Evaluation Of Epilepsy Research Articles

Novel GATOR1 variants in focal epilepsy

IntroductionVariants in GATOR1 genes are well established in focal epilepsy syndromes. A strong association of GATOR1 variants with drug-resistant epilepsy as well as an increased risk of sudden unexplained death in epilepsy warrants developing strategies to facilitate the identification of patients who could potentially benefit from genetic testing and precision medicine. We aimed to determine the yield of GATOR1 gene sequencing in patients with focal epilepsy typically referred for genetic testing, establish novel GATOR1 variants and determine clinical, electroencephalographic, and radiological characteristics of variant carriers. Patients and MethodsNinety-six patients with clinical suspicion of genetic focal epilepsy with previous comprehensive diagnostic epilepsy evaluation in The Neurology Clinic, University Clinical Center of Serbia, were included in the study. Sequencing was performed using a custom gene panel encompassing DEPDC5, NPRL2, and NPRL3. Variants of interest (VOI) were classified according to criteria proposed by the American College of Medical Genetics and the Association for Molecular Pathology. ResultsFour previously unreported VOI in 4/96 (4.2%) patients were found in our cohort. Three likely pathogenic variants were determined in 3/96 (3.1%) patients, one frameshift variant in DEPDC5 in a patient with nonlesional frontal lobe epilepsy, one splicogenic DEPDC5 variant in a patient with nonlesional posterior quadrant epilepsy, and one frameshift variant in NPRL2 in a patient with temporal lobe epilepsy associated with hippocampal sclerosis. Only one VOI, a missense variant in NPRL3, found in 1/96 (1.1%) patients, was classified as a variant of unknown significance. ConclusionGATOR1 gene sequencing was diagnostic in 3.1% of our cohort and revealed three novel likely pathogenic variants, including a previously unreported association of temporal lobe epilepsy with hippocampal sclerosis with an NPRL2 variant. Further research is essential for a better understanding of the clinical scope of GATOR1 gene-associated epilepsy.

The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources.

To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization. Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality. Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01). This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.

Quantifying trial-by-trial variability during cortico-cortical evoked potential mapping of epileptogenic tissue.

Measuring cortico-cortical evoked potentials (CCEPs) is a promising tool for mapping epileptic networks, but it is not known how variability in brain state and stimulation technique might impact the use of CCEPs for epilepsy localization. We test the hypotheses that (1) CCEPs demonstrate systematic variability across trials and (2) CCEP amplitudes depend on the timing of stimulation with respect to endogenous, low-frequency oscillations. We studied 11 patients who underwent CCEP mapping after stereo-electroencephalography electrode implantation for surgical evaluation of drug-resistant epilepsy. Evoked potentials were measured from all electrodes after each pulse of a 30 s, 1Hz bipolar stimulation train. We quantified monotonic trends, phase dependence, and standard deviation (SD) of N1 (15-50 ms post-stimulation) and N2 (50-300 ms post-stimulation) amplitudes across the 30 stimulation trials for each patient. We used linear regression to quantify the relationship between measures of CCEP variability and the clinical seizure-onset zone (SOZ) or interictal spike rates. We found that N1 and N2 waveforms exhibited both positive and negative monotonic trends in amplitude across trials. SOZ electrodes and electrodes with high interictal spike rates had lower N1 and N2 amplitudes with higher SD across trials. Monotonic trends of N1 and N2 amplitude were more positive when stimulating from an area with higher interictal spike rate. We also found intermittent synchronization of trial-level N1 amplitude with low-frequency phase in the hippocampus, which did not localize the SOZ. These findings suggest that standard approaches for CCEP mapping, which involve computing a trial-averaged response over a .2-1Hz stimulation train, may be masking inter-trial variability that localizes to epileptogenic tissue. We also found that CCEP N1 amplitudes synchronize with ongoing low-frequency oscillations in the hippocampus. Further targeted experiments are needed to determine whether phase-locked stimulation could have a role in localizing epileptogenic tissue.

