Alcohol for seizure induction in the epilepsy monitoring unit.

RATIONALE: Seizure induction techniques are used in the epilepsy monitoring unit (EMU) to increase diagnostic yield and reduce length of stay. There are insufficient data on the efficacy of alcohol as an induction technique. METHODS: We performed a retrospective cohort study using six years of EMU data at our institution. We compared cases who received alcohol for seizure induction to matched controls who did not. The groups were matched on the following variables: age, reason for admission, length of stay, number of antiseizure medications (ASM) at admission, whether ASMs were tapered during admission, and presence of interictal epileptiform discharges. We used both propensity score and exact matching strategies. We compared the likelihood of epileptic seizures and nonepileptic events in cases versus controls using Kaplan-Meier time-to-event analysis, as well as odds ratios for these outcomes occurring at any time during the admission. RESULTS: We analyzed 256 cases who received alcohol (median dose 2.5 standard drinks) and 256 propensity score-matched controls. Cases who received alcohol were no more likely than controls to have an epileptic seizure (X2(1) = 0.01, p = 0.93) or nonepileptic event (X2(1) = 2.1, p = 0.14) in the first 48 h after alcohol administration. For the admission overall, cases were no more likely to have an epileptic seizure (OR 0.89, 95 % CI 0.61-1.28, p = 0.58), nonepileptic event (OR 0.97, CI 0.62-1.53, p = 1.00), nor require rescue benzodiazepine (OR 0.63, CI 0.35-1.12, p = 0.15). Stratified analyses revealed no increased risk of epileptic seizure in any subgroups. Sensitivity analysis using exact matching showed that results were robust to matching strategy. CONCLUSIONS: Alcohol was not an effective induction technique in the EMU. This finding has implications for counseling patients with epilepsy about the risks of drinking alcohol in moderation in their daily lives.

Clinical characteristics and surgical outcomes of epilepsy associated with temporal encephalocele: A systematic review.

Temporal encephaloceles (TE) are an under-identified, potentially intervenable cause of epilepsy. This systematic review consolidates the current data to identify the major clinical, neuroimaging, and EEG features and surgical outcomes of epilepsy associated with TE. Literature searches were carried out using MEDLINE, Embase, PsycINFO, Scopus, and Cochrane Library databases from inception to December 7, 2023. Studies were included if they described clinical, neuroimaging, EEG, or surgical data in ≥5 patients with TE and epilepsy. Of 562 studies identified in the search, 24 met the eligibility criteria, reporting 423 unique patients with both epilepsy and TE. Compared to epilepsy patients without TE, those with TE had a higher mean age of seizure onset and were less likely to have a history of febrile seizures. Seizure semiologies were variable, but primarily mirrored temporal lobe onset patterns. Epilepsy patients with TE had a higher likelihood of having clinical or radiographic features of idiopathic intracranial hypertension (IIH) than those without. Brain MRI may show ipsilateral mesial temporal sclerosis (16 %). CT scans of the skull base usually revealed bony defects near the TE (90 %). Brain PET scans primarily showed ipsilateral temporal lobe hypometabolism (80 %), mostly in the anterior temporal lobe (67 %). Scalp EEG mostly lateralized ipsilateral to the implicated TE (92 % seizure onset) and localized to the temporal lobe (96 %). Intracranial EEG revealed seizure onset near the TE (11 of 12 cases including TE-adjacent electrodes) with variable timing of spread to the ipsilateral hippocampus. After surgical treatment of the TE, the rate of Engel I or ILAE 1 outcomes at one year was 75 % for lesionectomy, 85 % for anterior temporal lobectomy (ATL), and 80 % for ATL with amygdalohippocampectomy. Further studies are needed to better elucidate the relationship between IIH, TE, and epilepsy, improve the identification of TE, and optimize surgical interventions.

Passive and active markers of cortical excitability in epilepsy.

Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.

Quantifying trial-by-trial variability during cortico-cortical evoked potential mapping of epileptogenic tissue.

