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.

WONOEP 2022: Neurotechnology for the diagnosis of epilepsy.

Epilepsy's myriad causes and clinical presentations ensure that accurate diagnoses and targeted treatments remain a challenge. Advanced neurotechnologies are needed to better characterize individual patients across multiple modalities and analytical techniques. At the XVIth Workshop on Neurobiology of Epilepsy: Early Onset Epilepsies: Neurobiology and Novel Therapeutic Strategies (WONOEP 2022), the session on "advanced tools" highlighted a range of approaches, from molecular phenotyping of genetic epilepsy models and resected tissue samples to imaging-guided localization of epileptogenic tissue for surgical resection of focal malformations. These tools integrate cutting edge research, clinical data acquisition, and advanced computational methods to leverage the rich information contained within increasingly large datasets. A number of common challenges and opportunities emerged, including the need for multidisciplinary collaboration, multimodal integration, potential ethical challenges, and the multistage path to clinical translation. Despite these challenges, advanced epilepsy neurotechnologies offer the potential to improve our understanding of the underlying causes of epilepsy and our capacity to provide patient-specific treatment.

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.

Uncovering spatiotemporal dynamics of the corticothalamic network at ictal onset.

OBJECTIVE: Although the clinical efficacy of deep brain stimulation targeting the anterior nucleus (AN) and centromedian nucleus (CM) of the thalamus has been actively investigated for the treatment of medication-resistant epilepsy, few studies have investigated dynamic ictal changes in corticothalamic connectivity in human electroencephalographic (EEG) recording. This study aims to establish the complex spatiotemporal dynamics of the ictal corticothalamic network associated with various seizure foci. METHODS: We analyzed 10 patients (aged 2.7-28.1 years) with medication-resistant focal epilepsy who underwent stereotactic EEG evaluation with thalamic sampling. We examined both undirected and directed connectivity, incorporating coherence and spectral Granger causality analysis (GCA) between the diverse seizure foci and thalamic nuclei (AN and CM) at ictal onset. RESULTS: In our analysis of 36 seizures, coherence between seizure onset and thalamic nuclei increased across all frequencies, especially in slower bands (delta, theta, alpha). GCA showed increased information flow from seizure onset to the thalamus across all frequency bands, but outflows from the thalamus were mainly in slower frequencies, particularly delta. In the subgroup analysis based on various seizure foci, the delta coherence showed a more pronounced increase at CM than at AN during frontal lobe seizures. Conversely, in limbic seizures, the delta coherence increase was greater at AN compared to CM. SIGNIFICANCE: It appears that the delta frequency plays a pivotal role in modulating the corticothalamic network during seizures. Our results underscore the significance of comprehending the spatiotemporal dynamics of the corticothalamic network at ictal onset, and this knowledge could guide personalized responsive neuromodulation treatment strategies.

EEG-based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms.

OBJECTIVE: Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls. METHODS: We retrospectively analyzed peri-ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age-matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency- and time-dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel-based closeness centrality (CC), over frequency and time. RESULTS: GE in the fast ripple band (251-400 Hz) was significantly greater following the onset of spasms in all patients (P < 0.05). Fast ripple networks during the first 10s from spasm onset show enhanced anteroposterior gradient in connectivity (posterior > central > anterior, Kruskal-Wallis P < 0.001), with maximum CC over the centroparietal channels in 10/14 patients. Additionally, this anteroposterior gradient in CC connectivity is observed during spasms but not during the interictal awake or asleep states of infants with IS. In controls, anteroposterior gradient in fast ripple CC was noted during arousals and wakefulness but not during sleep. There was also a simultaneous decrease in GE in the 5-8 Hz range after the onset of spasms (P < 0.05), of unclear biological significance. SIGNIFICANCE: We identified an anteroposterior gradient in the CC connectivity of fast ripple hubs during spasms. This anteroposterior gradient observed during spasms is similar to the anteroposterior gradient in the CC connectivity observed in wakefulness or arousals in controls, suggesting that this state change is related to arousal networks.

Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction.

