Overnight Electroencephalogram to Forecast Epilepsy Development in Children with Autism Spectrum Disorders.

OBJECTIVE: To establish the utility of long-term electroencephalogram (EEG) in forecasting epilepsy onset in children with autism spectrum disorder (ASD). STUDY DESIGN: A single-institution, retrospective analysis of children with ASD, examining long-term overnight EEG recordings collected over a period of 15 years, was conducted. Clinical EEG findings, patient demographics, medical histories, and additional Autism Diagnostic Observation Schedule data were examined. Predictors for the timing of epilepsy onset were evaluated using survival analysis and Cox regression. RESULTS: Among 151 patients, 17.2% (n = 26) developed unprovoked seizures (Sz group), while 82.8% (n = 125) did not (non-Sz group). The Sz group displayed a higher percentage of interictal epileptiform discharges (IEDs) in their initial EEGs compared with the non-Sz group (46.2% vs 20.0%, P = .01). The Sz group also exhibited a greater frequency of slowing (42.3% vs 13.6%, P < .01). The presence of IEDs or slowing predicted an earlier seizure onset, based on survival analysis. Multivariate Cox proportional hazards regression revealed that the presence of any IEDs (HR 3.83, 95% CI 1.38-10.65, P = .01) or any slowing (HR 2.78, 95% CI 1.02-7.58, P = .046 significantly increased the risk of developing unprovoked seizures. CONCLUSION: Long-term EEGs are valuable for predicting future epilepsy in children with ASD. These findings can guide clinicians in early education and potential interventions for epilepsy prevention.

Discovering Neurophysiological Characteristics of Pathological High-Frequency Oscillations in Epilepsy with an Explainable Deep Generative Model.

Interictal high-frequency oscillation (HFO) is a promising biomarker of the epileptogenic zone (EZ). However, objective definitions to distinguish between pathological and physiological HFOs have remained elusive, impeding HFOs' clinical applications. We employed self-supervised deep generative variational autoencoders to learn such discriminative HFO features directly from their morphologies in a data-driven manner. We studied a large retrospective cohort of 185 patients who underwent intracranial monitoring and analyzed 686,410 candidate HFO events collected from 18,265 brain contacts across diverse brain regions. The model automatically clustered HFOs into distinct morphological groups in the latent space. One cluster consisted of putative morphologically defined pathological HFOs (mpHFOs): HFOs in that cluster were observed to be associated with spikes and exhibited high signal intensity both in the HFO band (>80 Hz) at detection and in the sub-HFO band (10-80 Hz) surrounding the detection and were primarily localized in the seizure onset zone (SOZ). Moreover, resection of brain regions based on a higher prevalence of interictal mpHFOs better predicted postoperative seizure outcomes than current clinical standards based on SOZ removal. Our self-supervised, explainable, deep generative model distills pathological HFOs and thus potentially helps delineate the EZ purely from interictal intracranial EEG data.

Bilateral centromedian nucleus of thalamus responsive neurostimulation for pediatric-onset drug-resistant epilepsy.

Neuromodulation therapies offer an efficacious treatment alternative for patients with drug-resistant epilepsy (DRE), particularly those unlikely to benefit from surgical resection. Here we present our retrospective single-center case series of patients with pediatric-onset DRE who underwent responsive neurostimulation (RNS) depth electrode implantation targeting the bilateral centromedian nucleus (CM) of the thalamus between October 2020 and October 2022. Sixteen patients were identified; seizure outcomes, programming parameters, and complications at follow-up were reviewed. The median age at implantation was 13 years (range 3.6-22). Six patients (38%) were younger than 12 years of age at the time of implantation. Ictal electroencephalography (EEG) patterns during patients' most disabling seizures were reliably detected. Ten patients (62%) achieved 50% or greater reduction in seizure frequency at a median 1.3 years (range 0.6-2.6) of follow-up. Eight patients (50%) experienced sensorimotor side effects, and three patients (19%) had superficial pocket infection, prompting the removal of the RNS device. Side effects of stimulation were experienced mostly in monopolar-cathodal configuration and alleviated with programming change to bipolar configuration or low-frequency stimulation. Closed-loop neurostimulation using RNS targeting bilateral CM is a feasible and useful therapy for patients with pediatric-onset DRE.