OBJECTIVE: Measuring cortico-cortical evoked potentials (CCEPs) is a promising tool for mapping epileptic networks, but it is not known how variability in brain state and stimulation technique might impact the use of CCEPs for epilepsy localization. We test the hypotheses that (1) CCEPs demonstrate systematic variability across trials and (2) CCEP amplitudes depend on the timing of stimulation with respect to endogenous, low-frequency oscillations. METHODS: We studied 11 patients who underwent CCEP mapping after stereo-electroencephalography electrode implantation for surgical evaluation of drug-resistant epilepsy. Evoked potentials were measured from all electrodes after each pulse of a 30 s, 1 Hz bipolar stimulation train. We quantified monotonic trends, phase dependence, and standard deviation (SD) of N1 (15-50 ms post-stimulation) and N2 (50-300 ms post-stimulation) amplitudes across the 30 stimulation trials for each patient. We used linear regression to quantify the relationship between measures of CCEP variability and the clinical seizure-onset zone (SOZ) or interictal spike rates. RESULTS: We found that N1 and N2 waveforms exhibited both positive and negative monotonic trends in amplitude across trials. SOZ electrodes and electrodes with high interictal spike rates had lower N1 and N2 amplitudes with higher SD across trials. Monotonic trends of N1 and N2 amplitude were more positive when stimulating from an area with higher interictal spike rate. We also found intermittent synchronization of trial-level N1 amplitude with low-frequency phase in the hippocampus, which did not localize the SOZ. SIGNIFICANCE: These findings suggest that standard approaches for CCEP mapping, which involve computing a trial-averaged response over a .2-1 Hz stimulation train, may be masking inter-trial variability that localizes to epileptogenic tissue. We also found that CCEP N1 amplitudes synchronize with ongoing low-frequency oscillations in the hippocampus. Further targeted experiments are needed to determine whether phase-locked stimulation could have a role in localizing epileptogenic tissue.

Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy.

OBJECTIVE: Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ). METHODS: We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates. RESULTS: We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve = .83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep. SIGNIFICANCE: The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.

A pharmacokinetic model of antiseizure medication load to guide care in the epilepsy monitoring unit.

OBJECTIVE: Evaluating patients with drug-resistant epilepsy often requires inducing seizures by tapering antiseizure medications (ASMs) in the epilepsy monitoring unit (EMU). The relationship between ASM taper strategy, seizure timing, and severity remains unclear. In this study, we developed and validated a pharmacokinetic model of total ASM load and tested its association with seizure occurrence and severity in the EMU. METHODS: We studied 80 patients who underwent intracranial electroencephalographic recording for epilepsy surgery planning. We developed a first order pharmacokinetic model of the ASMs administered in the EMU to generate a continuous metric of overall ASM load. We then related modeled ASM load to seizure likelihood and severity. We determined the association between the rate of ASM load reduction, the length of hospital stay, and the probability of having a severe seizure. Finally, we used modeled ASM load to predict oncoming seizures. RESULTS: Seizures occurred in the bottom 50th percentile of sampled ASM loads across the cohort (p < .0001, Wilcoxon signed-rank test), and seizures requiring rescue therapy occurred at lower ASM loads than seizures that did not require rescue therapy (logistic regression mixed effects model, odds ratio = .27, p = .01). Greater ASM decrease early in the EMU was not associated with an increased likelihood of having a severe seizure, nor with a shorter length of stay. SIGNIFICANCE: A pharmacokinetic model can accurately estimate ASM levels for patients in the EMU. Lower modeled ASM levels are associated with increased seizure likelihood and seizure severity. We show that ASM load, rather than ASM taper speed, is associated with severe seizures. ASM modeling has the potential to help optimize taper strategy to minimize severe seizures while maximizing diagnostic yield.

Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes.

OBJECTIVE: Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recently developed and validated novel natural language processing (NLP) algorithms to automatically extract key epilepsy outcome measures from clinic notes. In this study, we assessed the feasibility of extracting these measures to study the natural history of epilepsy at our center. METHODS: We applied our previously validated NLP algorithms to extract seizure freedom, seizure frequency, and date of most recent seizure from outpatient visits at our epilepsy center from 2010 to 2022. We examined the dynamics of seizure outcomes over time using Markov model-based probability and Kaplan-Meier analyses. RESULTS: Performance of our algorithms on classifying seizure freedom was comparable to that of human reviewers (algorithm F1 = .88 vs. human annotator κ = .86). We extracted seizure outcome data from 55 630 clinic notes from 9510 unique patients written by 53 unique authors. Of these, 30% were classified as seizure-free since the last visit, 48% of non-seizure-free visits contained a quantifiable seizure frequency, and 47% of all visits contained the date of most recent seizure occurrence. Among patients with at least five visits, the probabilities of seizure freedom at the next visit ranged from 12% to 80% in patients having seizures or seizure-free at the prior three visits, respectively. Only 25% of patients who were seizure-free for 6 months remained seizure-free after 10 years. SIGNIFICANCE: Our findings demonstrate that epilepsy outcome measures can be extracted accurately from unstructured clinical note text using NLP. At our tertiary center, the disease course often followed a remitting and relapsing pattern. This method represents a powerful new tool for clinical research with many potential uses and extensions to other clinical questions.