Disparities in pediatric drug-resistant epilepsy care

IntroductionEpilepsy affects millions of children worldwide, with 20–40% experiencing drug-resistant epilepsy (DRE) who are recommended for epilepsy surgery evaluation and may benefit from surgical management. However, many patients live with DRE for multiple years prior to surgical epilepsy referral or treatment or are never referred at all.ObjectiveWe aimed to describe factors associated with referral for epilepsy surgery in the USA, in order to identify disparities in DRE, characterize why they may exist, and recognize areas for improvement.MethodsPediatric patients diagnosed with DRE between January 1, 2004 and December 31, 2020 were identified from the Pediatric Health Information System (PHIS) Database. Patients treated with antiseizure medications (ASMs) only, ASMs plus vagus nerve stimulation (VNS), and ASMs plus cranial epilepsy surgery were studied regarding access to epilepsy surgery and disparities in care. This study used chi-square tests to determine associations between treatment time and preoperative factors. Preoperative factors studied included epilepsy treatment type, age, sex, race/ethnicity, insurance type, geographic region, patient type, epilepsy type, and presence of pediatric complex chronic conditions (PCCCs).ResultsA total of 18,292 patients were identified; 10,240 treated with ASMs, 5019 treated with ASMs + VNS, and 3033 treated with ASMs + cranial epilepsy surgery. Sex was not found to significantly vary among groups. There was significant variation in age, census region, race/ethnicity, patient type, presence of PCCCs, diagnosis, and insurance (p < 0.001). Those treated surgically, either with VNS or cranial epilepsy surgery, were 2 years older than those medically treated. Additionally, those medically treated were less likely to be living in the Midwest (25.46%), identified as non-Hispanic white (51.78%), have a focal/partial epilepsy diagnosis (8.74%), and be privately insured (35.82%).ConclusionsWe studied a large administrative US database examining variables associated with surgical epilepsy evaluation and management. We found significant variation in treatment associated with age, US census region, race/ethnicity, patient type, presence of PCCCs, diagnosis, and health insurance type. We believe that these disparities in care are related to access and social determinants of health, and we encourage focused outreach strategies to mitigate these disparities to broaden access and improve outcomes in children in the USA with DRE.

The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence.

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.

Basic Principles of Electroencephalogram

Electroencephalogram(EEG)signals are mainly generated by postsynaptic potentials(EPSP)in the apical dendrites of pyramidal cells in the cortex. The mechanism of generation of interictal epileptiform discharges(IED)is paroxysmal depolarization shift(PDS). IEDs are negative towards the cortical surface and positive towards the white matter. To interpret EEG, readers should know normal EEG waveforms of wakefulness and sleep, normal variants such as wicket spikes or small sharp spikes, and artifacts such as eye movements or electromyography. Overinterpretation of EEG is a major cause of misdiagnosis; therefore, the accurate identification of IED is essential. The International Federation of Clinical Neurophysiology has proposed a criteria for the identification of IED. Long-term video-EEG is useful for correct diagnosis and localization of the epileptogenic zone in the setting of presurgical evaluation of intractable epilepsy and is also useful for the differential diagnosis of epileptic seizures and non-epileptic events. Clinical history and symptomatology are of utmost importance for correct diagnosis. Diagnosis should not be made using only EEG findings.

Chapter 15 - Motor seizure semiology

Motor semiology is a major component of epilepsy evaluation, which provides essential information on seizure classification and helps in seizure localization. The typical motor seizures include tonic, clonic, tonic-clonic, myoclonic, atonic, epileptic spasms, automatisms, and hyperkinetic seizures. Compared to the "positive" motor signs, negative motor phenomena, for example, atonic seizures and Todd's paralysis are also crucial in seizure analysis. Several motor signs, for example, version, unilateral dystonia, figure 4 sign, M2e sign, and asymmetric clonic ending, are commonly observed and have significant clinical value in seizure localization. The purpose of this chapter is to review the localization value and pathophysiology associated with the well-defined motor seizure semiology using updated knowledge from intracranial electroencephalographic recordings, particularly stereoelectroencephalography.

Validating EEG source imaging using intracranial electrical stimulation.

Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neurosciences to localize neuronal sources of brain potentials recorded on EEG. This study evaluates the spatial accuracy of electrical source imaging for known sources, using electrical stimulation potentials recorded on simultaneous stereo-EEG and 37-electrode scalp EEG, and identifies factors determining the localization error. In 11 patients undergoing simultaneous stereo-EEG and 37-electrode scalp EEG recordings, sequential series of 99-110 biphasic pulses (2 ms pulse width) were applied by bipolar electrical stimulation on adjacent contacts of implanted stereo-EEG electrodes. The scalp EEG correlates of stimulation potentials were recorded with a sampling rate of 30 kHz. Electrical source imaging of averaged stimulation potentials was calculated utilizing a dipole source model of peak stimulation potentials based on individual four-compartment finite element method head models with various skull conductivities (range from 0.0413 to 0.001 S/m). Fitted dipoles with a goodness of fit of ≥80% were included in the analysis. The localization error was calculated using the Euclidean distance between the estimated dipoles and the centre point of adjacent stimulating contacts. A total of 3619 stimulation locations, respectively, dipole localizations, were included in the evaluation. Mean localization errors ranged from 10.3 to 26 mm, depending on source depth and selected skull conductivity. The mean localization error increased with an increase in source depth (r(3617) = [0.19], P = 0.000) and decreased with an increase in skull conductivity (r(3617) = [-0.26], P = 0.000). High skull conductivities (0.0413-0.0118 S/m) yielded significantly lower localization errors for all source depths. For superficial sources (<20 mm from the inner skull), all skull conductivities yielded insignificantly different localization errors. However, for deeper sources, in particular >40 mm, high skull conductivities of 0.0413 and 0.0206 S/m yielded significantly lower localization errors. In relation to stimulation locations, the majority of estimated dipoles moved outward-forward-downward to inward-forward-downward with a decrease in source depth and an increase in skull conductivity. Multivariate analysis revealed that an increase in source depth, number of skull holes and white matter volume, while a decrease in skull conductivity independently led to higher localization error. This evaluation of electrical source imaging accuracy using artificial patterns with a high signal-to-noise ratio supports its application in presurgical epilepsy evaluation and cognitive neurosciences. In our artificial potential model, optimizing the selected skull conductivity minimized the localization error. Future studies should examine if this accounts for true neural signals.

Risk Factors of Seizures in Children

Background: As the most prevalent noncontagious neurologic condition, epilepsy is an important cause of mortality and disability in children, and its etiology is an important issue. Objectives: Epilepsy may be induced by different risk factors, some of which may be unclear; therefore, this study aimed to evaluate the risk factors of seizures in children. Methods: Children with seizures admitted to the Pediatric Clinic of Amir-Kabir Hospital were enrolled in this study. First, accurate biography of the parents was taken to rule out other differential diagnoses of seizures. After the diagnosis of seizures, based on the diagnosis of a pediatric neurologist, demographic information, detailed history of seizure, neurologic examination, and process of growth and development were recorded. Then, we take electroencephalogram (EEG), magnetic resonance imaging, and computed tomography scans from study cases. Results: Of the 291 evaluated cases, 54.6% were male. Variables associated with seizure and epilepsy evaluation showed that generalized tonic-clonic seizure was the most prevalent seizure in 134 children (46%). The duration of seizures was lower than 15 minutes in 266 cases; in addition, 191 children (55.5%) had a fever before seizures. Regarding underlying diseases, 268 children (92.2%) had no disease. Cerebral palsy (CP) was the most common underlying disease in 10 cases (3.4%). Conclusions: Infectious diseases (such as gastroenteritis and pneumonia) were observed as the most important possible risk factors in this study.

The Noninvasive Evaluation for Minimally Invasive Pediatric Epilepsy Surgery (MIPES): A Multimodal Exploration of the Localization-Based Hypothesis

AbstractMinimally invasive pediatric epilepsy surgery (MIPES) is a rising technique in the management of focal-onset drug-refractory epilepsy. Minimally invasive surgical techniques are based on small, focal interventions (such as parenchymal ablation or localized neuromodulation) leading to elimination of the seizure onset zone or interruption of the larger epileptic network. Precise localization of the seizure onset zone, demarcation of eloquent cortex, and mapping of the network leading to seizure propagation are required to achieve optimal outcomes. The toolbox for presurgical, noninvasive evaluation of focal epilepsy continues to expand rapidly, with a variety of options based on advanced imaging and electrophysiology. In this article, we will examine several of these diagnostic modalities from the standpoint of MIPES and discuss how each can contribute to the development of a localization-based hypothesis for potential surgical targets.