OBJECTIVE: In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models. METHODS: Our proposed model incorporates modular Volterra kernel convolutional networks and bidirectional recurrent networks in combination with the phase amplitude cross-frequency coupling features derived from scalp EEG. They are applied to the standard CHB-MIT dataset containing focal epilepsy episodes as well as two other datasets from the Montefiore Medical Center and the University of California Los Angeles that provide data of patients experiencing infantile spasm (IS) syndrome. RESULTS: Overall, in this study, the networks can produce accurate predictions (100%) and significant detection latencies (10 min). Furthermore, the biomimetic network outperforms conventional ones by producing no false positives. SIGNIFICANCE: Biomimetic neural networks utilize extensive knowledge about processing and learning in the electrical networks of the brain. Predicting seizures in adults can improve their quality of life. Epileptic spasms in infants are part of a particular seizure type that needs identifying when suspicious behaviors are noticed in babies. Predicting epileptic spasms within a given time frame (the prediction horizon) suggests their existence and allows an epileptologist to flag an EEG trace for future review.

Hemispheric epilepsy surgery for hemimegalencephaly: The UCLA experience.

OBJECTIVES: Hemimegalencephaly (HME) is a rare congenital brain malformation presenting predominantly with drug-resistant epilepsy. Hemispheric disconnective surgery is the mainstay of treatment; however, little is known about how postoperative outcomes compare across techniques. Thus we present the largest single-center cohort of patients with HME who underwent epilepsy surgery and characterize outcomes. METHODS: This observational study included patients with HME at University of California Los Angeles (UCLA) from 1984 to 2021. Patients were stratified by surgical intervention: anatomic hemispherectomy (AH), functional hemispherectomy (FH), or less-than-hemispheric resection (LTH). Seizure freedom, functional outcomes, and operative complications were compared across surgical approaches. Regression analysis identified clinical and intraoperative variables that predict seizure outcomes. RESULTS: Of 56 patients, 43 (77%) underwent FH, 8 (14%) underwent AH, 2 (4%) underwent LTH, 1 (2%) underwent unknown hemispherectomy type, and 2 (4%) were managed non-operatively. At median last follow-up of 55 months (interquartile range [IQR] 20-92 months), 24 patients (49%) were seizure-free, 17 (30%) required cerebrospinal fluid (CSF) shunting for hydrocephalus, 9 of 43 (21%) had severe developmental delay, 8 of 38 (21%) were non-verbal, and 15 of 38 (39%) were non-ambulatory. There was one (2%) intraoperative mortality due to exsanguination earlier in this cohort. Of 12 patients (29%) requiring revision surgery, 6 (50%) were seizure-free postoperatively. AH, compared to FH, was not associated with statistically significant improved seizure freedom (hazard ratio [HR] = .48, p = .328), although initial AH trended toward greater odds of seizure freedom (75% vs 46%, p = .272). Younger age at seizure onset (HR = .29, p = .029), lack of epilepsia partialis continua (EPC) (HR = .30, p = .022), and no contralateral seizures on electroencephalography (EEG) (HR = .33, p = .039) independently predicted longer duration of seizure freedom. SIGNIFICANCE: This study helps inform physicians and parents of children who are undergoing surgery for HME by demonstrating that earlier age at seizure onset, absence of EPC, and no contralateral EEG seizures were associated with longer postoperative seizure freedom. At our center, initial AH for HME may provide greater odds of seizure freedom with complications and functional outcomes comparable to those of FH.

Structural connections of the centromedian nucleus of thalamus and their relevance for neuromodulation in generalized drug-resistant epilepsy: insight from a tractography study.

Background: Epilepsy is a widespread neurologic disorder and almost one-third of patients suffer from drug-resistant epilepsy (DRE). Neuromodulation targeting the centromediannucleus of the thalamus (CM) has been showing promising results for patients with generalized DRE who are not surgical candidates. Recently, the effect of CM- deep brain stimulation (DBS) in DRE patients was investigated in the Electrical Stimulation of Thalamus for Epilepsy of Lennox-Gastaut phenotype (ESTEL) trial, a monocentric randomized-controlled study. The same authors described a 'cold-spot' and a 'sweet-spot', which are defined as the volume of stimulation in the thalamus yielding the least and the best clinical response, respectively. However, it remains unclear which structural connections may contribute to the anti-seizure effect of the stimulation. Objective: We investigated the differences in structural connectivity among CM, the sweet-spot and the cold-spot. Furthermore, we tried to validate our results in a cohort of DRE patients who underwent CM-DBS or CM-RNS (responsive neurostimulation). We hypothesized that the sweet-spot would share similar structural connectivity with responder patients. Methods: By using the software FMRIB Software Library (FSL), probabilistic tractography was performed on 100 subjects from the Human Connectome Project to calculate the probability of connectivity of the whole CM, the sweet-spot and the cold-spot to 45 cortical and subcortical areas. Results among the three seeds were compared with multivariate analysis of variance (MANOVA). Similarly, the structural connectivity of volumes of tissue activated (VTAs) from eight DRE patients was investigated. Patients were divided into responders and non-responders based on the degree of reduction in seizure frequency, and the mean probabilities of connectivity were similarly compared between the two groups. Results: The sweet-spot demonstrated a significantly higher probability of connectivity (p < 0.001) with the precentral gyrus, superior frontal gyrus, and the cerebellum than the whole CM and the cold-spot. Responder patients displayed a higher probability of connectivity with both ipsilateral (p = 0.011) and contralateral cerebellum (p = 0.04) than the non-responders. Conclusion: Cerebellar connections seem to contribute to the beneficial effects of CM-neuromodulation in patients with drug-resistant generalized epilepsy.