Computational EEG attributes predict response to therapy for epileptic spasms.

OBJECTIVE: We set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes. METHODS: We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively. RESULTS: After statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044). CONCLUSION: This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse. SIGNIFICANCE: This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Epileptic spasms relapse is associated with response latency but not conventional attributes of post-treatment EEG.

OBJECTIVE: Relapse of epileptic spasms after initial treatment of infantile epileptic spasms syndrome (IESS) is common. However, past studies of small cohorts have inconsistently linked relapse risk to etiology, treatment modality, and EEG features upon response. Using a large single-center IESS cohort, we set out to quantify the risk of epileptic spasms relapse and identify specific risk factors. METHODS: We identified all children with epileptic spasms at our center using a clinical EEG database. Using the electronic medical record, we confirmed IESS syndrome classification and ascertained treatment, response, time to relapse, etiology, EEG features, and other demographic factors. Relapse-free survival analysis was carried out using Cox proportional hazards regression. RESULTS: Among 599 children with IESS, 197 specifically responded to hormonal therapy and/or vigabatrin (as opposed to surgery or other second-line treatments). In this study, 41 (21%) subjects exhibited relapse of epileptic spasms within 12 months of response. Longer duration of IESS prior to response (>3 months) was strongly associated with shorter latency to relapse (hazard ratio = 3.11; 95% CI 1.59-6.10; p = 0.001). Relapse was not associated with etiology, developmental status, or any post-treatment EEG feature. SIGNIFICANCE: This study suggests that long duration of IESS before response is the single largest clinical predictor of relapse risk, and therefore underscores the importance of prompt and successful initial treatment. Further study is needed to evaluate candidate biomarkers of epileptic spasms relapse and identify treatments to mitigate this risk. PLAIN LANGUAGE SUMMARY: Relapse of infantile spasms is common after initially successful treatment. With study of a large group of children with infantile spasms, we determined that relapse is linked to long duration of infantile spasms. In contrast, relapse was not associated with the cause of infantile spasms, developmental measures, or EEG features at the time of initial response. Further study is needed to identify tools to predict impending relapse of infantile spasms.

Interrater reliability of interictal EEG waveforms in Lennox-Gastaut Syndrome.

OBJECTIVE: Identification of EEG waveforms is critical for diagnosing Lennox-Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS. METHODS: A novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs). The algorithm was applied to EEG from 20 LGS subjects and 20 healthy controls during NREM sleep, and 1350 EOIs were identified. Three raters independently reviewed the EOIs within isolated 15-second EEG segments in a randomized, blinded fashion. For each EOI, the raters assigned a waveform label (spike and slow wave, generalized paroxysmal fast activity, seizure, spindle, vertex, muscle, artifact, nothing, or other) and indicated the perceived subject type (LGS or control). RESULTS: Labeling of subject type had 85% accuracy across all EOIs and an IRR of κ =0.790, suggesting that brief segments of EEG containing high-power waveforms can be reliably classified as pathological or normal. Waveform labels were less consistent, with κ =0.558, and the results were highly variable for different categories of waveforms. Label mismatches typically occurred when one reviewer selected "nothing," suggesting that reviewers had different thresholds for applying named labels. SIGNIFICANCE: Classification of EEG waveforms associated with LGS has weak IRR, due in part to varying thresholds applied during visual review. Computational methods to objectively define EEG biomarkers of LGS may improve IRR and aid clinical decision-making.

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. METHODS: We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. RESULTS: The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. CONCLUSIONS: HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE: Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.

Efficacy of cannabidiol in convulsive and nonconvulsive seizure types associated with treatment-resistant epilepsies in the Expanded Access Program.