Mood and Anxiety Disorders and Suicidality in Patients With Newly Diagnosed Focal Epilepsy: An Analysis of a Complex Comorbidity.

Mood, anxiety disorders, and suicidality are more frequent in people with epilepsy than in the general population. Yet, their prevalence and the types of mood and anxiety disorders associated with suicidality at the time of the epilepsy diagnosis are not established. We sought to answer these questions in patients with newly diagnosed focal epilepsy and to assess their association with suicidal ideation and attempts. The data were derived from the Human Epilepsy Project study. A total of 347 consecutive adults aged 18-60 years with newly diagnosed focal epilepsy were enrolled within 4 months of starting treatment. The types of mood and anxiety disorders were identified with the Mini International Neuropsychiatric Interview, whereas suicidal ideation (lifetime, current, active, and passive) and suicidal attempts (lifetime and current) were established with the Columbia Suicidality Severity Rating Scale (CSSRS). Statistical analyses included the t test, χ2 statistics, and logistic regression analyses. A total of 151 (43.5%) patients had a psychiatric diagnosis; 134 (38.6%) met the criteria for a mood and/or anxiety disorder, and 75 (21.6%) reported suicidal ideation with or without attempts. Mood (23.6%) and anxiety (27.4%) disorders had comparable prevalence rates, whereas both disorders occurred together in 43 patients (12.4%). Major depressive disorders (MDDs) had a slightly higher prevalence than bipolar disorders (BPDs) (9.5% vs 6.9%, respectively). Explanatory variables of suicidality included MDD, BPD, panic disorders, and agoraphobia, with BPD and panic disorders being the strongest variables, particularly for active suicidal ideation and suicidal attempts. In patients with newly diagnosed focal epilepsy, the prevalence of mood, anxiety disorders, and suicidality is higher than in the general population and comparable to those of patients with established epilepsy. Their recognition at the time of the initial epilepsy evaluation is of the essence.

Thalamic stereoelectroencephalography in epilepsy surgery: a scoping literature review.

Stereoelectroencephalography (sEEG) is a well-established surgical method for defining the epileptogenic network. Traditionally reserved for identifying discrete cortical regions for resection or ablation, sEEG in current practice is also used for identifying more broadly involved subcortical epileptic network components, driven by the availability of brain-based neuromodulation strategies. In particular, sEEG investigations including thalamic nuclei are becoming more frequent in parallel with the increase in therapeutic strategies involving thalamic targets such as deep brain stimulation (DBS) and responsive neurostimulation (RNS). The objective to this study was to evaluate existing evidence and trends regarding the purpose, techniques, and relevant electrographic findings of thalamic sEEG. MEDLINE and Embase databases were systematically queried for eligible peer-reviewed studies involving sEEG electrode implantation into thalamic nuclei of patients with epilepsy. Available data were abstracted concerning preoperative workup and purpose for implanting the thalamus, thalamic targets and trajectories, and electrophysiological methodology and findings. sEEG investigations have included thalamic targets for both basic and clinical research purposes. Medial pulvinar, dorsomedial, anterior, and centromedian nuclei have been the most frequently studied. Few studies have reported any complications with thalamic sEEG implantation, and no studies have reported long-term complications. Various methods have been utilized to characterize thalamic activity in epileptic disorders including evoked potentials, power spectrograms, synchronization indices, and the epileptogenicity index. Thalamic intracranial recordings are beginning to be used to guide neuromodulation strategies including RNS and DBS, as well as to understand complex, network-dependent seizure disorders. Inclusion of thalamic coverage during sEEG evaluation in drug-resistant epilepsy is a growing practice and is amenable to various methods of electrographic data analysis. Further study is required to establish well-defined criteria for thalamic implantation during invasive investigations as well as safety and ethical considerations.

Recurrent Hippocampo-neocortical sleep-state divergence in humans

Sleep is assumed to be a unitary, global state in humans and most other animals that is coordinated by executive centers in the brain stem, hypothalamus, and basal forebrain. However, the common observation of unihemispheric sleep in birds and marine mammals, as well as the recently discovered nonpathological regional sleep in rodents, calls into question whether the whole human brain might also typically exhibit different states between brain areas at the same time. We analyzed sleep states independently from simultaneously recorded hippocampal depth electrodes and cortical scalp electrodes in eight human subjects who were implanted with depth electrodes for pharmacologically intractable epilepsy evaluation. We found that the neocortex and hippocampus could be in nonsimultaneous states, on average, one-third of the night and that the hippocampus often led in asynchronous state transitions. Nonsimultaneous bout lengths varied from 30 s to over 30 min. These results call into question the conclusions of studies, across phylogeny, that measure only surface cortical state but seek to assess the functions and drivers of sleep states throughout the brain.