Uncovering Spatiotemporal Dynamics of the Corticothalamic Network during Seizures.

Objective: Although the clinical efficacy of deep brain stimulation targeting the anterior nucleus (AN) and centromedian nucleus (CM) of the thalamus has been actively investigated for the treatment of medication-resistant epilepsy, few studies have investigated dynamic ictal changes in corticothalamic connectivity in human EEG recording. This study aims to establish the complex spatiotemporal dynamics of the ictal corticothalamic network associated with various seizure foci. Methods: We analyzed ten patients (aged 2.7-28.1) with medication-resistant focal epilepsy who underwent stereotactic EEG evaluation with thalamic coverage. We examined both undirected and directed connectivity, incorporating coherence and spectral Granger causality analysis (GCA) between the diverse seizure foci and thalamic nuclei (AN and CM). Results: In our analysis of 36 seizures, coherence between seizure onset and thalamic nuclei increased across all frequencies, especially in slower bands (delta, theta, alpha). GCA showed increased information flow from seizure onset to the thalamus across all frequency bands, but outflows from the thalamus were mainly in slower frequencies, particularly delta. In the subgroup analysis based on various seizure foci, the delta coherence showed a more pronounced increase at CM than at AN during frontal lobe seizures. Conversely, in limbic seizures, the delta coherence increase was greater at AN compared to CM. Interpretation: It appears that the delta frequency plays a pivotal role in modulating the corticothalamic network during seizures. Our results underscore the significance of comprehending the spatiotemporal dynamics of the corticothalamic network during seizures, and this knowledge could guide personalized neuromodulation treatment strategies.

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.

Characteristics of ictal thalamic EEG in pediatric-onset neocortical focal epilepsy.

OBJECTIVE: To characterize ictal EEG change in the centromedian (CM) and anterior nucleus (AN) of the thalamus, using stereoelectroencephalography (SEEG) recordings. METHODS: Forty habitual seizures were analyzed in nine patients with pediatric-onset neocortical drug-resistant epilepsy who underwent SEEG (age 2-25 y) with thalamic coverage. Both visual and quantitative analysis was used to evaluate ictal EEG signal in the cortex and thalamus. The amplitude and cortico-thalamic latencies of broadband frequencies at ictal onset were measured. RESULTS: Visual analysis demonstrated consistent detection of ictal EEG changes in both the CM nucleus and AN nucleus with latency to thalamic ictal EEG changes of less than 400 ms in 95% of seizures, with low-voltage fast activity being the most common ictal pattern. Quantitative broadband amplitude analysis showed consistent power changes across the frequency bands, corresponding to ictal EEG onset, while while ictal EEG latency was variable from -18.0 seconds to 13.2 seconds. There was no significant difference between detection of CM and AN ictal activity on visual or amplitude analysis. Four patients with subsequent thalamic responsive neurostimulation (RNS) demonstrated ictal EEG changes consistent with SEEG findings. CONCLUSIONS: Ictal EEG changes were consistently seen at the CM and AN of the thalamus during neocortical seizures. SIGNIFICANCE: It may be feasible to use a closed-loop system in the thalamus to detect and modulate seizure activity for neocortical epilepsy.

Characteristics of ictal thalamic EEG in pediatric-onset neocortical focal epilepsy.

