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Background: Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study., New Method: We systematically compare how common average re-referencing and filtering CCEP data impacts quantification., Results: We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5 Hz), low cutoff low pass filters (< 200 Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise., Comparison With Existing Methods: These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy; rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods., Conclusions: We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Purpose: Rasmussen encephalitis (RE) is a very rare chronic neurological disorder of unilateral inflammation of the cerebral cortex. Hemispherotomy provides the best chance at achieving seizure freedom in RE patients, but with significant risks and variable long-term outcomes. The goal of this study is to utilize our multicenter pediatric cohort to characterize if differences in pathology and/or imaging characterization of RE may provide a window into post-operative seizure outcomes, which in turn could guide decision-making for parents and healthcare providers., Methods: This multi-institutional retrospective review of medical record, imaging, and pathology samples was approved by each individual institution's review board. Data was collected from all known pediatric cases of peri-insular functional hemispherotomy from the earliest available electronic medical records. Mean follow-up time was 4.9 years. Clinical outcomes were measured by last follow-up visit using both Engel and ILAE scoring systems. Relationships between categorical and continuous variables were analyzed with Pearson correlation values., Results: Twenty-seven patients met study criteria. No statistically significant correlations existed between patient imaging and pathology data. Pathology stage, MRI brain imaging stages, and a combined assessment of pathology and imaging stages showed no statistically significant correlation to post-operative seizure freedom rates. Hemispherectomy Outcome Prediction Scale scoring demonstrated seizure freedom in only 71% of patients receiving a score of 1 and 36% of patients receiving a score of 2 which were substantially lower than predicted., Conclusions: Our analysis did not find evidence for either independent or combined analysis of imaging and pathology staging being predictive for post peri-insular hemispherotomy seizure outcomes, prompting the need for other biomarkers to be explored. Our data stands in contrast to the recently proposed Hemispherectomy Outcome Prediction Scale and does not externally validate this metric for an RE cohort., (© 2024. The Author(s).)

The rise of adaptive stimulation approaches has shown great therapeutic promise in the growing field of neuromodulation. The discovery and growth of these novel adaptive stimulation paradigms has been largely concentrated around several implantable devices with research application programming interfaces (APIs) that allow for custom applications to be created for clinical neuromodulation studies. However, the sunsetting of devices and ongoing development of new platforms is leading to an increased fragmentation in the research environment- resulting in the reinvention of system features and the inability to leverage previous development efforts for future studies. The Open Mind Neuromodulation Interface (OMNI) is a previously proposed solution to address the weaknesses of the DLL-driven API approach of past neuromodulation research by utilizing an alternative gRPC-enabled microservice framework. Here, we introduce OMNI-BIC, an implementation of the OMNI framework to the CorTec Brain Interchange system. This paper describes the design and implementation of the OMNI-BIC software tools and demonstrates the framework's capabilities for implementing customized neuromodulation therapies for clinical investigations. Through the development and deployment of the OMNI-BIC system, we hope to improve future clinical studies with the Brain Interchange system and aid in continuing the growth and momentum of the exciting field of adaptive neuromodulation.

Focal malformations of cortical development including focal cortical dysplasia, hemimegalencephaly and megalencephaly, are a spectrum of neurodevelopmental disorders associated with brain overgrowth, cellular and architectural dysplasia, intractable epilepsy, autism and intellectual disability. Importantly, focal cortical dysplasia is the most common cause of focal intractable paediatric epilepsy. Gain and loss of function variants in the PI3K-AKT-MTOR pathway have been identified in this spectrum, with variable levels of mosaicism and tissue distribution. In this study, we performed deep molecular profiling of common PI3K-AKT-MTOR pathway variants in surgically resected tissues using droplet digital polymerase chain reaction (ddPCR), combined with analysis of key phenotype data. A total of 159 samples, including 124 brain tissue samples, were collected from 58 children with focal malformations of cortical development. We designed an ultra-sensitive and highly targeted molecular diagnostic panel using ddPCR for six mutational hotspots in three PI3K-AKT-MTOR pathway genes, namely PIK3CA (p.E542K, p.E545K, p.H1047R), AKT3 (p.E17K) and MTOR (p.S2215F, p.S2215Y). We quantified the level of mosaicism across all samples and correlated genotypes with key clinical, neuroimaging and histopathological data. Pathogenic variants were identified in 17 individuals, with an overall molecular solve rate of 29.31%. Variant allele fractions ranged from 0.14 to 22.67% across all mutation-positive samples. Our data show that pathogenic MTOR variants are mostly associated with focal cortical dysplasia, whereas pathogenic PIK3CA variants are more frequent in hemimegalencephaly. Further, the presence of one of these hotspot mutations correlated with earlier onset of epilepsy. However, levels of mosaicism did not correlate with the severity of the cortical malformation by neuroimaging or histopathology. Importantly, we could not identify these mutational hotspots in other types of surgically resected epileptic lesions (e.g. polymicrogyria or mesial temporal sclerosis) suggesting that PI3K-AKT-MTOR mutations are specifically causal in the focal cortical dysplasia-hemimegalencephaly spectrum. Finally, our data suggest that ultra-sensitive molecular profiling of the most common PI3K-AKT-MTOR mutations by targeted sequencing droplet digital polymerase chain reaction is an effective molecular approach for these disorders with a good diagnostic yield when paired with neuroimaging and histopathology., (© The Author(s) (2022). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Research with human intracranial electrodes has traditionally been constrained by the limitations of the inpatient clinical setting. Immersive virtual reality (VR), however, can transcend setting and enable novel task design with precise control over visual and auditory stimuli. This control over visual and auditory feedback makes VR an exciting platform for new in-patient, human electrocorticography (ECOG) and stereo-electroencephalography (sEEG) research. The integration of intracranial electrode recording and stimulation with VR task dynamics required foundational systems engineering. In this work, we present a custom API that bridges Unity, the leading VR game development engine, and Synapse, the proprietary software of the Tucker Davis Technologies (TDT) neural recording and stimulation platform. To demonstrate the functionality and efficiency of our API, we developed a closed-loop brain-computer interface (BCI) task in which filtered neural signals controlled the movement of a virtual object and virtual object dynamics triggered neural stimulation. This closed-loop VR-BCI task confirmed the utility, safety, and efficacy of our API and its readiness for human task deployment.