Seizure count forecasting to aid diagnostic testing in epilepsy.

Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug-resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure-related morbidity. Here, we evaluated the performance of a state-space method that uses prior seizure count data to predict future counts. A Bayesian negative-binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1-7 days ahead. One-day-ahead prediction of the number of electrographic seizures using a negative-binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained >50% for forecast horizons of up to 7 days. Superior performance (mean error=.99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer-valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day. This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions.

Mapping hippocampal glutamate in mesial temporal lobe epilepsy with glutamate weighted CEST (GluCEST) imaging.

Temporal lobe epilepsy (TLE) is one of the most common subtypes of focal epilepsy, with mesial temporal sclerosis (MTS) being a common radiological and histopathological finding. Accurate identification of MTS during presurgical evaluation confers an increased chance of good surgical outcome. Here we propose the use of glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) at 7 Tesla for mapping hippocampal glutamate distribution in epilepsy, allowing to differentiate lesional from non-lesional mesial TLE. We demonstrate that a directional asymmetry index, which quantifies the relative difference between GluCEST contrast in hippocampi ipsilateral and contralateral to the seizure onset zone, can differentiate between sclerotic and non-sclerotic hippocampi, even in instances where traditional presurgical MRI assessments did not provide evidence of sclerosis. Overall, our results suggest that hippocampal glutamate mapping through GluCEST imaging is a valuable addition to the presurgical epilepsy evaluation toolbox.

A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG.

Cognitive tasks are commonly used to identify brain networks involved in the underlying cognitive process. However, inferring the brain networks from intracranial EEG data presents several challenges related to the sparse spatial sampling of the brain and the high variability of the EEG trace due to concurrent brain processes. In this manuscript, we use a well-known facial emotion recognition task to compare three different ways of analyzing the contrasts between task conditions: permutation cluster tests, machine learning (ML) classifiers, and a searchlight implementation of multivariate pattern analysis (MVPA) for intracranial sparse data recorded from 13 patients undergoing presurgical evaluation for drug-resistant epilepsy. Using all three methods, we aim at highlighting the brain structures with significant contrast between conditions. In the absence of ground truth, we use the scientific literature to validate our results. The comparison of the three methods’ results shows moderate agreement, measured by the Jaccard coefficient, between the permutation cluster tests and the machine learning [0.33 and 0.52 for the left (LH) and right (RH) hemispheres], and 0.44 and 0.37 for the LH and RH between the permutation cluster tests and MVPA. The agreement between ML and MVPA is higher: 0.65 for the LH and 0.62 for the RH. To put these results in context, we performed a brief review of the literature and we discuss how each brain structure’s involvement in the facial emotion recognition task.

Electric Source Imaging in Presurgical Evaluation of Epilepsy: An Inter-Analyser Agreement Study.

Electric source imaging (ESI) estimates the cortical generator of the electroencephalography (EEG) signals recorded with scalp electrodes. ESI has gained increasing interest for the presurgical evaluation of patients with drug-resistant focal epilepsy. In spite of a standardised analysis pipeline, several aspects tailored to the individual patient involve subjective decisions of the expert performing the analysis, such as the selection of the analysed signals (interictal epileptiform discharges and seizures, identification of the onset epoch and time-point of the analysis). Our goal was to investigate the inter-analyser agreement of ESI in presurgical evaluations of epilepsy, using the same software and analysis pipeline. Six experts, of whom five had no previous experience in ESI, independently performed interictal and ictal ESI of 25 consecutive patients (17 temporal, 8 extratemporal) who underwent presurgical evaluation. The overall agreement among experts for the ESI methods was substantial (AC1 = 0.65; 95% CI: 0.59–0.71), and there was no significant difference between the methods. Our results suggest that using a standardised analysis pipeline, newly trained experts reach similar ESI solutions, calling for more standardisation in this emerging clinical application in neuroimaging.