Normative intracranial EEG maps epileptogenic tissues in focal epilepsy.

Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

Spatial distribution of interictal spikes fluctuates over time and localizes seizure onset.

The location of interictal spikes is used to aid surgical planning in patients with medically refractory epilepsy; however, their spatial and temporal dynamics are poorly understood. In this study, we analysed the spatial distribution of interictal spikes over time in 20 adult and paediatric patients (12 females, mean age = 34.5 years, range = 5-58) who underwent intracranial EEG evaluation for epilepsy surgery. Interictal spikes were detected in the 24 h surrounding each seizure and spikes were clustered based on spatial location. The temporal dynamics of spike spatial distribution were calculated for each patient and the effects of sleep and seizures on these dynamics were evaluated. Finally, spike location was assessed in relation to seizure onset location. We found that spike spatial distribution fluctuated significantly over time in 14/20 patients (with a significant aggregate effect across patients, Fisher's method: P < 0.001). A median of 12 sequential hours were required to capture 80% of the variability in spike spatial distribution. Sleep and postictal state affected the spike spatial distribution in 8/20 and 4/20 patients, respectively, with a significant aggregate effect (Fisher's method: P < 0.001 for each). There was no evidence of pre-ictal change in the spike spatial distribution for any patient or in aggregate (Fisher's method: P = 0.99). The electrode with the highest spike frequency and the electrode with the largest area of downstream spike propagation both localized the seizure onset zone better than predicted by chance (Wilcoxon signed-rank test: P = 0.005 and P = 0.002, respectively). In conclusion, spikes localize seizure onset. However, temporal fluctuations in spike spatial distribution, particularly in relation to sleep and post-ictal state, can confound localization. An adequate duration of intracranial recording-ideally at least 12 sequential hours-capturing both sleep and wakefulness should be obtained to sufficiently sample the interictal network.

Virtual resection predicts surgical outcome for drug-resistant epilepsy.

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.

Reproducibility of interictal spike propagation in children with refractory epilepsy.

OBJECTIVE: Interictal spikes are a characteristic feature of invasive electroencephalography (EEG) recordings in children with refractory epilepsy. Spikes frequently co-occur across multiple brain regions with discernable latencies, suggesting that spikes can propagate through distributed neural networks. The purpose of this study was to examine the long-term reproducibility of spike propagation patterns over hours to days of interictal recording. METHODS: Twelve children (mean age 13.1 years) were retrospectively studied. A mean ± standard deviation (SD) of 47.2 ± 40.1 hours of interictal EEG recordings were examined per patient (range 17.5-166.5 hours). Interictal recordings were divided into 30-minute segments. Networks were extracted based on the frequency of spike coactivation between pairs of electrodes. For each 30-minute segment, electrodes were assigned a "Degree Preference (DP)" based on the tendency to appear upstream or downstream within propagation sequences. The consistency of DPs across segments ("DP-Stability") was quantified using the Spearman rank correlation. RESULTS: Regions exhibited highly stable preferences to appear upstream, intermediate, or downstream in spike propagation sequences. Across networks, the mean ± SD DP-Stability was 0.88 ± 0.07, indicating that propagation patterns observed in 30-minute segments were representative of the patterns observed in the full interictal window. At the group level, regions involved in seizure generation appeared more upstream in spike propagation sequences. SIGNIFICANCE: Interictal spike propagation is a highly reproducible output of epileptic networks. These findings shed new light on the spatiotemporal dynamics that may constrain the network mechanisms of refractory epilepsy.

Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care.

Atlas-Independent, Electrophysiological Mapping of the Optimal Locus of Subthalamic Deep Brain Stimulation for the Motor Symptoms of Parkinson Disease.