The cannabidiol (CBD) Expanded Access Program (EAP), initiated in 2014, provided CBD (Epidiolex) to patients with treatment-resistant epilepsy (TRE). In the final pooled analysis of 892 patients treated through January 2019 (median exposure = 694 days), CBD treatment was associated with a 46%-66% reduction in median monthly total (convulsive plus nonconvulsive) seizure frequency. CBD was well tolerated, and adverse events were consistent with previous findings. We used pooled EAP data to investigate the effectiveness of add-on CBD therapy for individual convulsive seizure types (clonic, tonic, tonic-clonic, atonic, focal to bilateral tonic-clonic), nonconvulsive seizure types (focal with and without impaired consciousness, absence [typical and atypical], myoclonic, myoclonic absence), and epileptic spasms. CBD treatment was associated with a reduction in the frequency of convulsive seizure types (median percentage reduction = 47%-100%), and nonconvulsive seizure types and epileptic spasms (median percentage reduction = 50%-100%) across visit intervals through 144 weeks of treatment. Approximately 50% of patients had ≥50% reduction in convulsive and nonconvulsive seizure types and epileptic spasms at nearly all intervals. These results show a favorable effect of long-term CBD use in patients with TRE, who may experience various convulsive and nonconvulsive seizure types. Future controlled trials are needed to confirm these findings.

Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy.

Objective: This study aimed to explore sensitive detection methods and deep learning (DL)-based classification for pathological high-frequency oscillations (HFOs). Methods: We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent resection after chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for pathological features based on spike association and time-frequency plot characteristics. A DL-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. Results: The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors exhibited the most pathological features. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. Conclusions: HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. Significance: Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes. HIGHLIGHTS: HFOs detected by the MNI detector showed different traits and higher pathological bias than those detected by the STE detectorHFOs detected by both MNI and STE detectors (the Intersection HFOs) were deemed the most pathologicalA deep learning-based classification was able to distill pathological HFOs, regard-less of the initial HFO detection methods.

Clustered variants in the 5' coding region of TRA2B cause a distinctive neurodevelopmental syndrome.

PURPOSE: Transformer2 proteins (Tra2α and Tra2ß) control splicing patterns in human cells, and no human phenotypes have been associated with germline variants in these genes. The aim of this work was to associate germline variants in the TRA2B gene to a novel neurodevelopmental disorder. METHODS: A total of 12 individuals from 11 unrelated families who harbored predicted loss-of-function monoallelic variants, mostly de novo, were recruited. RNA sequencing and western blot analyses of Tra2ß-1 and Tra2ß-3 isoforms from patient-derived cells were performed. Tra2ß1-GFP, Tra2ß3-GFP and CHEK1 exon 3 plasmids were transfected into HEK-293 cells. RESULTS: All variants clustered in the 5' part of TRA2B, upstream of an alternative translation start site responsible for the expression of the noncanonical Tra2ß-3 isoform. All affected individuals presented intellectual disability and/or developmental delay, frequently associated with infantile spasms, microcephaly, brain anomalies, autism spectrum disorder, feeding difficulties, and short stature. Experimental studies showed that these variants decreased the expression of the canonical Tra2ß-1 isoform, whereas they increased the expression of the Tra2ß-3 isoform, which is shorter and lacks the N-terminal RS1 domain. Increased expression of Tra2ß-3-GFP were shown to interfere with the incorporation of CHEK1 exon 3 into its mature transcript, normally incorporated by Tra2ß-1. CONCLUSION: Predicted loss-of-function variants clustered in the 5' portion of TRA2B cause a new neurodevelopmental syndrome through an apparently dominant negative disease mechanism involving the use of an alternative translation start site and the overexpression of a shorter, repressive Tra2ß protein.

Characterizing physiological high-frequency oscillations using deep learning.

