High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery.

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.

Spike ripples localize the epileptogenic zone best: an international intracranial study.

We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone (EZ) compared to other leading interictal biomarkers in a multicenter, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centers who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection (ILAE 1 outcome) 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. 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, P < 0.001). Among ILAE 1 subjects, the mean spike ripple rate was higher in the RV (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 to 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, multicenter cohort, when surgical resection was successful, the majority of spike ripples were removed. Further, automatically detected spike ripples have improved specificity for epileptogenic tissue compared to spikes, spike-gamma, wideband HFOs, ripples, and fast ripples.

Estimation of Circular Statistics in the Presence of Measurement Bias.

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 [Formula: see text]. In contrast, the Rayleigh test statistic overestimated 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.

Optimization of ictal aborting stimulation using the dynamotype taxonomy.

Electrical stimulation is an increasingly popular method to terminate epileptic seizures, yet it is not always successful. A potential reason for inconsistent efficacy is that stimuli are applied empirically without considering the underlying dynamical properties of a given seizure. We use a computational model of seizure dynamics to show that different bursting classes have disparate responses to aborting stimulation. This model was previously validated in a large set of human seizures and led to a description of the Taxonomy of Seizure Dynamics and the dynamotype, which is the clinical analog of the bursting class. In the model, the stimulation is realized as an applied input, which successfully aborts the burst when it forces the system from a bursting state to a quiescent state. This transition requires bistability, which is not present in all bursters. We examine how topological and geometric differences in the bistable state affect the probability of termination as the burster progresses from onset to offset. We find that the most significant determining factors are the burster class (dynamotype) and whether the burster has a DC (baseline) shift. Bursters with a baseline shift are far more likely to be terminated due to the necessary structure of their state space. Furthermore, we observe that the probability of termination varies throughout the burster's duration, is often dependent on the phase when it was applied, and is highly correlated to dynamotype. Our model provides a method to predict the optimal method of termination for each dynamotype. These results lead to the prediction that optimization of ictal aborting stimulation should account for seizure dynamotype, the presence of a DC shift, and the timing of the stimulation.

Increasing challenges to trial recruitment and conduct over time.

OBJECTIVE: This study was undertaken to evaluate how the challenges in the recruitment and retention of participants in clinical trials for focal onset epilepsy have changed over time. METHODS: In this systematic analysis of randomized clinical trials of adjunct antiseizure medications for medication-resistant focal onset epilepsy, we evaluated how the numbers of participants, sites, and countries have changed since the first such trial in 1990. We also evaluated the proportion of participants who completed each trial phase and their reasons for early trial exit. We analyzed these trends using mixed effects generalized linear models accounting for the influence of the number of trial sites and trial-specific variability. RESULTS: The number of participants per site has steadily decreased over decades, with recent trials recruiting fewer than five participants per site (reduction by .16 participants/site/year, p < .0001). Fewer participants also progressed from recruitment to randomization over time (odds ratio = .94/year, p = .014). Concurrently, there has been an increase in the placebo response over time (increase in median percent reduction of .4%/year, p = .02; odds ratio of increase in 50% responder rate of 1.03/year, p = .02), which was not directly associated with the number of sites per trial (p > .20). SIGNIFICANCE: This historical analysis highlights the increasing challenges with participant recruitment and retention, as well as increasing placebo response. It serves as a call to action to change clinical trial design to address these challenges.

The BEST Conceivable Way to Talk About Epilepsy Biomarkers.

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.

Therapeutic Effect of Argatroban During Cardiopulmonary Resuscitation and Streptokinase During Extracorporeal Cardiopulmonary Resuscitation in a Porcine Model of Prolonged Cardiac Arrest.

Prolonged cardiac arrest (CA) causes microvascular thrombosis which is a potential barrier to organ reperfusion during extracorporeal cardiopulmonary resuscitation (ECPR). The aim of this study was to test the hypothesis that early intra-arrest anticoagulation during cardiopulmonary resuscitation (CPR) and thrombolytic therapy during ECPR improve recovery of brain and heart function in a porcine model of prolonged out-of-hospital CA. DESIGN: Randomized interventional trial. SETTING: University laboratory. SUBJECTS: Swine. INTERVENTIONS: In a blinded study, 48 swine were subjected to 8 minutes of ventricular fibrillation CA followed by 30 minutes of goal-directed CPR and 8 hours of ECPR. Animals were randomized into four groups (n = 12) and given either placebo (P) or argatroban (ARG; 350 mg/kg) at minute 12 of CA and either placebo (P) or streptokinase (STK, 1.5 MU) at the onset of ECPR. MEASUREMENTS AND MAIN RESULTS: Primary outcomes included recovery of cardiac function measured by cardiac resuscitability score (CRS: range 0-6) and recovery of brain function measured by the recovery of somatosensory-evoked potential (SSEP) cortical response amplitude. There were no significant differences in recovery of cardiac function as measured by CRS between groups (p = 0.16): P + P 2.3 (1.0); ARG + P = 3.4 (2.1); P + STK = 1.6 (2.0); ARG + STK = 2.9 (2.1). There were no significant differences in the maximum recovery of SSEP cortical response relative to baseline between groups (p = 0.73): P + P = 23% (13%); ARG + P = 20% (13%); P + STK = 25% (14%); ARG + STK = 26% (13%). Histologic analysis demonstrated reduced myocardial necrosis and neurodegeneration in the ARG + STK group relative to the P + P group. CONCLUSIONS: In this swine model of prolonged CA treated with ECPR, early intra-arrest anticoagulation during goal-directed CPR and thrombolytic therapy during ECPR did not improve initial recovery of heart and brain function but did reduce histologic evidence of ischemic injury. The impact of this therapeutic strategy on the long-term recovery of cardiovascular and neurological function requires further investigation.

