Nascimento, Fábio A

Abstract Emotional experiences during focal seizures are often misinterpreted as psychiatric disorders. We aimed to evaluate symptoms involving affective symptoms at seizure onset. We aimed to identify the most prevalent affective symptoms at seizure onset and characterize their localizing and lateralizing value. We performed a scoping review based on Preferred Reporting Items for Systematic reviews and Meta‐Analyses–extension for Scoping Reviews guidelines. A systematic search identified records from 4 databases: PubMed, Embase, CINAHL (via EBSCOhost), and Web of Science. Two reviewers independently screened titles/abstracts and full texts; discrepancies were resolved by consensus. This review focused on patients with focal seizures, as affective symptoms may occur in generalized epilepsies. The affective symptoms: fear, anxiety, panic, sadness, mystic, ecstatic, sexual, and mirth were identified, and semiology was evaluated in accordance with ILAE guidelines. Symptom onset, frequency, brain localization, and video‐EEG monitoring technique were evaluated to assess localization and lateralization of regional networks. Scalp EEG, invasive EEG recordings, and electrical stimulation were analyzed. A total of 1276 documents were identified, with 98 studies included for review. Fear was the most frequently reported ictal emotion, identified in 274 patients (66%), predominantly in temporal lobe epilepsy. Sadness and sexual, ecstatic symptoms were also consistently associated with temporal networks. Mirth was more frequently reported with frontal onset seizures. Evidence, primarily from Class IV studies, reported right hemispheric predominance for ecstasy, mystic, and sexual symptoms at seizure onset. Ictal affective symptoms at seizure onset yield localizing information. The limbic system, especially the temporal lobe, followed by the frontal lobe, was the most common site of origin. Fear was the most common spontaneous and electrically elicited symptom, followed by anxiety. While bi‐hemispheric lateralization occurred, right lateralization predominated. Recognizing paroxysmal affective symptoms as ictal phenomena may reduce misdiagnosis and aid presurgical evaluation in drug‐resistant focal epilepsy.

Abstract Objective To measure the relative levels of signal and noise in expert diagnosis of epilepsy. Methods Twenty multinational epileptologists independently reviewed 50 vignettes of adult and pediatric patients presenting with suspected seizure(s) on two separate occasions with a ≥30‐day washout period. Experts provided a diagnosis of epilepsy or non‐epilepsy based on clinical information and, if requested, routine EEG and neuroimaging data. Cases had an established clinical diagnosis of epilepsy or non‐epilepsy based on capture of habitual paroxysmal events on video‐EEG or long‐term clinical follow‐up. Experts' judgments were analyzed to decompose variability into different sources: signal (objective differences between cases), level noise (experts' bias toward over/under‐diagnosis), pattern noise (experts' idiosyncratic reactions to specific case features), and occasion noise (inconsistency across occasions). Results The probability of an expert making a different diagnosis for a given case on two different occasions was 16%. The probability of two different experts making a different diagnosis for the same case was 26%. Signal (case “difficulty”) accounted for 66–69% of total variation, with 31–34% attributable to noise. Level noise was the largest contributor in the absence of EEG/neuroimaging results (23%), while pattern noise dominated when test results were available (24%). Occasion noise contributed relatively little (1%) but was still sufficient to cause diagnostic reversals in 16–22% between occasions. Significance The degree of noise in expert diagnosis of epilepsy is substantial, stemming primarily from physicians' idiosyncratic interpretations of case features and variable dispositions toward over‐ or under‐diagnosis. Strategies to improve reliability are needed, including standardized data collection protocols and structured decision algorithms. For “difficult cases,” where expert reliability and accuracy are lowest, our findings support current clinical practice which favors early referral for video‐EEG monitoring over reliance on diagnostic anchoring. This diagnostic pathway may become more accessible with advances in EEG technology (e.g., wearable devices) and artificial intelligence.

AbstractObjectiveWe published a list of “must‐know” routine EEG (rEEG) findings for trainees based on expert opinion. Here, we studied the accuracy and inter‐rater agreement (IRA) of these “must‐know” rEEG findings among international experts.MethodsA previously validated online rEEG examination was disseminated to EEG experts. It consisted of a survey and 30 multiple‐choice questions predicated on the previously published “must‐know” rEEG findings divided into four domains: normal, abnormal, normal variants, and artifacts. Questions contained de‐identified 10–20‐s epochs of EEG that were considered unequivocal examples by five EEG experts.ResultsThe examination was completed by 258 international EEG experts. Overall mean accuracy and IRA (AC1) were 81% and substantial (0.632), respectively. The domain‐specific mean accuracies and IRA were: 76%, moderate (0.558) (normal); 78%, moderate (0.575) (abnormal); 85%, substantial (0.678) (normal variants); 85%, substantial (0.740) (artifacts). Academic experts had a higher accuracy than private practice experts (82% vs. 77%; p = .035). Country‐specific overall mean accuracies and IRA were: 92%, almost perfect (0.836) (U.S.); 86%, substantial (0.762) (Brazil); 79%, substantial (0.646) (Italy); and 72%, moderate (0.496) (India). In conclusion, collective expert accuracy and IRA of “must‐know” rEEG findings are suboptimal and heterogeneous.SignificanceWe recommend the development and implementation of pragmatic, accessible, country‐specific ways to measure and improve the expert accuracy and IRA.

AbstractCorrectly diagnosing and classifying seizures and epilepsies is paramount to ensure the delivery of optimal care to patients with epilepsy. Focal seizures, defined as those that originate within networks limited to one hemisphere, are primarily subdivided into focal aware, focal impaired awareness, and focal to bilateral tonic–clonic seizures. Focal epilepsies account for most epilepsy cases both in children and adults. In children, focal epilepsies are typically subdivided in three groups: self‐limited focal epilepsy syndromes (e.g., self‐limited epilepsy with centrotemporal spikes), focal epilepsy of unknown cause but which do not meet criteria for a self‐limited focal epilepsy syndrome, and focal epilepsy of known cause (e.g., structural lesions—developmental or acquired). In adults, focal epilepsies are often acquired and may be caused by a structural lesion such as stroke, infection and traumatic brain injury, or brain tumors, vascular malformations, metabolic disorders, autoimmune, and/or genetic causes. In addition to seizure semiology, neuroimaging, neurophysiology, and neuropathology constitute the cornerstones of a diagnostic evaluation. Patients with focal epilepsy who become drug‐resistant should promptly undergo assessment in an epilepsy center. After excluding pseudo‐resistance, these patients should be considered for presurgical evaluation as a means to identify the location and extent of the epileptogenic zone and assess their candidacy for a surgical procedure. The goal of this seminar in epileptology is to summarize clinically relevant information concerning focal epilepsies. This contributes to the ILAE's mission to ensure that worldwide healthcare professionals, patients, and caregivers continue to have access to high‐quality educational resources concerning epilepsy.