EEG in focal and generalized epilepsies: Pearls and perils.

Correctly diagnosing and classifying seizures and epilepsies is vital to ensure a tailored approach to patients with epilepsy. The ILAE seizure classification consists of two main groups: focal and generalized. Establishing if a seizure is focal or generalized is essential to classify the epilepsy type and the epilepsy syndrome, providing more personalized treatment and counseling about prognosis. EEG is one of the most essential tools for this classification process and further localization of the epileptogenic focus. However, some EEG findings are misleading and may postpone the correct diagnosis and proper treatment. Knowing the most common EEG pitfalls in focal and generalized epilepsies is valuable for clinical practice, avoiding misinterpretations. Some atypical features can be challenging in focal epilepsies, such as secondary bilateral synchrony, focal epileptiform activity induced by hyperventilation and photic stimulation, and non-focal slowing. On the other hand, more than 60 % of persons with idiopathic generalized epilepsies have at least one type of atypical abnormality. In this manuscript, we describe and illustrate some of the most common EEG findings that can make even experienced epileptologists question not only where the epileptogenic focus is but also if the patient has focal or generalized epilepsy. This review summarizes the perils and provide some pearls to assist EEG readers.

A spatial perturbation framework to validate implantation of the epileptogenic zone.

Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.

Basal temporal language area revisited in Japanese language with a language function density map.

We revisited the anatomo-functional characteristics of the basal temporal language area (BTLA), first described by Lüders et al. (1986), using electrical cortical stimulation (ECS) in the context of Japanese language and semantic networks. We recruited 11 patients with focal epilepsy who underwent chronic subdural electrode implantation and ECS mapping with multiple language tasks for presurgical evaluation. A semiquantitative language function density map delineated the anatomo-functional characteristics of the BTLA (66 electrodes, mean 3.8 cm from the temporal tip). The ECS-induced impairment probability was higher in the following tasks, listed in a descending order: spoken-word picture matching, picture naming, Kanji word reading, paragraph reading, spoken-verbal command, and Kana word reading. The anterior fusiform gyrus (FG), adjacent anterior inferior temporal gyrus (ITG), and the anterior end where FG and ITG fuse, were characterized by stimulation-induced impairment during visual and auditory tasks requiring verbal output or not, whereas the middle FG was characterized mainly by visual input. The parahippocampal gyrus was the least impaired of the three gyri in the basal temporal area. We propose that the BTLA has a functional gradient, with the anterior part involved in amodal semantic processing and the posterior part, especially the middle FG in unimodal semantic processing.

Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra.

BACKGROUND: Accurate identification of abnormal electroencephalographic (EEG) activity is pivotal for diagnosing and treating epilepsy. Recent studies indicate that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components can illuminate the drivers of spectral activity changes. NEW METHODS: We analysed intracranial EEG (iEEG) data from 234 subjects, creating a normative map. This map was compared to a cohort of 63 patients with refractory focal epilepsy under consideration for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed, and (iii) the aperiodic exponent. Abnormalities were calculated for each approach in the patient cohort. We evaluated the spatial profiles, assessed their ability to localize abnormalities, and replicated the findings using magnetoencephalography (MEG). RESULTS: Normative maps of relative complete band power and relative periodic band power exhibited similar spatial profiles, while the aperiodic normative map revealed higher exponent values in the temporal lobe. Abnormalities estimated through complete band power effectively distinguished between good and bad outcome patients. Combining periodic and aperiodic abnormalities enhanced performance, like the complete band power approach. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: Sparing cerebral tissue with abnormalities in both periodic and aperiodic activity may result in poor surgical outcomes. Both periodic and aperiodic components do not carry sufficient information in isolation. The relative complete band power solution proved to be the most reliable method for this purpose. Future studies could investigate how cerebral location or pathology influences periodic or aperiodic abnormalities.

How fast does the brain recover after an epileptic seizure?