Objective.Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL).Approach.We studied children with neocortical epilepsy who underwent intracranial strip/grid evaluation. Time-series EEG data were transformed into DL training inputs. The eloquent cortex (EC) was defined by functional cortical mapping and used as a DL label. Morphological characteristics of HFOs obtained from EC (ecHFOs) were distilled and interpreted through a novel weakly supervised DL model.Main results.A total of 63 379 interictal intracranially-recorded HFOs from 18 children were analyzed. The ecHFOs had lower amplitude throughout the 80-500 Hz frequency band around the HFO onset and also had a lower signal amplitude in the low frequency band throughout a one-second time window than non-ecHFOs, resembling a bell-shaped template in the time-frequency map. A minority of ecHFOs were HFOs with spikes (22.9%). Such morphological characteristics were confirmed to influence DL model prediction via perturbation analyses. Using the resection ratio (removed HFOs/detected HFOs) of non-ecHFOs, the prediction of postoperative seizure outcomes improved compared to using uncorrected HFOs (area under the ROC curve of 0.82, increased from 0.76).Significance.We characterized salient features of physiological HFOs using a DL algorithm. Our results suggested that this DL-based HFO classification, once trained, might help separate physiological from pathological HFOs, and efficiently guide surgical resection using HFOs.

Association of Time to Clinical Remission With Sustained Resolution in Children With New-Onset Infantile Spasms.

BACKGROUND AND OBJECTIVES: Standard therapies (adrenocorticotropic hormone [ACTH], oral steroids, or vigabatrin) fail to control infantile spasms in almost half of children. Early identification of nonresponders could enable rapid initiation of sequential therapy. We aimed to determine the time to clinical remission after appropriate infantile spasms treatment initiation and identify predictors of the time to infantile spasms treatment response. METHODS: The National Infantile Spasms Consortium prospectively followed children aged 2-24 months with new-onset infantile spasms at 23 US centers (2012-2018). We included children treated with standard therapy (ACTH, oral steroids, or vigabatrin). Sustained treatment response was defined as having the last clinically recognized infantile spasms on or before treatment day 14, absence of hypsarrhythmia on EEG 2-4 weeks after treatment, and persistence of remission to day 30. We analyzed the time to treatment response and assessed clinical characteristics to predict sustained treatment response. RESULTS: Among 395 infants, clinical infantile spasms remission occurred in 43% (n = 171) within the first 2 weeks of treatment, of which 81% (138/171) responded within the first week of treatment. There was no difference in the median time to response across standard therapies (ACTH: median 4 days, interquartile range [IQR] 3-7; oral steroids: median 3 days, IQR 2-5; vigabatrin: median 3 days, IQR 1-6). Individuals without hypsarrhythmia on the pretreatment EEG (i.e., abnormal but not hypsarrhythmia) were more likely to have early treatment response than infants with hypsarrhythmia at infantile spasms onset (hazard ratio 2.23, 95% CI 1.39-3.57). No other clinical factors predicted early responders to therapy. DISCUSSION: Remission after first infantile spasms treatment can be identified by treatment day 7 in most children. Given the importance of early and effective treatment, these data suggest that children who do not respond to standard infantile spasms therapy within 1 week should be reassessed immediately for additional standard treatment. This approach could optimize outcomes by facilitating early sequential therapy for children with infantile spasms.

Inequities in Therapy for Infantile Spasms: A Call to Action.

OBJECTIVE: The aim of this study was to determine whether selection of treatment for children with infantile spasms (IS) varies by race/ethnicity. METHODS: The prospective US National Infantile Spasms Consortium database includes children with IS treated from 2012 to 2018. We examined the relationship between race/ethnicity and receipt of standard IS therapy (prednisolone, adrenocorticotropic hormone, vigabatrin), adjusting for demographic and clinical variables using logistic regression. Our primary outcome was treatment course, which considered therapy prescribed for the first and, when needed, the second IS treatment together. RESULTS: Of 555 children, 324 (58%) were non-Hispanic white, 55 (10%) non-Hispanic Black, 24 (4%) non-Hispanic Asian, 80 (14%) Hispanic, and 72 (13%) other/unknown. Most (398, 72%) received a standard treatment course. Insurance type, geographic location, history of prematurity, prior seizures, developmental delay or regression, abnormal head circumference, hypsarrhythmia, and IS etiologies were associated with standard therapy. In adjusted models, non-Hispanic Black children had lower odds of receiving a standard treatment course compared with non-Hispanic white children (odds ratio [OR], 0.42; 95% confidence interval [CI], 0.20-0.89; p = 0.02). Adjusted models also showed that children with public (vs. private) insurance had lower odds of receiving standard therapy for treatment 1 (OR, 0.42; CI, 0.21-0.84; p = 0.01). INTERPRETATION: Non-Hispanic Black children were more often treated with non-standard IS therapies than non-Hispanic white children. Likewise, children with public (vs. private) insurance were less likely to receive standard therapies. Investigating drivers of inequities, and understanding the impact of racism on treatment decisions, are critical next steps to improve care for patients with IS. ANN NEUROL 2022;92:32-44.

