Objective.High frequency oscillations (HFOs) are a promising biomarker of epilepsy, but automated detectors have significant risk for false positives due to diverse EEG artifacts. Many of these artifacts, previously uncharacterized in HFO research, are readily visible to clinicians under standard EEG viewing conditions. We present an artifact detector trained on clinician markings that identify when HFOs were produced by artifacts.Approach.Clinicians read standard resolution (10 sec per screen, all channels visible, 0-30 Hz), intracranial EEG with 8000 HFO events marked in 35 patients. They labeled each event as brain-derived or artifactual based upon their expert interpretation of the EEG at the time of the event, creating a new gold standard of HFO labeling. We used 4000 events for training/validation and 4000 for held-out prospective testing. We extracted features at the time of the HFOs from the single intracranial HFO channel and the scalp and intracranial common average reference, then trained candidate supervised learning classifiers to distinguish artifacts and non-artifactual HFOs (naHFOs).Main results.The resulting Michigan Intracranial Artifact Filter (MIAF) uses binary logistic regression on just intracranial data at the time of the HFO detection to remove false positives. The MIAF applied on held-out patient data significantly increased positive predictive value from 86% to 98% and resulted in an area under the precision recall curve and receiver operating characteristic curve of 99% and 92% respectively. It improved the correlation between HFOs and the seizure onset zone and resected volume in 76.5% and 88.9% of patients respectively, outperformed alternative artifact detectors, and allowed HFO analysis from all states of vigilance.Significance.MIAF effectively removes false positives HFO detections while retaining sufficient naHFOs for downstream analysis. Because it relies only on raw intracranial data during HFO detections, it can be easily ported to other HFO detectors and recording environments.

Obstructive sleep apnea (OSA) severity varies by sleep position and sex; however, it is unknown whether these factors affect hypoglossal nerve stimulation (HGNS) treatment effectiveness. Thus, this study evaluates HGNS control of OSA by sleep position and sex. To enhance real-world clinical applicability of results and address concerns regarding appropriate postoperative sleep study criteria to evaluate HGNS outcomes, our study also proposes and utilizes a standardized HGNS titration study scoring protocol to assess apnea-hypopnea index (AHI). A retrospective cohort of adult patients underwent device implantation at a tertiary care center between January 2018 and June 2021. Outcome measures included response rate by sleep position and sex using Sher criteria (reduction of AHI ≥50% to < 20/hr) and AHI normalization (<5/hr). Of 181 patients implanted, 113 patients met inclusion criteria. Patients were primarily older (median 65.0 years), overweight (BMI 30.0), white (91.2%) males (67.3%) with severe OSA (AHI 32.0/hr). HGNS success by Sher criteria and AHI normalization were 77.0% and 54.1%, respectively, in lateral sleep compared to 52.9% and 23.5% in supine sleep. Females attained a greater supine AHI reduction (p < 0.04, Wilcoxon rank-sum test) and success by both response criteria (both p < 0.03, chi-square tests of independence). Subgroup analyses using fixed-voltage home sleep apnea tests were underpowered and limited by low counts. Thus, HGNS more effectively controlled OSA in lateral sleep and for females, suggesting clinically meaningful differences by sleep position and sex that may help inform clinical decision making for patients with similar demographics.

