) was superior to raw ictal SPECT (p-value = 0.003) and subtraction ictal SPECT coregistered to magnetic resonance imaging (MRI) (SISCOM; p-value = 0.021). Eventually, seven patients (31.8%) underwent resective brain surgery of whom four (57.1%) became seizure-free (median follow-up = 22 months). CONCLUSIONS: Our findings suggest that resuming home ASDs without the addition of scheduled IV lorazepam following inpatient ictal SPECT injection is equally efficacious for seizure onset zone (SOZ) localization on SPECT studies, especially SPM. This approach is also associated with fewer transient TEAEs and lower financial cost with no difference in preventing seizure between ictal and interictal SPECT injections.

Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction.

The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition.

Localizing the seizure onset zone by comparing patient postictal hypoperfusion to healthy controls.

Arterial spin labeling (ASL) MRI can provide seizure onset zone (SOZ) localizing information in up to 80% of patients. Clinical implementation of this technique is limited by the need to obtain two scans per patient: a postictal scan that is subtracted from an interictal scan. We aimed to determine whether it is possible to limit the number of ASL scans to one per patient by comparing patient postictal ASL scans to baseline scans of 100 healthy controls. Eighteen patients aged 20-55 years underwent ASL MRI <90 min after a seizure and during the interictal period. Each postictal cerebral blood flow (CBF) map was statistically compared to average baseline CBF maps from 100 healthy controls (pvcASL; patient postictal CBF vs. control baseline CBF). The pvcASL maps were compared to subtraction ASL maps (sASL; patient baseline CBF minus patient postictal CBF). Postictal CBF reductions from pvcASL and sASL maps were seen in 17 of 18 (94.4%) and 14 of 18 (77.8%) patients, respectively. Maximal postictal hypoperfusion seen in pvcASL and sASL maps was concordant with the SOZ in 10 of 17 (59%) and 12 of 14 (86%) patients, respectively. In seven patients, both pvcASL and sASL maps showed similar results. In two patients, sASL showed no significant hypoperfusion, while pvcASL showed significant hypoperfusion concordant with the SOZ. We conclude that pvcASL is clinically useful and although it may have a lower overall concordance rate than sASL, pvcASL does provide localizing or lateralizing information for specific cases that would be otherwise missed through sASL.

Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy.

High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.

Resting-state functional magnetic resonance imaging with independent component analysis for presurgical seizure onset zone localization: A systematic review and meta-analysis.

OBJECTIVE: One of the greatest challenges of achieving successful surgical outcomes in patients with epilepsy is the ability to properly localize the seizure onset zone (SOZ). Many techniques exist for localizing the SOZ, including intracranial electroencephalography, magnetoencephalography, and stereoelectroencephalography. Recently, resting-state functional magnetic resonance imaging (rs-fMRI) in conjunction with independent component analysis (ICA) has been utilized for presurgical planning of SOZ resection, with varying results. In this meta-analysis, we analyze the current role of rs-fMRI in identifying the SOZ for presurgical planning for patients with drug-resistant epilepsy. Specifically, we seek to demonstrate its current effectiveness compared to other methods of SOZ localization. METHODS: A literature review was conducted using the PubMed, MEDLINE, and Embase databases up to May of 2020. A total of 253 articles were screened, and seven studies were chosen for analysis. Each study was analyzed for SOZ localization by ground truth, SOZ localization by rs-fMRI with ICA, principal component analysis, or intrinsic connectivity contrast, and outcomes of surgery. A meta-analysis was performed to analyze how ground truth compares to rs-fMRI in SOZ localization. RESULTS: The odds ratio comparing ground truth to rs-fMRI was 2.63 (95% confidence interval = 0.66-10.56). Average concordance of rs-fMRI SOZ localization compared with ground truth localization across studies was 71.3%. SIGNIFICANCE: In the hunt for less invasive presurgical planning for epilepsy surgery, rs-fMRI with ICA provides a promising avenue for future standard practice. Our preliminary results show no significant difference in surgical outcomes between traditional standards of SOZ localization and rs-fMRI with ICA. We believe that rs-fMRI could be a step forward in this search. Further investigation comparing rs-fMRI to traditional methods of SOZ localization should be conducted, with the hope of moving toward relying solely on noninvasive screening methods.

Enhanced Regional Functional Connectivity Indicates Seizure Onset Zone.