Objective: To develop and validate a model to predict seizure freedom in children undergoing cerebral hemispheric surgery for the treatment of drug-resistant epilepsy., Methods: We analyzed 1267 hemispheric surgeries performed in pediatric participants across 32 centers and 12 countries to identify predictors of seizure freedom at 3 months after surgery. A multivariate logistic regression model was developed based on 70% of the dataset (training set) and validated on 30% of the dataset (validation set). Missing data were handled using multiple imputation techniques., Results: Overall, 817 of 1237 (66%) hemispheric surgeries led to seizure freedom (median follow-up = 24 months), and 1050 of 1237 (85%) were seizure-free at 12 months after surgery. A simple regression model containing age at seizure onset, presence of generalized seizure semiology, presence of contralateral 18-fluoro-2-deoxyglucose-positron emission tomography hypometabolism, etiologic substrate, and previous nonhemispheric resective surgery is predictive of seizure freedom (area under the curve = .72). A Hemispheric Surgery Outcome Prediction Scale (HOPS) score was devised that can be used to predict seizure freedom., Significance: Children most likely to benefit from hemispheric surgery can be selected and counseled through the implementation of a scale derived from a multiple regression model. Importantly, children who are unlikely to experience seizure control can be spared from the complications and deficits associated with this surgery. The HOPS score is likely to help physicians in clinical decision-making., (© 2021 International League Against Epilepsy.)

Because hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels modulate the excitability of cortical and hippocampal principal neurons, these channels play a key role in the hyperexcitability that occurs during the development of epilepsy after a brain insult, or epileptogenesis. In epileptic rats generated by pilocarpine-induced status epilepticus, HCN channel activity is downregulated by two main mechanisms: a hyperpolarizing shift in gating and a decrease in amplitude of the current mediated by HCN channels, I h . Because these mechanisms are modulated by various phosphorylation signaling pathways, we hypothesized that phosphorylation changes occur at individual HCN channel amino acid residues (phosphosites) during epileptogenesis. We collected CA1 hippocampal tissue from male Sprague Dawley rats made epileptic by pilocarpine-induced status epilepticus, and age-matched naïve controls. We also included resected human brain tissue containing epileptogenic zones (EZs) where seizures arise for comparison to our chronically epileptic rats. After enrichment for HCN1 and HCN2 isoforms by immunoprecipitation and trypsin in-gel digestion, the samples were analyzed by mass spectrometry. We identified numerous phosphosites from HCN1 and HCN2 channels, representing a novel survey of phosphorylation sites within HCN channels. We found high levels of HCN channel phosphosite homology between humans and rats. We also identified a novel HCN1 channel phosphosite S791, which underwent significantly increased phosphorylation during the chronic epilepsy stage. Heterologous expression of a phosphomimetic mutant, S791D, replicated a hyperpolarizing shift in I h gating seen in neurons from chronically epileptic rats. These results show that HCN1 channel phosphorylation is altered in epilepsy and may be of pathogenic importance., (Copyright © 2021 IBRO. Published by Elsevier Ltd. All rights reserved.)

Studying the neural correlates of sleep can lead to revelations in our understanding of sleep and its interplay with different neurological disorders. Sleep research relies on manual annotation of sleep stages based on rules developed for healthy adults. Automating sleep stage annotation can expedite sleep research and enable us to better understand atypical sleep patterns. Our goal was to create a fully unsupervised approach to label sleep and wake states in human electro-corticography (ECoG) data from epilepsy patients. Here, we demonstrate that with continuous data from a single ECoG electrode, hidden semi-Markov models (HSMM) perform best in classifying sleep/wake states without excessive transitions, with a mean accuracy (n=4) of 85.2% compared to using K-means clustering (72.2%) and hidden Markov models (81.5%). Our results confirm that HSMMs produce meaningful labels for ECoG data and establish the groundwork to apply this model to cluster sleep stages and potentially other behavioral states.