BACKGROUND/AIMS: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor symptoms of Parkinson disease (PD). However, motor outcomes can be variable, perhaps due to inconsistent positioning of the active contact relative to an unknown optimal locus of stimulation. Here, we determine the optimal locus of STN stimulation in a geometrically unconstrained, mathematically precise, and atlas-independent manner, using Unified Parkinson Disease Rating Scale (UPDRS) motor outcomes and an electrophysiological neuronal stimulation model. METHODS: In 20 patients with PD, we mapped motor improvement to active electrode location, relative to the individual, directly MRI-visualized STN. Our analysis included a novel, unconstrained and computational electrical-field model of neuronal activation to estimate the optimal locus of DBS. RESULTS: We mapped the optimal locus to a tightly defined ovoid region 0.49 mm lateral, 0.88 mm posterior, and 2.63 mm dorsal to the anatomical midpoint of the STN. On average, this locus is 11.75 lateral, 1.84 mm posterior, and 1.08 mm ventral to the mid-commissural point. CONCLUSION: Our novel, atlas-independent method reveals a single, ovoid optimal locus of stimulation in STN DBS for PD. The methodology, here applied to UPDRS and PD, is generalizable to atlas-independent mapping of other motor and non-motor effects of DBS.

The sixth sense: how much does interictal intracranial EEG add to determining the focality of epileptic networks?

Intracranial EEG is used for two main purposes: to determine (i) if epileptic networks are amenable to focal treatment and (ii) where to intervene. Currently, these questions are answered qualitatively and differently across centres. There is a need to quantify the focality of epileptic networks systematically, which may guide surgical decision-making, enable large-scale data analysis and facilitate multi-centre prospective clinical trials. We analysed interictal data from 101 patients with drug-resistant epilepsy who underwent pre-surgical evaluation with intracranial EEG at a single centre. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. Sixty-five patients had unifocal seizure onset on intracranial EEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal intracranial EEG abnormalities for each patient. We compared these measures against the '5 Sense Score,' a pre-implant prediction of the likelihood of focal seizure onset, assessed the ability to predict unifocal seizure onset by combining these metrics and evaluated how predicted focality relates to subsequent treatment and outcomes. The spatial dispersion of intracranial EEG electrodes predicted network focality with similar performance to the 5-SENSE score [area under the receiver operating characteristic curve = 0.68 (95% confidence interval 0.57, 0.78)], indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5-SENSE score and the spatial dispersion of interictal intracranial EEG abnormalities significantly improved this prediction [area under the receiver operating characteristic curve = 0.79 (95% confidence interval 0.70, 0.88); P < 0.05]. Predictions from this combined model differed between surgical- from device-treated patients with an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.68, 0.85) and between patients with good and poor post-surgical outcome at 2 years with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval 0.56, 0.85). Spatial measures of interictal intracranial EEG abnormality significantly improved upon pre-implant predictions of network focality by area under the receiver operating characteristic curve and increased sensitivity in a single-centre study. Quantified focality predictions related to ultimate treatment strategy and surgical outcomes. While the 5-SENSE score weighed for specificity in their multi-centre validation to prevent unnecessary implantation, sensitivity improvement found in our single-centre study by including intracranial EEG may aid the decision on whom to perform the focal intervention. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.

Quantitative EEG Spectral Features Differentiate Genetic Epilepsies and Predict Neurologic Outcomes.