The postictal state, an abnormal cerebral condition following a seizure until the return to the interictal baseline, is frequently overlooked, despite often exceeding ictal duration and significantly impacting patients' lives. This study analyzes stereo-EEG (SEEG) signal dynamics using permutation entropy to quantify recovery time (postictal alteration time - PAT) in focal epilepsy and its clinical correlations. The average PAT was 4.5 min, extending up to an hour and was highest in temporal epilepsy and hippocampal sclerosis. Correlating with age at seizure onset and at SEEG, PAT provides a solution for operationally defining the postictal state and guiding interventions.

Long-term seizure diary tracking habits in clinical studies: Evidence from the Human Epilepsy Project.

OBJECTIVE: To characterize seizure tracking patterns of people with focal epilepsy using electronic seizure diary entries, and to assess for risk factors associated with poor tracking. METHODS: We analyzed electronic seizure diary data from 410 participants with newly diagnosed focal epilepsy in the Human Epilepsy Project 1 (HEP1). Each participant was expected to record data each day during the study, regardless of seizure occurrence. The primary outcome of this post-hoc analysis was whether each participant properly tracked a seizure diary entry each day during their study participation. Using finite mixture modeling, we grouped patient tracking trajectories into data-driven clusters. Once defined, we used multinomial modeling to test for independent risk factors of tracking group membership. RESULTS: Using over up to three years of daily seizure diary data per subject, we found four distinct seizure tracking groups: consistent, frequent at study onset, occasional, and rare. Participants in the consistent tracking group tracked a median of 92% (interquartile range, IQR: 82%, 99%) of expected days, compared to 47% (IQR:34%, 60%) in the frequent at study onset group, 37% (IQR: 26%, 49%) in the occasional group, and 9% (IQR: 3%, 15%) in the rare group. In multivariable analysis, consistent trackers had lower rates of seizure days per tracked year during their study participation, compared to other groups. SIGNIFICANCE: Future efforts need to focus on improving seizure diary tracking adherence to improve quality of outcome data, particularly in those with higher seizure burden. In addition, accounting for missing data when using seizure diary data as a primary outcome is important in research trials. If not properly accounted for, total seizure burden may be underestimated and biased, skewing results of clinical trials.

The Role of EEG microstates in predicting oxcarbazepine treatment outcomes in patients with newly-diagnosed focal epilepsy.

PURPOSE: Microstates represent the global and topographical distribution of electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepine monotherapy. METHODS: This study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes. RESULTS: In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively. CONCLUSION: This study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.

Intraoperative changes in large-scale thalamic circuitry following laser ablation of hypothalamic hamartomas.

BACKGROUND AND OBJECTIVES: Gelastic seizures due to hypothalamic hamartomas (HH) are challenging to treat, in part due to an incomplete understanding of seizure propagation pathways. Although magnetic resonance imaging-guided laser interstitial thermal therapy (MRgLITT) is a promising intervention to disconnect HH from ictal propagation networks, the optimal site of ablation to achieve seizure freedom is not known. In this study, we investigated intraoperative post-ablation changes in resting-state functional connectivity to identify large-scale networks associated with successful disconnection of HH. METHODS: Children who underwent MRgLITT for HH at two institutions were consecutively recruited and followed for a minimum of one year. Seizure freedom was defined as Engel score of 1A at the last available follow-up. Immediate pre- and post- ablation resting-state functional MRI scans were acquired while maintaining a constant depth of general anesthetic. Multivariable generalized linear models were used to identify intraoperative changes in large-scale connectivity associated with seizure outcomes. RESULTS: Twelve patients underwent MRgLITT for HH, five of whom were seizure-free at their last follow-up. Intraprocedural changes in thalamocortical circuitry involving the anterior cingulate cortex were associated with seizure-freedom. Children who were seizure-free demonstrated an increase and decrease in connectivity to the pregenual and dorsal anterior cingulate cortices, respectively. In addition, children who became seizure-free demonstrated increased thalamic connectivity to the periaqueductal gray immediately following MRgLITT. DISCUSSION: Successful disconnection of HH is associated with intraoperative, large-scale changes in thalamocortical connectivity. These changes provide novel insights into the large-scale basis of gelastic seizures and may represent intraoperative biomarkers of treatment success.