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach.

Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The 'purification power' of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%, P < 0.001). While the resection ratio of detected high-frequency oscillations (number of resected events/number of detected events) did not correlate significantly with post-operative seizure freedom (the area under the curve = 0.76, P = 0.06), the resection ratio of epileptogenic high-frequency oscillations positively correlated with post-operative seizure freedom (the area under the curve = 0.87, P = 0.01). We discovered that epileptogenic high-frequency oscillations had a higher signal intensity associated with ripple (80-250 Hz) and fast ripple (250-500 Hz) bands at the high-frequency oscillation onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-epileptogenic high-frequency oscillations. We then designed perturbations on the input of the trained model for non-epileptogenic high-frequency oscillations to determine the model's decision-making logic. The model confidence significantly increased towards epileptogenic high-frequency oscillations by the artificial introduction of the inverted T-shaped signal template (mean probability increase: 0.285, P < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, P < 0.001). With this deep learning-based framework, we reliably replicated high-frequency oscillation classification tasks by human experts. Using a reverse engineering technique, we distinguished epileptogenic high-frequency oscillations from others and identified its salient features that aligned with current knowledge.

Evolution of Cortical Functional Networks in Healthy Infants.

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from controls during wakefulness.

OBJECTIVE: Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake. METHODS: Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC). RESULTS: The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85). CONCLUSIONS: We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.

Real-World Preliminary Experience With Responsive Neurostimulation in Pediatric Epilepsy: A Multicenter Retrospective Observational Study.

BACKGROUND: Despite the well-documented utility of responsive neurostimulation (RNS, NeuroPace) in adult epilepsy patients, literature on the use of RNS in children is limited. OBJECTIVE: To determine the real-world efficacy and safety of RNS in pediatric epilepsy patients. METHODS: Patients with childhood-onset drug-resistant epilepsy treated with RNS were retrospectively identified at 5 pediatric centers. Reduction of disabling seizures and complications were evaluated for children (<18 yr) and young adults (>18 yr) and compared with prior literature pertaining to adult patients. RESULTS: Of 35 patients identified, 17 were <18 yr at the time of RNS implantation, including a 3-yr-old patient. Four patients (11%) had concurrent resection. Three complications, requiring additional surgical interventions, were noted in young adults (2 infections [6%] and 1 lead fracture [3%]). No complications were noted in children. Among the 32 patients with continued therapy, 2 (6%) achieved seizure freedom, 4 (13%) achieved ≥90% seizure reduction, 13 (41%) had ≥50% reduction, 8 (25%) had <50% reduction, and 5 (16%) experienced no improvement. The average follow-up duration was 1.7 yr (median 1.8 yr, range 0.3-4.8 yr). There was no statistically significant difference for seizure reduction and complications between children and young adults in our cohort or between our cohort and the adult literature. CONCLUSION: These preliminary data suggest that RNS is well tolerated and an effective off-label surgical treatment of drug-resistant epilepsy in carefully selected pediatric patients as young as 3 yr of age. Data regarding long-term efficacy and safety in children will be critical to optimize patient selection.

Abnormal sleep physiology in children with 15q11.2-13.1 duplication (Dup15q) syndrome.