This project aims to explore if stronger functional connectivity (FC) exists in the maximal BOLD response of EEG/fMRI analysis when it is concordant with seizure-onset-zone (SOZ). Twenty-six patients with drug-resistant focal epilepsy who had an EEG/fMRI and later underwent stereo-EEG implantation were included. Different types of IEDs were labeled in scalp EEG and IED-related maximal BOLD responses were evaluated separately, each constituting one study. After evaluating concordance between maximal BOLD and SOZ, twenty-seven studies were placed in the concordant group and eight in the discordant group. We evaluated the local connectivity and ipsilaterally distant connectivity difference between the maximal BOLD and the contralateral homotopic region. Significantly stronger local FC was found for the maximal BOLD in the concordant group (p < 0.05, Bonferroni corrected). 52% of the studies in the concordant group and 13% in the discordant group had a significant difference compared to healthy subjects (p < 0.05, uncorrected). The finding suggests that, when concordant with the SOZ, the maximal BOLD is more likely to have stronger local FC compared to its contralateral counterpart. This asymmetry in functional connectivity may help to noninvasively improve the specificity of EEG/fMRI analysis.

Emerging indications for stereotactic laser interstitial thermal therapy in pediatric neurosurgery.

Surgical treatment of deep or difficult to access lesions represents a unique and significant challenge for pediatric neurosurgeons. The introduction of stereotactic magnetic resonance-guided laser interstitial thermal therapy (LITT) over the last decade has had a dramatic impact on the landscape of pediatric neurosurgery. LITT provides a safe and effective option for children with epilepsy from hypothalamic hamartoma that represents a ground-breaking new therapy for a condition which was historically very difficult to treat with previous neurosurgical techniques. LITT has also been used as an alternative surgical technique for mesial temporal sclerosis, focal cortical dysplasia, MR-negative epilepsy, cavernoma-related epilepsy, insular epilepsy, and corpus callosotomy among other epilepsy etiologies. In some cases, LITT has been associated with improved cognitive outcomes compared to standard techniques, as in mesial temporal lobe epilepsy. Initial experiences with LITT for neuro-oncologic processes are also promising. LITT is often attractive to patients and providers as a minimally invasive approach, but the differences in safety and clinical outcome between LITT and traditional approaches are still being studied. In this review, we examine the emerging indications and clinical evidence for LITT in pediatric neurosurgery.

Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy.

The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods.

The predictive value of hypometabolism in focal epilepsy: a prospective study in surgical candidates.

PURPOSE: FDG PET is an established tool in presurgical epilepsy evaluation, but it is most often used selectively in patients with discordant MRI and EEG results. Interpretation is complicated by the presence of remote or multiple areas of hypometabolism, which leads to doubt as to the true location of the seizure onset zone (SOZ) and might have implications for predicting the surgical outcome. In the current study, we determined the sensitivity and specificity of PET localization prospectively in a consecutive unselected cohort of patients with focal epilepsy undergoing in-depth presurgical evaluation. METHODS: A total of 130 patients who underwent PET imaging between 2006 and 2015 matched our inclusion criteria, and of these, 86 were operated on (72% with a favourable surgical outcome, Engel class I). Areas of focal hypometabolism were identified using statistical parametric mapping and concordance with MRI, EEG and intracranial EEG was evaluated. In the surgically treated patients, postsurgical outcome was used as the gold standard for correctness of localization (minimum follow-up 12 months). RESULTS: PET sensitivity and specificity were both 95% in 86 patients with temporal lobe epilepsy (TLE) and 80% and 95%, respectively, in 44 patients with extratemporal epilepsy (ETLE). Significant extratemporal hypometabolism was observed in 17 TLE patients (20%). Temporal hypometabolism was observed in eight ETLE patients (18%). Among the 86 surgically treated patients, 26 (30%) had hypometabolism extending beyond the SOZ. The presence of unilobar hypometabolism, included in the resection, was predictive of complete seizure control (p = 0.007), with an odds ratio of 5.4. CONCLUSION: Additional hypometabolic areas were found in one of five of this group of nonselected patients with focal epilepsy, including patients with "simple" lesional epilepsy, and this finding should prompt further in-depth evaluation of the correlation between EEG findings, semiology and PET. Hypometabolism confined to the epileptogenic zone as defined by EEG and MRI is associated with a favourable postoperative outcome in both TLE and ETLE patients.