Objective: To characterize ictal EEG change in the centromedian (CM) and anterior nucleus (AN) of the thalamus, using stereoelectroencephalography (SEEG) recordings. Methods: Forty habitual seizures were analyzed in nine patients with pediatric-onset neocortical drug-resistant epilepsy who underwent SEEG (age 2-25 y) with thalamic coverage. Both visual and quantitative analysis was used to evaluate ictal EEG signal in the cortex and thalamus. The amplitude and cortico-thalamic latencies of broadband frequencies at ictal onset were measured. Results: Visual analysis demonstrated consistent detection of ictal EEG changes in both the CM nucleus and AN nucleus with latency to thalamic ictal EEG changes of less than 400ms in 95% of seizures, with low-voltage fast activity being the most common ictal pattern. Quantitative broadband amplitude analysis showed consistent power changes across the frequency bands, corresponding to ictal EEG onset, while while ictal EEG latency was variable from -18.0 seconds to 13.2 seconds. There was no significant difference between detection of CM and AN ictal activity on visual or amplitude analysis. Four patients with subsequent thalamic responsive neurostimulation (RNS) demonstrated ictal EEG changes consistent with SEEG findings. Conclusions: Ictal EEG changes were consistently seen at the CM and AN of the thalamus during neocortical seizures. Significance: It may be feasible to use a closed-loop system in the thalamus to detect and modulate seizure activity for neocortical epilepsy.

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.

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.

Ictal Electroencephalographic Characteristics of Nodding Syndrome: A Comparative Case-Series from South Sudan, Tanzania, and Uganda.

Nodding syndrome (NS) is a poorly understood form of childhood-onset epilepsy that is characterized by the pathognomonic ictal phenomenon of repetitive vertical head drops. To evaluate the underlying ictal neurophysiology, ictal EEG features were evaluated in nine participants with confirmed NS from South Sudan, Tanzania, and Uganda and ictal presence of high frequency gamma oscillations on scalp EEG were assessed. Ictal EEG during the head nodding episode predominantly showed generalized slow waves or sharp-and-slow wave complexes followed by electrodecrement. Augmentation of gamma activity (30-70 Hz) was seen during the head nodding episode in all the participants. We confirm that head nodding episodes in persons with NS from the three geographically distinct regions in sub-Saharan Africa share the common features of slow waves with electrodecrement and superimposed gamma activity. ANN NEUROL 2022;92:75-80.

The consequence of financial incentives for not prescribing antibiotics: a Japan's nationwide quasi-experiment.

BACKGROUND: For addressing antibiotic overuse, Japan designed a health care policy in which eligible medical facilities could claim a financial reward when antibiotics were not prescribed for early-stage respiratory and gastrointestinal infections. The policy was introduced in a pilot manner in paediatric clinics in April 2018. METHODS: We conducted a quasi-experimental, propensity score-matched, difference-in-differences (DID) design to determine whether the nationwide financial incentives for appropriate non-prescribing of antibiotics as antimicrobial stewardship [800 JPY (≈7.3 US D) per case] were associated with changes in prescription patterns, including antibiotics, and health care use in routine paediatric health care settings at a national level. Data consisted of 9 253 261 cases of infectious diseases in 553 138 patients treated at 10 180 eligible or ineligible facilities. RESULTS: A total of 2959 eligible facilities claimed 316 770 cases for financial incentives and earned 253 million JPY (≈2.29 million USD). Compared with ineligible facilities, the introduction of financial incentives in the eligible facilities was associated with an excess reduction in antibiotic prescriptions [DID estimate, -228.6 days of therapy (DOTs) per 1000 cases (95% CI, -272.4 to -184.9), which corresponded to a relative reduction of 17.8% (95% CI, 14.8 to 20.7)]. The introduction was also associated with excess reductions in drugs for respiratory symptoms [DID estimates, -256.9 DOTs per 1000 cases (95% CI, -379.3 to -134.5)] and antihistamines [DID estimate, -198.5 DOTs per 1000 cases (95% CI, -282.1 to -114.9)]. There was no excess in out-of-hour visits [DID estimate, -4.43 events per 1000 cases (95% CI, -12.8 to 3.97)] or hospitalizations [DID estimate, -0.08 events per 1000 cases (95% CI, -0.48 to 0.31)]. CONCLUSIONS: Our findings suggest that financial incentives to medical facilities for not prescribing antibiotics were associated with reductions in prescriptions for antibiotics without adverse health care consequences. Japan's new health policy provided us with policy options for immediately reducing inappropriate antibiotic prescriptions by relatively small financial incentives.