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The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network. A major goal of computational neuroscience is to produce models with few parameters which can account for significant aspects of behavioral, neural or physiological data. The authors perform simulation-based inference on EEG spectral features with the Spectral Graph Model, and demonstrate that spectral maturation of the brain activity is an emergent phenomenon guided by age-dependent tuning of localized neuronal dynamics.

BackgroundThe contribution of pathogenic gene variants with development of epilepsy after acute symptomatic neonatal seizures is not known.MethodsCase–control study of 20 trios in children with a history of acute symptomatic neonatal seizures: 10 with and 10 without post-neonatal epilepsy. We performed whole-exome sequencing (WES) and identified pathogenic de novo, transmitted, and non-transmitted variants from established and candidate epilepsy association genes and correlated prevalence of these variants with epilepsy outcomes. We performed a sensitivity analysis with genes associated with coronary artery disease (CAD). We analyzed variants throughout the exome to evaluate for differential enrichment of functional properties using exploratory KEGG searches.ResultsQuerying 200 established and candidate epilepsy genes, pathogenic variants were identified in 5 children with post-neonatal epilepsy yet in only 1 child without subsequent epilepsy. There was no difference in the number of trios with non-transmitted pathogenic variants in epilepsy or CAD genes. An exploratory KEGG analysis demonstrated a relative enrichment in cell death pathways in children without subsequent epilepsy.ConclusionsIn this pilot study, children with epilepsy after acute symptomatic neonatal seizures had a higher prevalence of coding variants with a targeted epilepsy gene sequencing analysis compared to those patients without subsequent epilepsy.ImpactWe performed whole-exome sequencing (WES) in 20 trios, including 10 children with epilepsy and 10 without epilepsy, both after acute symptomatic neonatal seizures.Children with post-neonatal epilepsy had a higher burden of pathogenic variants in epilepsy-associated genes compared to those without post-neonatal epilepsy.Future studies evaluating this association may lead to a better understanding of the risk of epilepsy after acute symptomatic neonatal seizures and elucidate molecular pathways that are dysregulated after brain injury and implicated in epileptogenesis.

BackgroundNeonatal seizures are associated with adverse neurologic sequelae including epilepsy in childhood. Here we aim to determine whether levels of cytokines in neonates with brain injury are associated with acute symptomatic seizures or remote epilepsy.MethodsThis is a cohort study of term newborns with encephalopathy at UCSF between 10/1993 and 1/2000 who had dried blood spots. Maternal, perinatal/postnatal, neuroimaging, and epilepsy variables were abstracted by chart review. Logistic regression was used to compare levels of cytokines with acute seizures and the development of epilepsy.ResultsIn a cohort of 26 newborns with neonatal encephalopathy at risk for hypoxic ischemic encephalopathy with blood spots for analysis, diffuse alterations in both pro- and anti-inflammatory cytokine levels were observed between those with (11/28, 39%) and without acute symptomatic seizures. Seventeen of the 26 (63%) patients had >2 years of follow-up and 4/17 (24%) developed epilepsy. Higher levels of pro-inflammatory cytokines IL-6 and TNF-α within the IL-1β pathway were significantly associated with epilepsy.ConclusionsElevations in pro-inflammatory cytokines in the IL-1β pathway were associated with later onset of epilepsy. Larger cohort studies are needed to confirm the predictive value of these circulating biomarkers.

Background An ancillary study of the High-Dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) trial for neonates with hypoxic-ischemic encephalopathy (HIE) and treated with therapeutic hypothermia examined the hypothesis that neonates randomized to receive erythropoietin (Epo) would have a lower seizure risk and burden compared with neonates who received placebo. Methods Electroencephalograms (EEGs) from 7/17 HEAL trial centers were reviewed. Seizure presence was compared across treatment groups using a logistic regression model adjusting for treatment, HIE severity, center, and seizure burden prior to the first dose. Among neonates with seizures, differences across treatment groups in median maximal hourly seizure burden were assessed using adjusted quantile regression models. Results Forty-six of 150 (31%) neonates had EEG seizures (31% in Epo vs 30% in placebo, p  = 0.96). Maximal hourly seizure burden after the study drug was not significantly different between groups (median 11.4 for Epo, IQR: 5.6, 18.1 vs median 9.7, IQR: 4.9, 21.0 min/h for placebo). Conclusion In neonates with HIE treated with hypothermia who were randomized to Epo or placebo, we found no meaningful between-group difference in seizure risk or burden. These findings are consistent with overall trial results, which do not support Epo use for neonates with HIE undergoing therapeutic hypothermia. Impact In the HEAL trial of erythropoietin (Epo) vs placebo for neonates with encephalopathy presumed due to hypoxic-ischemic encephalopathy (HIE) who were also treated with therapeutic hypothermia, electrographic seizures were detected in 31%, which is lower than most prior studies. Epo did not reduce the proportion of neonates with acute provoked seizures (31% in Epo vs 30% in placebo) or maximal hourly seizure burden after the study drug (median 11.4, IQR 5.6, 18.1 for Epo vs median 9.7, IQR 4.9, 21.0 min/h for placebo). There was no anti- or pro-convulsant effect of Epo when combined with therapeutic hypothermia for HIE.