Low entropy of interictal gamma oscillations is a biomarker of the seizure onset zone in focal cortical dysplasia type II.

OBJECTIVE: Dynamic changes in the regularity of interictal gamma oscillations (GOs, 30-70 Hz) on intracranial electroencephalography (EEG) reflect focal ictogenesis with epileptogenic neuronal synchronization in focal cortical dysplasia (FCD). We investigated whether the regularity of interictal GOs is a biomarker of the seizure onset zone (SOZ) using multiscale entropy analysis. METHODS: We quantified the regularity of interictal GOs using intracranial EEG data from 1164 electrodes in 13 patients with FCD who were seizure-free postoperatively. The regularity of interictal GOs was quantified as entropy values. Low entropy represents high regularity. We standardized entropy values using Z values for each SOZ, resection area (RA), and the region outside the RA. The cutoff Z values, sensitivity, and specificity for detecting each area were calculated using area under the receiver operating characteristics curves (AUCs). RESULTS: Low Z values represent higher regularity of GOs. The cutoff Z value of ≤-2.09 for the SOZ had a sensitivity of 100% and specificity of 97.1% (AUC = 0.992 ±â€¯0.002). The cutoff Z value of ≤-0.12 for the RA had a sensitivity of 54.2% and specificity of 73.8% (AUC = 0.673 ±â€¯0.019). The cutoff Z value of ≥-0.11 for the region outside the RA had a sensitivity of 73.8% and specificity of 54.2% (AUC = 0.673 ±â€¯0.019). CONCLUSIONS: Low entropy of interictal GOs was a reliable biomarker for the SOZ. Maintained high entropy of interictal GOs may be an auxiliary biomarker for nonepileptogenic regions. SIGNIFICANCE: Low entropy of interictal GOs may be a biomarker for the SOZ in FCD type II.

Quantitative Analysis of [18F]FFMZ and [18F]FDG PET Studies in the Localization of Seizure Onset Zone in Drug-Resistant Temporal Lobe Epilepsy.

BACKGROUND: Positron emission tomography (PET) imaging in epilepsy is an in vivo technique that allows the localization of a possible seizure onset zone (SOZ) during the interictal period. Stereo-electro-encephalography (SEEG) is the gold standard to define the SOZ. The objective of this research was to evaluate the accuracy of PET imaging in localizing the site of SOZ compared with SEEG. METHODS: Seven patients with refractory temporal lobe epilepsy (Ep) and 2 healthy controls (HC) underwent 2 PET scans, one with 2-[18F]-fluoro-2-deoxy-D-glucose (FDG) and another with 2'-[18F]fluoroflumazenil (FFMZ), acquired 1 day apart. FDG was acquired for 10 min (static scan) 1 h after administration. An FFMZ scan was acquired for 60 min from radiopharmaceutical administration in a dynamic mode. Each brain PET image was segmented using a standard template implemented in PMOD 3.8. The pons was used as the reference region for modeling of the nondisplaceable binding potential (BPND)for FFMZ, and to obtain uptake ratios for FDG. SEEG studies of patients were performed as a part of their surgical evaluation to define the SOZ. RESULTS: Well-defined differences between HC and Ep were found with both radiopharmaceuticals, showing the utility to identify abnormal brain regions using quantitative PET imaging. Lateralization of the SOZ findings by PET (lower uptake/binding in a specific brain hemisphere) matched in 86% for FFMZ and 71% for FDG with SEEG data. CONCLUSION: Quantitative PET imaging is an excellent complementary tool that matches reasonably well with SEEG to define SOZ in presurgical evaluation.

Theta band network supporting human episodic memory is not activated in the seizure onset zone.

Episodic memory, everyday memory for events, is frequently impaired in patients with epilepsy. We tested patients undergoing intracranial electroencephalography (intracranial EEG) monitoring for the treatment of medically-refractory epilepsy on a well-characterized paradigm that requires episodic memory. We report that an anatomically diffuse network characterized by theta-band (4-7 Hz) coherence is activated at the time of target selection in a task that requires episodic memory. This distinct network of oscillatory activity is absent when episodic memory is not required. Further, the theta band synchronous network was absent in electrodes within the patient's seizure onset zone (SOZ). Our data provide novel empirical evidence for a set of brain areas that supports episodic memory in humans, and it provides a pathophysiologic mechanism for the memory deficits observed in patients with epilepsy.