Background and Objectives High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic tissue, yet the extent to which recording duration and vigilance state influence their spatial distributions remains unclear. This study quantified the recording duration and vigilance state required to reliably capture HFO spatial distributions to guide surgical planning in epilepsy.Methods We retrospectively analyzed long-term, continuous iEEG recordings from patients with drug-resistant epilepsy undergoing presurgical evaluation at the University of Michigan. Sleep stages were manually annotated, HFOs (80-500 Hz) were detected using a validated algorithm, and the correlations between HFOs and the seizure-onset zone (SOZ) were assessed across vigilance states. A novel similarity-based temporal padding approach was developed to measure the similarity of HFO distributions derived from data available up to a given time point with those derived from the full recording. Postsurgical outcome prediction was evaluated using a decision tree classifier based on the proportion of resected, top-ranked HFO-rate channels (critical resection percentage, CReP).Results Fifty-four patients were analyzed (30 female patients, mean age 32.8 [range, 6-66] years, mean recording duration 8.40 days [range, 3-22]). HFO-SOZ associations were present across all states but fluctuated over time; non-rapid eye movement (NREM) sleep showed the least temporal variability. HFO-SOZ associations were stronger in temporal lobe epilepsy and more stable in patients with frequent seizures. When analyzing only the HFOs during NREM, 21% of patients required more than 2 days of recording to capture its full distribution; however, all NREM data were insufficient to describe the full HFO distribution in 30% of patients. Across all vigilance states, 7 days of recording fully characterized HFO distributions in 98% of patients. Postsurgical outcome prediction using multiday aggregate CReP achieved more robust and accurate performance (AUC 0.86, 95% CI [0.70-1.00]) than analyses based on a random selection of single 5-minute NREM epoch (median AUC 0.42, IQR 0.23) or a single day (AUCs ranged 0.63-0.71 for different analyzed days).Discussion Short sampling period risks incomplete representation of the full HFO spatial profile. Future studies should consider multiday recordings to enable more reliable HFO characterization and improve HFO-based surgical outcome prediction.

BACKGROUND:In Parkinson's disease (PD), sleep-related oscillatory dynamics between basal ganglia and cortical nodes may inform sleep dysfunction. The subthalamic nucleus (STN) is highly interconnected with human sleep circuitry and is a current target of interest for sleep neuromodulation. METHODS:To investigate correlated cortical-subcortical activity, we recorded simultaneous PSG and STN local field potentials (LFP) in 19 patients undergoing deep brain stimulation (DBS) for PD. We evaluated the correlation in spectral power between STN-LFP and EEG during polysomnogram (PSG)-defined stages of sleep. In addition, we analyzed aperiodic 1/f spectral parameters (exponent and offset), which have been identified as markers of arousal across sleep stages. Finally, we trained classifiers on aperiodic features to test whether 1/f structure alone could recover PSG-defined sleep stages from EEG and STN-LFP. RESULTS:Results revealed a significant interaction between recording modality and sleep stage. During wakefulness and REM sleep, STN-LFP exhibited a significantly steeper spectral slope compared to scalp EEG. However, this dissociation diminished with sleep depth; slopes appeared more similar during N2 and reversed during N3, where EEG became steeper than LFP. Across subjects, the aperiodic component showed robust state dependence and modality differences with STN-LFP maintaining higher exponents overall. STN-LFP aperiodic features alone classified sleep stages above chance and outperformed scalp EEG, approaching the combined-modality ceiling. CONCLUSION:These findings highlight distinct subcortical electrophysiological signatures in sleep-dependent regulation of neural arousal across brain networks in PD. These insights offer potential biomarkers for both invasive and non-invasive forms of closed-loop neuromodulation strategies targeting sleep dysfunction. TRIAL REGISTRATION:ClinicalTrials.gov identifier: NCT04620551.

The fourth-order time-invariant spectrum, or trispectrum, has a simple derivation as the cross-spectrum among frequency bands in the Wigner-Ville distribution (WVD). Viewed this way, the trispectrum gains intuitive meaning as a measure of the linear dependence of power across frequencies, which yields some insight into its structure and interpretation. We highlight, in particular, a two-dimensional subdomain as useful for identifying modulated oscillations when the modulating envelope is non-negative or lowpass. Spectral characteristics of the carrier and modulating signals are revealed along separate axes of a two dimensional representation of this domain. The application of this framework, combined with a previously described additive de composition technique for higher-order spectra, is demonstrated by the blind identification and separation of sleep spindles and beta bursts in EEG.