Connectivity Alterations in Emotional and Cognitive Networks During a Manic State Induced by Direct Electrical Stimulation.

Mania is characterized by affective and cognitive alterations, with heightened external and self-awareness that are opposite to the alteration of awareness during epileptic seizures. Electrical stimulations carried out routinely during stereotactic intracerebral EEG (SEEG) recordings for presurgical evaluation of epilepsy may represent a unique opportunity to study the pathophysiology of such complex emotional-behavioral phenomenon, particularly difficult to reproduce in experimental setting. We investigated SEEG signals-based functional connectivity between different brain regions involved in emotions and in consciousness processing during a manic state induced by electrical stimulation in a patient with drug-resistant focal epilepsy. The stimulation inducing manic state and an asymptomatic stimulation of the same site, as well as a seizure with alteration of awareness (AOA) were analyzed. Functional connectivity analysis was performed by measuring interdependencies (nonlinear regression analysis based on the h2 coefficient) between broadband SEEG signals and within typical sub-bands, before and after stimulation, or before and during the seizure with AOA, respectively. Stimulation of the right lateral prefrontal cortex induced a manic state lasting several hours. Its onset was associated with significant increase of broadband-signal functional coupling between the right hemispheric limbic nodes, the temporal pole and the claustrum, whereas significant decorrelation between the right lateral prefrontal and the anterior cingulate cortex was observed in theta-band. In contrast, ictal alteration of awareness was associated with increased broadband and sub-bands synchronization within and between the internal and external awareness networks, including the anterior and middle cingulate, the mesial and lateral prefrontal, the inferior parietal and the temporopolar cortex. Our data suggest the existence of network- and frequency-specific functional connectivity patterns during manic state. A transient desynchronization of theta activity between the external and internal awareness network hubs is likely to increase awareness, with potential therapeutic effect.

VEP for VIP Care: Patient-Specific Modeling in Epilepsy Surgery.

Virtual Epileptic Patient Brain Modeling: Relationships With Seizure Onset and Surgical Outcome Makhalova J, Medina Villalon S, Wang H, Giusiano B, Woodman M, Bénar C, Guye M, Jirsa V, Bartolomei F. Epilepsia. 2022;63(8):1942-1955. doi:10.1111/epi.17310Objective: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.

Pediatric electroencephalography

Background Routine electroencephalography (EEG) is a widely used test in children for the evaluation of many neurological conditions. It is specifically important in the diagnosis of epilepsy and the differentiation of epilepsy from nonepileptic events. Aim This study aimed to evaluate the current situation of EEG recording at Alexandria University Children’s Hospital. Patients and methods All patients who had a routine EEG recording during a period of 6 months were included in the study. Review of the records was done including personal data, place and indication of referral, and initial diagnosis. EEG data included condition during recording, activation procedures, EEG interpretation regarding background activity, presence or absence of epileptiform activity, type and origin of epileptiform discharges, and the final yield of EEG. Results The study included 570 children, comprising 336 males and 234 females, with a mean age of 5.5±4.1 years. The main indication for referral was epilepsy in 69.8%. Most of the cases (83.3%) had an awake recording. Photic stimulation and hyperventilation were done in 93.3 and 31.9%, respectively, and abnormal response to hyperventilation was observed in 9.9% of them. Ictal recording was done in 22 (3.8%) cases. Abnormal epileptiform discharges were detected in 173 (30.4%) of the studied children; of them, 162 cases were initially referred for established epilepsy and 11 cases for other reasons. The abnormality was generalized in 53.8% and focal in 46.2% of the cases. The commonest types of epileptiform discharges were spikes (65.9%) followed by spike-and-wave complexes (56.1%). Hypsarrhythmia, 3-Hz spike-and-wave complexes, and nonconvulsive status epilepticus were detected in 12.7, 10.4, and 2.9%, respectively. Phase reversal, sharp waves, and polyspikes were detected in 15.6, 5.2, and 2.3%, respectively. Conclusion Routine EEG is a valuable test for evaluation of seizures and epilepsy. Specific abnormal epileptiform discharges are diagnostic for certain epileptic syndromes. EEG finding may be normal in children with epilepsy and should be interpreted in the context of the clinical settings.