Stereoelectroencephalography-guided radiofrequency thermocoagulation of the epileptogenic zone: a potential treatment and prognostic indicator for subsequent excision surgery.

PURPOSE: To evaluate the safety and efficacy of stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RFTC) for drug-resistant focal epilepsy and investigate the relationship between post-RFTC remission duration and delayed excision surgery effectiveness. METHODS: We conducted a retrospective analysis of 43 patients with drug-resistant focal epilepsy who underwent RFTC via SEEG electrodes. After excluding three, the remaining 40 were classified into subgroups based on procedures and outcomes. Twenty-four patients (60%) underwent a secondary excision surgery. We determined the predictive value of RFTC outcome upon subsequent surgical outcome by categorizing the delayed secondary surgery outcome as success (Engel I/II) versus failure (Engel III/IV). Demographic information, epilepsy characteristics, and the duration of seizure freedom after RFTC were assessed. RESULTS: Among 40 patients, 20% achieved Engel class I with RFTC alone, while 24 underwent delayed secondary excision surgery. Overall, 41.7% attained Engel class I, with a 66.7% success rate combining RFTC with delayed surgery. Seizure freedom duration was significantly longer in the success group (mean 4.9 months, SD = 2.7) versus the failure group (mean 1.9 months, SD = 1.1; P = 0.007). A higher proportion of RFTC-only and delayed surgical success group patients had preoperative lesional findings (p = 0.01), correlating with a longer time to seizure recurrence (p < 0.05). Transient postoperative complications occurred in 10%, resolving within a year. CONCLUSION: This study demonstrates that SEEG-guided RFTC is a safe and potential treatment option for patients with drug-resistant focal epilepsy. A prolonged duration of seizure freedom following RFTC may serve as a predictive marker for the success of subsequent excision surgery.

Epilepsia partialis continua: A review.

Epilepsia partialis continua (EPC) is a rare type of focal motor seizure characterized by continuous, involuntary muscle contractions in a specific part of the body. These contractions usually involve rhythmic, twitching movements and can last for several hours to days. The seizures are usually limited to one part of the body and can be clonic or dystonic. EPC can affect people of all ages but is more common in children and adolescents. The pathophysiology of EPC is complex and depends on the cause. There are several possible causes of EPC including structural brain abnormalities, infections, metabolic and genetic disorders, inflammatory conditions, traumatic brain injury, and vascular causes. The work-up of EPC includes electroencephalography (EEG), magnetic resonance imaging (MRI) of the brain, position emission tomography (PET) scan of the brain, autoimmune antibodies, infection work-up, and metabolic and genetic work-up. The management of EPC can be challenging. Antiseizure medications (ASDs) including benzodiazepines are an integral part of the management of EPC. Immunotherapy trials are recommended in resistant cases. Epilepsy surgery is one of the effective modalities in some surgically amenable cases. This article reviews the topic of EPC and summarizes diagnostic and .treatment recommendations.

DEPDC5 plays a vital role in epilepsy: Genotypic and phenotypic features in cohort and literature.

OBJECTIVE: DEPDC5 emerges to play a vital role in focal epilepsy. However, genotype-phenotype correlation in DEPDC5-related focal epilepsies is challenging and controversial. In this study, we aim to investigate the genotypic and phenotypic features in DEPDC5-affected patients. METHODS: Genetic testing combined with criteria published by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP), was used to identify pathogenic/likely pathogenic variants in DEPDC5 among the cohort of 479 patients with focal epilepsy. Besides, the literature review was performed to explore the genotype-phenotype correlation and the penetrance in DEPDC5-related focal epilepsies. RESULTS: Eight unrelated probands were revealed to carry different pathogenic/likely pathogenic variants in DEPDC5 and the total prevalence of DEPDC5-related focal epilepsy was 1.67% in the cohort. Sixty-five variants from 28 studies were included in our review. Combined with the cases reported, null variants accounted for a larger proportion than missense variants and were related to unfavorable prognosis (drug resistance or even sudden unexpected death in epilepsy; χ2 = 5.429, p = .020). And, the prognosis of probands with developmental delay/intellectual disability or focal cortical dysplasia was worse than that of probands with simple epilepsy (χ2 = -, p = .006). Besides, the overall penetrance of variants in DEPDC5 was 68.96% (231/335). SIGNIFICANCE: The study expands the variant spectrum of DEPDC5 and proves that the DEPDC5 variant plays a significant role in focal epilepsy. Due to the characteristics of phenotypic heterogeneity and incomplete penetrance, genetic testing is necessary despite no specific family history. And we propose to adopt the ACMG/AMP criteria refined by ClinGen Sequence Variant Interpretation Working Group, for consistency in usage and transparency in classification rationale. Moreover, we reveal an important message to clinicians that the prognosis of DEPDC5-affected patients is related to the variant type and complications.