In drug-resistant focal epilepsy, planning surgical resection can involve presurgical intracranial EEG (iEEG) recordings to detect seizures and other iEEG patterns to improve postsurgical seizure outcome. We hypothesized that resection of tissue generating interictal high-frequency oscillations (HFOs, 80-500 Hz) in the iEEG predicts surgical outcome.In eight international epilepsy centres, iEEG was recorded during the presurgical evaluation of patients. The patients were of all ages, had epilepsy of all types, and underwent surgical resection of a single focus aiming at seizure freedom. In a prospective analysis, we applied a fully automated definition of HFO that was independent of the dataset. Using an observational cohort design that was blinded to postsurgical seizure outcome, we analysed HFO rates during non-rapid-eye-movement sleep. If channels had consistently high rates over multiple epochs, they were labelled the 'HFO area'. After HFO analysis, centres provided the electrode contacts located in the resected volume and the seizure outcome at follow-up >= 24 months after surgery. The study was registered at www.clinicaltrials.gov (NCT05332990).We received 160 iEEG datasets. In 146 datasets (91%), the HFO area could be defined. The patients with a completely resected HFO area were more likely to achieve seizure freedom in comparison to those without [odds ratio 2.61, 95% confidence interval (CI) 1.15-5.91, P = 0.02]. Among seizure-free patients, the HFO area was completely resected in 31 and not completely resected in 43. Among patients with recurrent seizures, the HFO area was completely resected in 14 and not completely resected in 58. When predicting seizure freedom, the negative predictive value of the HFO area (68%, CI 52-81) was higher than that for the resected volume as a predictor by itself (51%, CI 42-59, P = 4 x 10-5). The sensitivity and specificity for complete HFO area resection were 0.88 (CI 0.72-0.98) and 0.39 (CI 0.25-0.54), respectively, and the area under the curve was 0.83 (CI 0.58-0.97), indicating good predictive performance.In a blinded cohort study from independent epilepsy centres, applying a previously validated algorithm for HFO marking without the need for adjusting to new datasets allowed us to validate the clinical relevance of HFOs to plan the surgical resection. Can postsurgical seizure outcomes in epilepsy patients be improved? In an observational study based on prospective blinded analysis of intracranial recordings, Dimakopoulos et al. found that removal of the area of brain tissue generating high frequency oscillations was associated with higher rates of seizure freedom.

Objective.Proper identification of eloquent cortices is essential to minimize post-surgical deficits in patients undergoing resection for epilepsy and tumors. Current methods are subjective, vary across centers, and require significant expertise, underscoring the need for more objective pre-surgical mapping. Phase-amplitude coupling (PAC), the interaction between the phase of low-frequency oscillations and the amplitude of high-frequency activity, has been implicated in task-induced brain activity and may serve as a biomarker for functional mapping. Our objective was to develop a novel PAC-based algorithm to non-invasively identify somatosensory eloquent cortex using magnetoencephalography (MEG) data in epilepsy patients.Approach.We analyzed somatosensory and spontaneous MEG recordings from 30 subjects with drug-resistant epilepsy. PAC was calculated on source-reconstructed data (5-12 Hz for low frequencies and 30-300 Hz for high frequencies), followed by rank-2 tensor decomposition. Density-based clustering compared active brain regions during somatosensory task and spontaneous data at a population level. We employed a linear mixed-effects model to quantify changes in PAC between somatosensory and resting-state data. We developed a patient-specific support vector machine (SVM) classifier to identify active brain regions based on PAC values during the somatosensory task.Main results.Five of six expected brain regions were active during left and right-sided stimulation (p=1.08×10-8, hypergeometric probability test). The mixed-effects model confirmed task-specific PAC in anatomically relevant brain regions (p < 0.01). The SVM classifier gave a specificity of 99.46% and a precision of 66.9%. These results demonstrate that the PAC algorithm reliably identifies somatosensory cortex activation at both individual and population levels with statistical significance.Significance.This study demonstrates the feasibility of using PAC as a non-invasive marker for identifying functionally relevant brain regions during somatosensory task in epilepsy patients. Future work will evaluate its applicability for mapping other eloquent cortices, including language, motor, and auditory areas.