Stroke is increasingly recognized as a significant cause of morbidity and mortality in children, and as a financial burden for families and society. Recent studies have identified and confirmed presumptive risk factors, and have identified novel associations with childhood arterial ischemic stroke. A better understanding of risk factors for stroke in children, which differ from the atherosclerotic risk factors in adults, is the first step needed to improve strategies for stroke prevention and intervention, and ultimately minimize the physical, mental, and financial burden of arterial ischemic stroke. Here, we discuss recent advances in research for selected childhood stroke risk factors, highlighting the progress made in our understanding of etiologic mechanisms and pathophysiology, and address the future directions for acute and long-term treatment strategies for pediatric stroke.

Importance The ability to predict neurodevelopmental impairment (NDI) for infants diagnosed with hypoxic ischemic encephalopathy (HIE) is important for parental guidance and clinical treatment as well as for stratification of patients for future neurotherapeutic studies. Objectives To examine the effect of erythropoietin on plasma inflammatory mediators in infants with moderate or severe HIE and to develop a panel of circulating biomarkers that improves the projection of 2-year NDI over and above the clinical data available at the time of birth. Design, Setting, and Participants This study is a preplanned secondary analysis of prospectively collected data from infants enrolled in the High-Dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) Trial, which tested the efficacy of erythropoietin as an adjunctive neuroprotective therapy to therapeutic hypothermia. The study was conducted at 17 academic sites comprising 23 neonatal intensive care units in the United States between January 25, 2017, and October 9, 2019, with follow-up through October 2022. Overall, 500 infants born at 36 weeks’ gestation or later with moderate or severe HIE were included. Intervention Erythropoietin treatment 1000 U/kg/dose on days 1, 2, 3, 4 and 7. Main Outcomes and Measures Plasma erythropoietin was measured in 444 infants (89%) within 24 hours after birth. A subset of 180 infants who had plasma samples available at baseline (day 0/1), day 2, and day 4 after birth and either died or had 2-year Bayley Scales of Infant Development III assessments completed were included in the biomarker analysis. Results The 180 infants included in this substudy had a mean (SD) gestational age of 39.1 (1.5) weeks, and 83 (46%) were female. Infants who received erythropoietin had increased concentrations of erythropoietin at day 2 and day 4 compared with baseline. Erythropoietin treatment did not alter concentrations of other measured biomarkers (eg, difference in interleukin [IL] 6 between groups on day 4: −1.3 pg/mL; 95% CI, −4.8 to 2.0 pg/mL). After adjusting for multiple comparisons, we identified 6 plasma biomarkers (C5a, interleukin [IL] 6, and neuron-specific enolase at baseline; IL-8, tau, and ubiquitin carboxy-terminal hydrolase-L1 at day 4) that significantly improved estimations of death or NDI at 2 years compared with clinical data alone. However, the improvement was only modest, increasing the AUC from 0.73 (95% CI, 0.70-0.75) to 0.79 (95% CI, 0.77-0.81;P = .01), corresponding to a 16% (95% CI, 5%-44%) increase in correct classification of participant risk of death or NDI at 2 years. Conclusions and Relevance In this study, erythropoietin treatment did not reduce biomarkers of neuroinflammation or brain injury in infants with HIE. Circulating biomarkers modestly improved estimation of 2-year outcomes. Trial Registration ClinicalTrials.gov Identifier:NCT02811263

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (p<0.01). Fewer readers could lateralize seizure-onset (p<0.05). The confidence measures of the assignments were low (probable-unlikely), but increased using AR2 (p<0.05). The ICC for identifying the time of seizure-onset was 0.15 (95% confidence interval (CI), 0.11-0.18) using AR1 and 0.26 (95% CI 0.21-0.30) using AR2.  The EEG interpretations were often consistent with behavioral, neurophysiological, and neuro-radiological findings, with left sided assignments correct in 95.9% (CI 85.7-98.9%, n=4) of cases using AR2, and 91.9% (77.0-97.5%) (n=4) of cases using AR1. Conclusions: EEG artifact reduction methods for localizing seizure-onset does not result in high rates of interpretability, reader confidence, and inter-reader agreement. However, the assignments by groups of readers are often congruent with other clinical data. Utilization of the AR2 software method may improve the validity of ictal EEG artifact reduction.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (p<0.01). Fewer readers could lateralize seizure-onset (p<0.05). The confidence measures of the assignments were low (probable-unlikely), but increased using AR2 (p<0.05). The ICC for identifying the time of seizure-onset was 0.15 (95% confidence interval (CI), 0.11-0.18) using AR1 and 0.26 (95% CI 0.21-0.30) using AR2.  The EEG interpretations were often consistent with behavioral, neurophysiological, and neuro-radiological findings, with left sided assignments correct in 95.9% (CI 85.7-98.9%, n=4) of cases using AR2. Conclusions: EEG artifact reduction methods for localizing seizure-onset does not result in high rates of interpretability, reader confidence, and inter-reader agreement. However, the assignments by groups of readers are often congruent with other clinical data. Utilization of the AR2 software method may improve the validity of ictal EEG artifact reduction.

The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.

The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.