Unrecognized Focal Nonmotor Seizures in Adolescents Presenting to Emergency Departments.

BACKGROUND AND OBJECTIVES: Many adolescents with undiagnosed focal epilepsy seek evaluation in emergency departments (EDs). Accurate history-taking is essential to prompt diagnosis and treatment. In this study, we investigated ED recognition of motor vs nonmotor seizures and its effect on management and treatment of focal epilepsy in adolescents. METHODS: This was a retrospective analysis of enrollment data from the Human Epilepsy Project (HEP), an international multi-institutional study that collected data from 34 sites between 2012 and 2017. Participants were 12 years or older, neurotypical, and within 4 months of treatment initiation for focal epilepsy. We used HEP enrollment medical records to review participants' initial diagnosis and management. RESULTS: A total of 83 adolescents were enrolled between 12 and 18 years. Fifty-eight (70%) presented to an ED before diagnosis of epilepsy. Although most ED presentations were for motor seizures (n = 52; 90%), many patients had a history of nonmotor seizures (20/52 or 38%). Adolescents with initial nonmotor seizures were less likely to present to EDs (26/44 or 59% vs 32/39 or 82%, p = 0.02), and nonmotor seizures were less likely to be correctly identified (2/6 or 33% vs 42/52 or 81%, p = 0.008). A history of initial nonmotor seizures was not recognized in any adolescent who presented for a first-lifetime motor seizure. As a result, initiation of treatment and admission from the ED was not more likely for these adolescents who met the definition of epilepsy compared with those with no seizure history. This lack of nonmotor seizure history recognition in the ED was greater than that observed in the adult group (0% vs 23%, p = 0.03) and occurred in both pediatric and nonpediatric ED settings. DISCUSSION: Our study supports growing evidence that nonmotor seizures are often undiagnosed, with many individuals coming to attention only after conversion to motor seizures. We found this treatment gap is exacerbated in the adolescent population. Our study highlights a critical need for physicians to inquire about the symptoms of nonmotor seizures, even when the presenting seizure is motor. Future interventions should focus on improving nonmotor seizure recognition for this population in EDs.

New stimulation procedures for language mapping in stereo-EEG.

OBJECTIVE: Cortical intracerebral electrical stimulation is an important tool for language mapping in the presurgical work-up of patients with drug-resistant focal epilepsy. Language mapping with stereo-electroencephalography (EEG) is usually performed by high-frequency stimulations (HFS: 50 Hz), whereas low-frequency stimulations (LFS: 1 Hz) are usually considered useful for primary cortices mapping. Little is known in literature about "intermediate" frequencies (IFS: 6-15 Hz). Our objective is to explore the clinical usefulness of IFS in language mapping and identify factors, beyond the electrical parameters, that impact the mapping. METHODS: We studied 23 patients submitted to stereo-EEG for presurgical evaluation. Language mapping was performed in the anterior, posterior and/or basal language region of the dominant hemisphere for language. We included all contact positions within these regions stimulated by HFS (50 Hz, 5 s, 1-3 mA) and IFS (6-15 Hz, 15 s, 5 mA). We compared the capability of both stimulation methods to induce a language deficit without afterdischarges (ADs), and we analyzed factors related to clinical examination, region, and stimulation technique by multivariate analysis. RESULTS: A total of 211 stimulations (98 HFS, 113 IFS) in 70 cortical sites within the anterior (84 stimulations), posterior (137), and basal language region (60) were included. IFS induced more frequently language deficits not associated to AD compared to HFS (37.1% vs 25.7%, p = .0043), whereas HFS provoked more diffuse AD (34.7% vs 15.0%, p = .001). Investigating multiple language functions increased the probability of revealing a deficit (odds ratio [OR] 3.16, p = .0016), independently of the stimulation method. SIGNIFICANCE: IFS are valuable for language mapping, thereby improving the probability of inducing a clinical deficit not accompanied by an AD. The completeness of the clinical examination independently affects the sensitivity of the mapping. IFS are a new tool with potential usefulness for the cortical mapping of other associative cortical regions.