While deep brain stimulation (DBS) effectively treats motor symptoms of Parkinson's Disease, the advent of closed-loop adaptive DBS (aDBS) provides an opportunity to dynamically adjust stimulation settings to optimize non-motor symptoms such as sleep. In a parallel study, we hypothesized that unintended over-stimulation during sleep using conventional DBS may interfere with sleep quality. The objective of this paper is to develop and prospectively validate an intracranial electrophysiology-based forecaster of awakenings integrated into an aDBS system, preparatory to testing this hypothesis. We developed and trained an algorithm using multiple nights of inhome concurrent subthalamic nucleus (STN) local field potential (LFP) recordings with sleep scoring from an EEG-based wearable device (N=2 subjects). We prospectively deployed the algorithm live in N=4 novel subjects (52 total nights). Our algorithm performed better than chance (p<0.001, permutation test) in N=3 subjects for forecasting awakenings, i.e., the first epoch of each wake after sleep onset (WASO) bout ($p=4 \times 10^{-9}$, binomial distribution), and in N=4 subjects for identifying all WASO epochs ($p=10^{-12}$, binomial distribution), missing an average of 26.9% of awakenings and 10.8% of WASO epochs, with warnings occurring an average of 2.6 minutes prior to awakenings. We thus conclude awakenings can be reliably forecast using STN LFP.

BACKGROUND:While open-loop deep brain stimulation (DBS) is an effective therapy for the motor symptoms of Parkinson's Disease (PD), recent work has explored whether closed-loop adaptive DBS (aDBS) may better address fluctuating symptoms through patient-specific and symptom-relevant neurophysiological biomarkers. To aid these investigations, we designed an interface for the research-enabled Summit Medtronic RC+S (RC+S) implanted neurostimulator (INS) to collect multi-day recordings along with the implementation of aDBS therapy. NEW METHOD:We developed applications in MATLAB for investigating optimal brain recording locations, setting thresholds for real-time analysis, determining the INS's position along with in-home recordings of neural activity, and implementation of aDBS algorithms. RESULTS:In a pilot study conducted in PD subjects (n = 5), we successfully determined optimal DBS lead contacts for detecting maximal beta (13-30 Hz) activity for streaming in-home neural activity with closed-loop adjustments to stimulation amplitude (n = 24-27 days). Using a Bluetooth connection method we developed, 95.2 % in-home data was collected. COMPARISON WITH EXISTING METHODS:The software and hardware applications described in this report provide MATLAB based tools to enable a distributed strategy for interfacing with the RC+S deployed at in-home settings for multi-hour recordings. CONCLUSIONS:Our interface provides investigators using the RC+S, in the context of aDBS, access to chronic recordings in real-time while providing adaptive stimulation based on continuous data analysis in MATLAB using a USB or Bluetooth connection. Advancing the efforts to characterize relevant biomarkers and develop therapeutic aDBS strategies for those treated with DBS, such as PD.

Mentor: Olga Taraschenko Program: Neurological Sciences Type: Original Research Background: Resection zone extension in medically refractory epilepsy patients to include the area of seizure spread may result in better postoperative seizure control, resulting in intact cognition and seizure freedom. We explore the relationship between seizure propagation speed and seizure control after resection focusing on presurgical IQ test performances. Methods: Epilepsy surgery records between 2008 and 2016 were reviewed. Demographics, presurgical scores on the Wechsler Adult Intelligence Scale (WAIS-IV) and Wechsler Abbreviated Score of Intelligence (WASI-II), including General Adult Intelligence Standard Score (GAI), Full-Scale Intelligence Quotient (FSIQ), Verbal Comprehension Index (VCI), and Perceptual Reasoning Index (PRI), were recorded and Engel scores at 2-year post-surgical visit were noted. Two epileptologists reviewed the presurgical intracranial EEG tracings to denote seizure onset as well as spread. Early spread was defined as propagation within ≥ 2 surrounding grid contacts within the first 10s of seizure onset. After identification of the primary channel by visual review, a frequency range of interest and then the power of all channels at +/− 1Hz were identified. Channels within 10sec from seizure onset with power > mean+2SD of identified channel for ≥2 consecutive epochs (epochs=1 sec with 0.5sec overlap) were noted. Results: Among 45 patients, 71% were female, and the mean age at surgery was 38±13 years. During visual analysis, 20(44%) had early spread, 16(35.5%) had late spread, and 9(20%) had absent spread. There were no statistically significant differences between early and late spread groups regarding their performance on GAI/FSIQ, VCI, and PRI tests, and no association was observed between early/late spread and Engel score distribution. The signal processing algorithm has agreed with the clinical categorization of early vs. late ictal spread in 78.5% of seizures. Conclusion: Early seizure spread in focal medically refractory epilepsy may not affect patients’ general intellectual function nor determine their postoperative seizure freedom. A signal processing algorithm for the analysis of seizure spread on intracranial EEGs can be applied to supplement the visual analysis of seizure data.