EEG plays an integral part in the diagnosis and management of children with genetic epilepsies. Nevertheless, how quantitative EEG features differ between genetic epilepsies and neurological outcomes remains largely unknown. Here, we aimed to identify quantitative EEG biomarkers in children with epilepsy and a genetic diagnosis in STXBP1, SCN1A, or SYNGAP1, and to assess how quantitative EEG features associate with neurological outcomes in genetic epilepsies more broadly. We analyzed individuals with pathogenic variants in STXBP1 (95 EEGs, n=20), SCN1A (154 EEGs, n=68), and SYNGAP1 (46 EEGs, n=21) and a control cohort of individuals without epilepsy or known cerebral disease (847 EEGs, n=806). After removing artifacts and epochs with excess noise or altered state from EEGs, we extracted spectral features. We validated our preprocessing pipeline by comparing automatically-detected posterior dominant rhythm (PDR) to annotations from clinical EEG reports. Next, as a coarse measure of pathological slowing, we compared the alpha-delta bandpower ratio between controls and the different genetic epilepsies. We then trained random forest models to predict a diagnosis of STXBP1, SCN1A, and SYNGAP1. Finally, to understand how EEG features vary with neurological outcomes, we trained random forest models to predict seizure frequency and motor function. There was strong agreement between the automatically-calculated PDR and clinical EEG reports (R 2=0.75). Individuals with STXBP1-related epilepsy have a significantly lower alpha-delta ratio than controls (P<0.001) across all age groups. Additionally, individuals with a missense variant in STXBP1 have a significantly lower alpha-delta ratio than those with a protein-truncating variant in toddlers (P<0.001), children (P=0.02), and adults (P<0.001). Models accurately predicted a diagnosis of STXBP1 (AUC=0.91), SYNGAP1 (AUC=0.82), and SCN1A (AUC=0.86) against controls and from each other in a three-class model (accuracy=0.74). From these models, we isolated highly correlated biomarkers for these respective genetic disorders, including alpha-theta ratio in frontal, occipital, and parietal electrodes with STXBP1, SYNGAP1, and SCN1A, respectively. Models were unable to predict seizure frequency (AUC=0.53). Random forest models predicted motor scores significantly better than age-based null models (P<0.001), suggesting spectral features contain information pertinent to gross motor function. In summary, we demonstrate that STXBP1-, SYNGAP1-, and SCN1A-related epilepsies have distinct quantitative EEG signatures. Furthermore, EEG spectral features are predictive of some functional outcome measures in patients with genetic epilepsies. Large-scale retrospective quantitative analysis of clinical EEG has the potential to discover novel biomarkers and to quantify and track individuals' disease progression across development.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Sidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy.

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data are captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-centre study for model development; two-centre study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using several interictal EEG features derived from recent publications. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. Forty-seven patients (30 female; ages 20-69; 20 left-sided, 10 right-sided and 17 bilateral seizure onsets) were analysed for model development and internal validation. Nineteen patients (10 female; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analysed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. We replicated the finding that right temporal lobe epilepsy was harder to distinguish in a separate modality of resting-state functional MRI. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome. A potential clinical application of these findings could be to either support or oppose a hypothesis of unilateral temporal lobe epilepsy when deciding to pursue surgical resection or ablation as opposed to device implantation.

Quantitative artifact reduction and pharmacologic paralysis improve detection of EEG epileptiform activity in critically ill patients.

OBJECTIVE: Epileptiform activity is common in critically ill patients, but movement-related artifacts-including electromyography (EMG) and myoclonus-can obscure EEG, limiting detection of epileptiform activity. We sought to determine the ability of pharmacologic paralysis and quantitative artifact reduction (AR) to improve epileptiform discharge detection. METHODS: Retrospective analysis of patients who underwent continuous EEG monitoring with pharmacologic paralysis. Four reviewers read each patient's EEG pre- and post- both paralysis and AR, and indicated the presence of epileptiform discharges. We compared the interrater reliability (IRR) of identifying discharges at baseline, post-AR, and post-paralysis, and compared the performance of AR and paralysis according to artifact type. RESULTS: IRR of identifying epileptiform discharges at baseline was slight (N = 30; κ = 0.10) with a trend toward increase post-AR (κ = 0.26, p = 0.053) and a significant increase post-paralysis (κ = 0.51, p = 0.001). AR was as effective as paralysis at improving IRR of identifying discharges in those with high EMG artifact (N = 15; post-AR κ = 0.63, p = 0.009; post-paralysis κ = 0.62, p = 0.006) but not with primarily myoclonus artifact (N = 15). CONCLUSIONS: Paralysis improves detection of epileptiform activity in critically ill patients when movement-related artifact obscures EEG features. AR improves detection as much as paralysis when EMG artifact is high, but is ineffective when the primary source of artifact is myoclonus. SIGNIFICANCE: In the appropriate setting, both AR and paralysis facilitate identification of epileptiform activity in critically ill patients.

Resting-state background features demonstrate multidien cycles in long-term EEG device recordings.

Background: Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. Objective/Hypothesis: We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may disrupt these cycles. Methods: We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. Results: Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56 - 0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). Conclusions: Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may disrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.

The seizure severity score: a quantitative tool for comparing seizures and their response to therapy.

Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.