Ictal fast activity chirps as markers of the epileptogenic zone.

The identification of the epileptogenic zone (EZ) boundaries is crucial for effective focal epilepsy surgery. We verify the value of a neurophysiological biomarker of focal ictogenesis, characterized by a low-voltage fast-activity ictal pattern (chirp) recorded with intracerebral electrodes during invasive presurgical monitoring (stereoelectroencephalography [SEEG]). The frequency content of SEEG signals was retrospectively analyzed with semiautomatic software in 176 consecutive patients with focal epilepsies that either were cryptogenic or presented with discordant anatomoelectroclinical findings. Fast activity seizure patterns with the spectrographic features of chirps were confirmed by computer-assisted analysis in 95.4% of patients who presented with heterogeneous etiologies and diverse lobar location of the EZ. Statistical analysis demonstrated (1) correlation between seizure outcome and concordance of sublobar regions included in the EZ defined by visual analysis and chirp-generating regions, (2) high concordance in contact-by contact analysis of 68 patients with Engel class Ia outcome, and (3) that discordance between chirp location and the visually outlined EZ correlated with worse seizure outcome. Seizure outcome analysis confirms the fast activity chirp pattern is a reproducible biomarker of the EZ in a heterogeneous group of patients undergoing SEEG.

Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy.

PURPOSE: Some individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data. METHODS: The study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms. RESULTS: Among the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features. CONCLUSIONS: Considering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.

Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods. METHODS: We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa. RESULTS: The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use. CONCLUSION: A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

The usefulness of NDDI-E and QOLIE-10 scales for the screening of major depressive disorders in patients with epilepsy in video-EEG units.

OBJECTIVE: Mood disorders in patients with epilepsy are common, with depression being the most prevalent. However, this comorbidity is often underdiagnosed. The systematic use of scales such as NDDI-E and QOLIE-10 in prolonged video-EEG monitoring units could be a useful tool for the detection of this comorbidity. METHODS: Descriptive cross-sectional study of a series of patients with epilepsy evaluated in a prolonged video-EEG monitoring unit. RESULTS: Three hundred forty-nine patients were included. The mean age was 49.1 years, and 49.3% were female. 66.2% had focal epilepsy. 20.4% had pharmacoresistant epilepsy. 38.7% of patients had NDDI-E > 13. 43% of patients with focal epilepsy had NDDI-E > 13 versus 21.8% of patients with idiopathic generalized epilepsy (p = .015). Patients with focal temporal epilepsy had the highest rate of NDDI-E > 13 (48.5%). Significant association was found between patient-perceived mood and NDDI-E score (p < .001). However, in the group of patients with NDDI-E > 13, 37.6% had reported feeling "very good" or "good" in mood. Likewise, in the group that had reported feeling "very good" or "good" 21.6% had NDDI-E > 13. In 50.5% of patients with NDDI-E > 13 some kind of therapeutic intervention aimed at this comorbidity was performed. Perceived quality of life as measured by the QOLIE-10 scale was lower in patients with NDDI-E > 13 (p < .001). SIGNIFICANCE: The use of scales such as the NDDI-E and QOLIE-10 at the time of admission in video-EEG monitoring units allows screening for major depressive disorders in patients with epilepsy, which subsequently needs to be confirmed by formal assessment by a psychiatrist. Their systematic use in these units prevents some patients from going undiagnosed. Detection of these disorders allows targeted therapeutic intervention.