Localizing eloquent cortices is crucial for many neurosurgical applications, such as epilepsy and tumor resections. Clinicians may use non-invasive methods such as magnetoencephalography (MEG) to localize these cortical regions using equivalent current dipoles (ECDs). While dipoles are clinically validated, they provide the estimated strength, location, and orientation of only one or a few sources that best describe the recorded neuromagnetic data, requiring clinicians to make subjective decisions on the spatial extent of the underlying cortical area. More accurate delineation of eloquent cortical areas using distributed source localization methods would provide additional pre-surgical information on these regions’ location and spatial distribution, which could lead to reduced post-surgical complications associated with damage to or removal of eloquent cortices. Our objective in this paper was to present a method to post-process the distributed source localization results to yield a directly interpretable, distributed region of activation. As a test case, we selected somatosensory stimulation in a retrospective cohort of focal and multi-focal epilepsy patients. Our algorithm performs source localization using a distributed method (sLORETA), followed by post-processing and blind source separation to identify the area and boundary of the cortical tissue that primarily activates in response to somatosensory stimulation. We calculated the statistical significance of localization by comparing the identified region to an anatomical atlas and random chance. While examining patients who received left (upper left, UL) and right (upper right, UR) sided median nerve stimulation, the cortical areas identified by the algorithm were in anatomically appropriate areas with a median overlap of 97.6% and 94.7%, respectively. We observe that our algorithm localized somatosensory responses better than random chance in 57/58 (98%) patients who performed the UL task (p < 10 × 10−10, binomial test) and 49/50 (98%) patients who performed the UR task (p < 10 × 10−10, binomial test). We compared the localization of our algorithm to current clinical methods and found that our algorithm is not inferior to dipole localization. The algorithm can successfully localize somatosensory responses on the cortical surface in anatomically appropriate regions while providing the spatial extent of cortical activation, reducing subjectivity associated with dipole localization.

We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone compared with other leading interictal biomarkers in a multicentre, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centres who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection [International League Against Epilepsy Class 1 outcome (ILAE 1)] and those with persistent seizures (ILAE 2-6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80-500 Hz), ripple (80-250 Hz) and fast ripple (250-500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Overall, 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P < 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P < 0.001 and P < 0.001, respectively). Among ILAE 1 subjects, the mean spike ripple rate was higher in the resected volume (0.66/min) than in the non-removed tissue (0.08/min, P < 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared with ILAE 2-6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P < 0.001), spike-gamma (P < 0.001), wideband HFOs (P < 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01) or fast ripples (36%, P < 0.001) removed. Thus, in this large, multicentre cohort, when surgical resection was successful, the majority of spike ripples were removed. Furthermore, automatically detected spike ripples localize the epileptogenic tissue better than spikes, spike-gamma, wideband HFOs, ripples and fast ripples.

