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Objective: To investigate spatial correlation between interictal HFOs and neuroimaging abnormalities, and to determine if complete removal of prospectively identified interictal HFOs correlates with post-surgical seizure-freedom., Methods: Interictal fast ripples (FRs: 250-500 Hz) in 19 consecutive children with pharmacoresistant focal epilepsy who underwent extra-operative electrocorticography (ECoG) recording were prospectively analyzed. The interictal FRs were sampled at 2000 Hz and were visually identified during 10 min of slow wave sleep. Interictal FRs, MRI and FDG-PET were delineated on patient-specific reconstructed three-dimensional brain MRI., Results: Interictal FRs were observed in all patients except one. Thirteen out of 18 patients (72%) exhibited FRs beyond the extent of neuroimaging abnormalities. Fifteen of 19 children underwent resective surgery, and survival analysis with log-rank test demonstrated that complete resection of cortical sites showing interictal FRs correlated with longer post-operative seizure-freedom (p < 0.01). Complete resection of seizure onset zones (SOZ) also correlated with longer post-operative seizure-freedom (p = 0.01), yet complete resection of neuroimaging abnormalities did not (p = 0.43)., Conclusions: Prospective visual analysis of interictal FRs was feasible, and it seemed to accurately localize epileptogenic zones., Significance: Topological extent of epileptogenic region may exceed what is discernible by multimodal neuroimaging., (Copyright © 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Our objective was to define the EEG features during sleep of children with neurodevelopmental disorders due to copy number gains of 15q11-q13 (Dup15q). We retrospectively reviewed continuous EEG recordings of 42 children with Dup15q (mean age: eight years, 32 with idic15), and data collected included background activity, interictal epileptiform discharges, sleep organization, and ictal activity. Three patterns were recognized: Pattern 1: Alpha–delta sleep was noted in 14 children (33%), not associated with any clinical changes. Pattern 2: Electrical status epilepticus in sleep was noted in 15 children (35%), all diagnosed with treatmentresistant epilepsy. Thirteen of the 15 children had clinical seizures. Pattern 3: Frequent bursts of high amplitude bifrontal predominant, paroxysmal fast activity (12–15 Hz) during non-REM sleep was noted in 15 children (35%). All 15 children had treatment-resistant epilepsy. This is the first report of electroencephalographic patterns during sleep of children with Dup15q reporting alpha-delta rhythms, CSWS, and high amplitude fast frequencies. Alpha-delta rhythms are described in children with dysautonomia and/or mood disorders and CSWS in children with developmental regression. The significance of these findings in cognitive function and epilepsy for the children in our cohort needs to be determined with follow-up studies., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: Electrographic seizures are common in encephalopathic critically ill children, but identification requires continuous EEG monitoring (CEEG). Development of a seizure prediction model would enable more efficient use of limited CEEG resources. We aimed to develop and validate a seizure prediction model for use among encephalopathic critically ill children., Method: We developed a seizure prediction model using a retrospectively acquired multi-center database of children with acute encephalopathy without an epilepsy diagnosis, who underwent clinically indicated CEEG. We performed model validation using a separate prospectively acquired single center database. Predictor variables were chosen to be readily available to clinicians prior to the onset of CEEG and included: age, etiology category, clinical seizures prior to CEEG, initial EEG background category, and inter-ictal discharge category., Results: The model has fair to good discrimination ability and overall performance. At the optimal cut-off point in the validation dataset, the model has a sensitivity of 59% and a specificity of 81%. Varied cut-off points could be chosen to optimize sensitivity or specificity depending on available CEEG resources., Conclusion: Despite inherent variability between centers, a model developed using multi-center CEEG data and few readily available variables could guide the use of limited CEEG resources when applied at a single center. Depending on CEEG resources, centers could choose lower cut-off points to maximize identification of all patients with seizures (but with more patients monitored) or higher cut-off points to reduce resource utilization by reducing monitoring of lower risk patients (but with failure to identify some patients with seizures)., (Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Objective: To describe the prevalence, characteristics, and predictors of electrographic seizures after convulsive status epilepticus (CSE)., Study Design: This was a multicenter retrospective study in which we describe clinical and electroencephalographic (EEG) features of children (1 month to 21 years) with CSE who underwent continuous EEG monitoring., Results: Ninety-eight children (53 males) with CSE (median age of 5 years) underwent subsequent continuous EEG monitoring after CSE. Electrographic seizures (with or without clinical correlate) were identified in 32 subjects (33%). Eleven subjects (34.4%) had electrographic-only seizures, 17 subjects (53.1%) had electroclinical seizures, and 4 subjects (12.5%) had an unknown clinical correlate. Of the 32 subjects with electrographic seizures, 15 subjects (46.9%) had electrographic status epilepticus. Factors associated with the occurrence of electrographic seizures after CSE were a previous diagnosis of epilepsy (P = .029) and the presence of interictal epileptiform discharges (P < .0005). The median (p25-p75) duration of stay in the pediatric intensive care unit was longer for children with electrographic seizures than for children without electrographic seizures (9.5 [3-22.5] vs 2 [2-5] days, Wilcoxon test, Z = 3.916, P = .0001). Four children (4.1%) died before leaving the hospital, and we could not identify a relationship between death and the presence or absence of electrographic seizures., Conclusions: After CSE, one-third of children who underwent EEG monitoring experienced electrographic seizures, and among these, one-third experienced entirely electrographic-only seizures. A previous diagnosis of epilepsy and the presence of interictal epileptiform discharges were risk factors for electrographic seizures., (Copyright © 2014 Mosby, Inc. All rights reserved.)

PurposeTraumatic brain injury (TBI) is an important cause of morbidity and mortality in children, and early posttraumatic seizures (EPTS) are a contributing factor to ongoing acute damage. Continuous video-EEG monitoring (cEEG) was utilized to assess the burden of clinical and electrographic EPTS.MethodsEighty-seven consecutive, unselected (mild - severe), acute TBI patients requiring pediatric intensive care unit (PICU) admission at two academic centers were monitored prospectively with cEEG per established clinical TBI protocols. Clinical and subclinical seizures and status epilepticus (SE, clinical and subclinical) were assessed for their relation to clinical risk factors and short-term outcome measures.Key findingsOf all patients, 42.5% (37/87) had seizures. Younger age (p = 0.002) and injury mechanism (abusive head trauma - AHT, p < 0.001) were significant risk factors. Subclinical seizures occurred in 16.1% (14/87), while 6.9% (6/87) had only subclinical seizures. Risk factors for subclinical seizures included younger age (p < 0.001), AHT (p < 0.001), and intraaxial bleed (p < 0.001). SE occurred in 18.4% (16/87) with risk factors including younger age (p < 0.001), AHT (p < 0.001), and intraaxial bleed (p = 0.002). Subclinical SE was detected in 13.8% (12/87) with significant risk factors including younger age (p < 0.001), AHT (p = 0.001), and intraaxial bleed (p = 0.004). Subclinical seizures were associated with lower discharge King's Outcome Scale for Childhood Head Injury (KOSCHI) score (p = 0.002). SE and subclinical SE were associated with increased hospital length of stay (p = 0.017 and p = 0.041, respectively) and lower hospital discharge KOSCHI (p = 0.007 and p = 0.040, respectively).SignificancecEEG monitoring significantly improves detection of seizures/SE and is the only way to detect subclinical seizures/SE. cEEG may be indicated after pediatric TBI, particularly in younger children, AHT cases, and those with intraaxial blood on computerized tomography (CT).