Why did my seizures start now? Influences of lesion connectivity and genetic etiology on age at seizure onset in focal epilepsy.

OBJECTIVE: Patients with focal, lesional epilepsy present with seizures at variable ages. Larger lesion size and overlap with sensorimotor or default mode network (DMN) have been associated with younger age at seizure onset in cohorts with mixed types of focal cortical dysplasia (FCD). Here, we studied determinants of age at seizure onset in patients with bottom-of-sulcus dysplasia (BOSD), a discrete type of FCD with highly localized epileptogenicity. METHODS: Eighty-four patients (77% operated) with BOSD were studied. Demographic, histopathologic, and genetic findings were recorded. BOSD volume and anatomical, primary versus association, rostral versus caudal, and functional network locations were determined. Normative functional connectivity analyses were performed using each BOSD as a region of interest in resting-state functional magnetic resonance imaging data of healthy children. Variables were correlated with age at seizure onset. RESULTS: Median age at seizure onset was 5.4 (interquartile range = 2-7.9) years. Of 50 tested patients, 22 had somatic and nine had germline pathogenic mammalian target of rapamycin (mTOR) pathway variants. Younger age at seizure onset was associated with greater BOSD volume (p = .002), presence of a germline pathogenic variant (p = .04), DMN overlap (p = .04), and increased functional connectivity with the DMN (p < .05, false discovery rate corrected). Location within sensorimotor cortex and networks was not associated with younger age at seizure onset in our relatively small but homogenous cohort. SIGNIFICANCE: Greater lesion size, pathogenic mTOR pathway germline variants, and DMN connectivity are associated with younger age at seizure onset in small FCD. Our findings strengthen the suggested role of DMN connectivity in the onset of FCD-related focal epilepsy and reveal novel contributions of genetic etiology.

Modification of brain conductivity in human focal epilepsy: A model-based estimation from stereoelectroencephalography.

OBJECTIVE: We have developed a novel method for estimating brain tissue electrical conductivity using low-intensity pulse stereoelectroencephalography (SEEG) stimulation coupled with biophysical modeling. We evaluated the hypothesis that brain conductivity is correlated with the degree of epileptogenicity in patients with drug-resistant focal epilepsy. METHODS: We used bipolar low-intensity biphasic pulse stimulation (.2 mA) followed by a postprocessing pipeline for estimating brain conductivity. This processing is based on biophysical modeling of the electrical potential induced in brain tissue between the stimulated contacts in response to pulse stimulation. We estimated the degree of epileptogenicity using a semi-automatic method quantifying the dynamic of fast discharge at seizure onset: the epileptogenicity index (EI). We also investigated how the location of stimulation within specific anatomical brain regions or within lesional tissue impacts brain conductivity. RESULTS: We performed 1034 stimulations of 511 bipolar channels in 16 patients. We found that brain conductivity was lower in the epileptogenic zone (EZ; unpaired median difference = .064, p < .001) and inversely correlated with the epileptogenic index value (p < .001, Spearman rho = -.32). Conductivity values were also influenced by anatomical site, location within lesion, and delay between SEEG electrode implantation and stimulation, and had significant interpatient variability. Mixed model multivariate analysis showed that conductivity is significantly associated with EI (F = 13.45, p < .001), anatomical regions (F = 5.586, p < .001), delay since implantation (F = 14.71, p = .003), and age at SEEG (F = 6.591, p = .027), but not with the type of lesion (F = .372, p = .773) or the delay since last seizure (F = 1.592, p = .235). SIGNIFICANCE: We provide a novel model-based method for estimating brain conductivity from SEEG low-intensity pulse stimulations. The brain tissue conductivity is lower in EZ as compared to non-EZ. Conductivity also varies significantly across anatomical brain regions. Involved pathophysiological processes may include changes in the extracellular space (especially volume or tortuosity) in epileptic tissue.