Circular statistics and Rayleigh tests are important tools for analyzing cyclic events. However, current methods are not robust to significant measurement bias, especially incomplete or otherwise non-uniform sampling. One example is studying 24-cyclicity but having data not recorded uniformly over the full 24-hour cycle. Our objective is to present a robust method to estimate circular statistics and their statistical significance in the presence of incomplete or otherwise non-uniform sampling. Our method is to solve the underlying Fredholm Integral Equation for the more general problem, estimating probability distributions in the context of imperfect measurements, with our circular statistics in the presence of incomplete/non-uniform sampling being one special case. The method is based on linear parameterizations of the underlying distributions. We simulated the estimation error of our approach for several toy examples as well as for a real-world example: analyzing the 24-hour cyclicity of an electrographic biomarker of epileptic tissue controlled for states of vigilance. We also evaluated the accuracy of the Rayleigh test statistic versus the direct simulation of statistical significance. Our method shows a very low estimation error. In the real-world example, the corrected moments had a root mean square error of < 0.007. In contrast, the Rayleigh test statistic over estimated the statistical significance and was thus not reliable. The presented methods thus provide a robust solution to computing circular moments even with incomplete or otherwise non-uniform sampling. Since Rayleigh test statistics cannot be used in this circumstance, direct estimation of significance is the preferable option for estimating statistical significance.

High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.

A growing body of literature suggests that deep brain stimulation to treat motor symptoms of Parkinson’s disease may also ameliorate certain sleep deficits. Many foundational studies have examined the impact of stimulation on sleep following several months of therapy, leaving an open question regarding the time course for improvement. It is unknown whether sleep improvement will immediately follow onset of therapy or accrete over a prolonged period of stimulation. The objective of our study was to address this knowledge gap by assessing the impact of deep brain stimulation on sleep macro-architecture during the first nights of stimulation. Polysomnograms were recorded for 3 consecutive nights in 14 patients with advanced Parkinson’s disease (10 male, 4 female; age: 53–74 years), with intermittent, unilateral subthalamic nucleus deep brain stimulation on the final night or 2. Sleep scoring was determined manually by a consensus of 4 experts. Sleep macro-architecture was objectively quantified using the percentage, latency, and mean bout length of wake after sleep onset and on each stage of sleep (rapid eye movement and non-rapid eye movement stages 1, 2, 3). Sleep was found to be highly disrupted in all nights. Sleep architecture on nights without stimulation was consistent with prior results in treatment naive patients with Parkinson’s disease. No statistically significant difference was observed due to stimulation. These objective measures suggest that 1 night of intermittent subthreshold stimulation appears insufficient to impact sleep macro-architecture. Registry: ClinicalTrials.gov; Name: Adaptive Neurostimulation to Restore Sleep in Parkinson’s Disease; URL: https://clinicaltrials.gov/ct2/show/NCT04620551 ; Identifier: NCT04620551. Das R, Gliske SV, West LC, et al. Sleep macro-architecture in patients with Parkinson’s disease does not change during the first night of neurostimulation in a pilot study. J Clin Sleep Med. 2024;20(9):1489–1496.

OBJECTIVE:Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) is a common treatment for motor symptoms of Parkinson's disease but its influence on non-motor symptoms is less clear. Sleep spindles are known to be reduced in patients with Parkinson's disease, but the effect of STN DBS is unknown. The objective of our study was to address this knowledge gap. METHOD:Polysomnograms were recorded for three consecutive nights in 15 patients with advanced Parkinson's disease (11 male, 4 female; age: 53-75 years), including at least one night each of unilateral STN DBS stimulation ON and OFF. Stimulation ON was set to 70 % of clinical amplitude to mitigate sleep being altered via changing motor symptoms or due to patient awareness of stimulation. Sleep spindles were detected in electroencephalogram (EEG) data by two previously published, validated automated sleep spindle detection algorithms: Ferrarelli et al. (2007) and Martin et al. (2013). RESULTS:Sleep spindle density was higher during stimulation ON than OFF nights in 11 of 12 subjects using either sleep spindle detection algorithm (p<=0.01, Wilcoxon rank sum). Stimulation ON versus OFF had no statistically significant effect on sleep spindle duration or amplitude. CONCLUSION:Our analysis indicates that a single night of sub-optimal STN stimulation significantly increases sleep spindle density in Parkinson's disease patients. SIGNIFICANCE:These results further our understanding of how DBS impacts non-motor symptoms of Parkinson's disease.