BACKGROUND: Sleep disturbances in autism spectrum disorder (ASD) represent a common and vexing comorbidity. Clinical heterogeneity amongst these warrants studies of the mechanisms associated with specific genetic etiologies. Duplications of 15q11.2-13.1 (Dup15q syndrome) are highly penetrant for neurodevelopmental disorders (NDDs) such as intellectual disability and ASD, as well as sleep disturbances. Genes in the 15q region, particularly UBE3A and a cluster of GABAA receptor genes, are critical for neural development, synaptic protein synthesis and degradation, and inhibitory neurotransmission. During awake electroencephalography (EEG), children with Dup15q syndrome demonstrate increased beta band oscillations (12-30 Hz) that likely reflect aberrant GABAergic neurotransmission. Healthy sleep rhythms, necessary for robust cognitive development, are also highly dependent on GABAergic neurotransmission. We therefore hypothesized that sleep physiology would be abnormal in children with Dup15q syndrome. METHODS: To test the hypothesis that elevated beta oscillations persist in sleep in Dup15q syndrome and that NREM sleep rhythms would be disrupted, we computed: (1) beta power, (2) spindle density, and (3) percentage of slow-wave sleep (SWS) in overnight sleep EEG recordings from a cohort of children with Dup15q syndrome (n = 15) and compared them to age-matched neurotypical children (n = 12). RESULTS: Children with Dup15q syndrome showed abnormal sleep physiology with elevated beta power, reduced spindle density, and reduced or absent SWS compared to age-matched neurotypical controls. LIMITATIONS: This study relied on clinical EEG where sleep staging was not available. However, considering that clinical polysomnograms are challenging to collect in this population, the ability to quantify these biomarkers on clinical EEG-routinely ordered for epilepsy monitoring-opens the door for larger-scale studies. While comparable to other human studies in rare genetic disorders, a larger sample would allow for examination of the role of seizure severity, medications, and developmental age that may impact sleep physiology. CONCLUSIONS: We have identified three quantitative EEG biomarkers of sleep disruption in Dup15q syndrome, a genetic condition highly penetrant for ASD. Insights from this study not only promote a greater mechanistic understanding of the pathophysiology defining Dup15q syndrome, but also lay the foundation for studies that investigate the association between sleep and cognition. Abnormal sleep physiology may undermine healthy cognitive development and may serve as a quantifiable and modifiable target for behavioral and pharmacological interventions.

Comparative Effectiveness of Initial Treatment for Infantile Spasms in a Contemporary US Cohort.

OBJECTIVE: Compare the effectiveness of initial treatment for infantile spasms. METHODS: The National Infantile Spasms Consortium prospectively followed children with new onset infantile spasms that began at age 2-24 months at 23 US centers (2012-2018). Freedom from treatment failure at 60 days required no second treatment for infantile spasms and no clinical spasms after 30 days of treatment initiation. We managed treatment selection bias with propensity score weighting and within-center correlation with generalized estimating equations. RESULTS: Freedom from treatment failure rates were: ACTH 88/190 (46%), oral steroids 42/95 (44%), vigabatrin 32/87 (37%), and non-standard therapy 4/51 (8%). Changing from oral steroids to ACTH was not estimated to affect response (observed 44% estimated to change to 44% [95% CI 34-54]). Changing from non-standard therapy to ACTH would improve response from 8% to 39 [17-67]%, and to oral steroids from 8% to 38 [15-68]%. There were large but not statistically significant estimated effects of changing from vigabatrin to ACTH (29% to 42 [15-75]%), vigabatrin to oral steroids (29% to 42 [28-57]%), and non-standard therapy to vigabatrin (8% to 20 [6-50]%). Among children treated with vigabatrin, those with tuberous sclerosis complex (TSC) responded more often than others (62% vs 29%; p<0.05) CONCLUSION: Compared to non-standard therapy, ACTH and oral steroids are superior for initial treatment of infantile spasms. The estimated effectiveness of vigabatrin is between ACTH / oral steroids and non-standard therapy, though the sample was underpowered for statistical confidence. When used, vigabatrin worked best for TSC. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that for children with new onset infantile spasms, ACTH or oral steroids were superior to non-standard therapies.