The search for valid biomarkers to aid in epilepsy diagnosis and management is a major goal of the Epilepsy Research Benchmarks. Many papers and grants answer this call by searching for new biomarkers from a wide range of disciplines. However, the academic use of the word “biomarker” is often imprecise. Without proper definition, such work is not well-prepared to progress to the next step of translating these biomarkers into clinical use. In 2016, the Food and Drug Administration and National Institutes of Health collaborated to develop the BEST (Biomarkers, EndpointS, and other Tools) Resource as a guide to adopt formal definitions that aid in pushing successful biomarkers toward regulatory approval. Using the vignette of high-frequency oscillations, which have been proposed as a potential biomarker of several potential aspects of epilepsy, we demonstrate how improper use of the term “biomarker,” and lack of a clear context of use, can lead to confusion and difficulty obtaining regulatory approval. Similar conditions are likely in many areas of biomarker research. This Resource should be adopted by all researchers developing epilepsy biomarkers. Adopting the BEST guidelines will improve reproducibility, guide research objectives toward translation, and better target the Epilepsy Benchmarks.

Objective We evaluated whether the combination of epileptiform spikes and ripples (spike ripples) outperformed other leading biomarkers in identifying the epileptogenic zone across subjects in a multicenter international study. Methods We validated and applied an automated spike ripple detector on intracranial EEG recordings in subjects from 4 centers who subsequently underwent surgical resection with known 1-year seizure outcomes. We evaluated the spike ripple rate in subjects cured after resection (ILAE 1 outcome) and those with persistent seizures (ILAE 2-5) across sites and recording types. We also evaluated spike, wideband HFO (80-500 Hz), fast ripple (250-500 Hz), and ripple (80-250 Hz) rates using validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Results 109 subjects were included. The majority of spike ripples were removed in subjects with ILAE 1 outcome (p = 1e-6), and this was qualitatively observed across the four sites (p = 0.032, p = 0.092, p = 0.0005, p = 0.003) and the two electrode types (p = 0.01, p = 7e-6). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared to ILAE 2-5 outcomes (p = 0.02). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (p = 0.0004), wideband HFOs (p = 0.0004), fast ripples (p = 0.008), and ripples (p = 0.008) removed. At the individual level, more subjects with ILAE 1 outcome had the majority of spike ripples removed (40/48, 83%) than spikes (69%, p = 0.04), wideband HFOs (63%, p = 0.009), fast ripples (36%, p = 2e-5), or ripples (45%, p = 0.0007) removed. Interpretation When surgical resection was successful, the majority of spike ripples were removed. Automatically detected spike ripples have improved specificity for epileptogenic tissue compared to spikes, wideband HFOs, fast ripples, and ripples. ### Competing Interest Statement SG and WCS have a licensing agreement with natus medical inc. Natus was not involved in the publication of this manuscript. ### Funding Statement This study was funded by NIH NINDS R01NS119483 ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Massachusetts General Hospital Institutional Review Board I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data produced in the present study are available upon reasonable request to the relevant co-authors from each institution.

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.

Epilepsy is one of the most common neurological diseases. In cases where patients do not respond to medications, resective surgery is often the next best option to obtain seizure freedom. Intracranial EEG analysis is the current gold standard for resective surgery planning. However, clinical marking is subjective, and many seizures are complex with ambiguous onset locations. The objective, in this proof-of-concept study, was to determine whether quantification with dynamic mode decomposition (DMD) may assist in localizing seizure onset. We analyzed one seizure each from five patients with epilepsy and identified channels with maximal involvement in the leading dynamic mode. In three of the five cases, the area of activity identified by our method showed statistically significant correlation with clinically identified channels. We conclude that DMD effectively captures the seizure onsets and is ready for future study in larger cohorts.