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.

Seizure forecasting: Where do we stand?

A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls.

PURPOSE: Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations. METHODS: Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners. RESULTS: After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm3, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS. CONCLUSIONS: These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

Visual cortex responds to sound onset and offset during passive listening.

Sounds enhance our ability to detect, localize, and respond to co-occurring visual targets. Research suggests that sounds improve visual processing by resetting the phase of ongoing oscillations in visual cortex. However, it remains unclear what information is relayed from the auditory system to visual areas and if sounds modulate visual activity even in the absence of visual stimuli (e.g., during passive listening). Using intracranial electroencephalography (iEEG) in humans, we examined the sensitivity of visual cortex to three forms of auditory information during a passive listening task: auditory onset responses, auditory offset responses, and rhythmic entrainment to sounds. Because some auditory neurons respond to both sound onsets and offsets, visual timing and duration processing may benefit from each. In addition, if auditory entrainment information is relayed to visual cortex, it could support the processing of complex stimulus dynamics that are aligned between auditory and visual stimuli. Results demonstrate that in visual cortex, amplitude-modulated sounds elicited transient onset and offset responses in multiple areas, but no entrainment to sound modulation frequencies. These findings suggest that activity in visual cortex (as measured with iEEG in response to auditory stimuli) may not be affected by temporally fine-grained auditory stimulus dynamics during passive listening (though it remains possible that this signal may be observable with simultaneous auditory-visual stimuli). Moreover, auditory responses were maximal in low-level visual cortex, potentially implicating a direct pathway for rapid interactions between auditory and visual cortices. This mechanism may facilitate perception by time-locking visual computations to environmental events marked by auditory discontinuities.NEW & NOTEWORTHY Using intracranial electroencephalography (iEEG) in humans during a passive listening task, we demonstrate that sounds modulate activity in visual cortex at both the onset and offset of sounds, which likely supports visual timing and duration processing. However, more complex auditory rate information did not affect visual activity. These findings are based on one of the largest multisensory iEEG studies to date and reveal the type of information transmitted between auditory and visual regions.

Interictal high frequency background activity as a biomarker of epileptogenic tissue.

High frequency oscillations (HFOs) are very brief events that are a well-established biomarker of the epileptogenic zone (EZ) but are rare and comprise only a tiny fraction of the total recorded EEG. We hypothesize that the interictal high frequency 'background' data, which has received little attention but represents the majority of the EEG record, also may contain additional, novel information for identifying the EZ. We analysed intracranial EEG (30-500 Hz frequency range) acquired from 24 patients who underwent resective surgery. We computed 38 quantitative features based on all usable, interictal data (63-307 h per subject), excluding all detected HFOs. We assessed association between each feature and the seizure onset zone (SOZ) and resected volume (RV) using logistic regression. A pathology score per channel was also created via principle component analysis and logistic regression, using hold-out-one-patient cross-validation to avoid in-sample training. Association of the pathology score with the SOZ and RV was quantified using an asymmetry measure. Many features were associated with the SOZ: 23/38 features had odds ratios >1.3 or <0.7 and 17/38 had odds ratios different than zero with high significance (P < 0.001/39, logistic regression with Bonferroni Correction). The pathology score, the rate of HFOs, and their channel-wise product were each strongly associated with the SOZ [median asymmetry ≥0.44, good surgery outcome patients; median asymmetry ≥0.40, patients with other outcomes; 95% confidence interval (CI) > 0.27 in both cases]. The pathology score and the channel-wise product also had higher asymmetry with respect to the SOZ than the HFO rate alone (median difference in asymmetry ≥0.18, 95% CI >0.05). These results support that the high frequency background data contains useful information for determining the EZ, distinct and complementary to information from detected HFOs. The concordance between the high frequency activity pathology score and the rate of HFOs appears to be a better biomarker of epileptic tissue than either measure alone.

Visual speech differentially modulates beta, theta, and high gamma bands in auditory cortex.

Speech perception is a central component of social communication. Although principally an auditory process, accurate speech perception in everyday settings is supported by meaningful information extracted from visual cues. Visual speech modulates activity in cortical areas subserving auditory speech perception including the superior temporal gyrus (STG). However, it is unknown whether visual modulation of auditory processing is a unitary phenomenon or, rather, consists of multiple functionally distinct processes. To explore this question, we examined neural responses to audiovisual speech measured from intracranially implanted electrodes in 21 patients with epilepsy. We found that visual speech modulated auditory processes in the STG in multiple ways, eliciting temporally and spatially distinct patterns of activity that differed across frequency bands. In the theta band, visual speech suppressed the auditory response from before auditory speech onset to after auditory speech onset (-93 to 500 ms) most strongly in the posterior STG. In the beta band, suppression was seen in the anterior STG from -311 to -195 ms before auditory speech onset and in the middle STG from -195 to 235 ms after speech onset. In high gamma, visual speech enhanced the auditory response from -45 to 24 ms only in the posterior STG. We interpret the visual-induced changes prior to speech onset as reflecting crossmodal prediction of speech signals. In contrast, modulations after sound onset may reflect a decrease in sustained feedforward auditory activity. These results are consistent with models that posit multiple distinct mechanisms supporting audiovisual speech perception.

The accuracy of quantitative EEG biomarker algorithms depends upon seizure onset dynamics.

OBJECTIVE: To compare the performance of different ictal quantitative biomarkers of the seizure onset zone (SOZ) across many seizures in a cohort of consecutive patients with a variety of seizure onset patterns. METHODS: The Epileptogenicity Index (EI, a measure of fast activity) and Slow Polarizing Shift index (SPS, a measure of infraslow activity) were calculated for 212 seizures (22 patients). After stratification by onset pattern, median index values inside and outside the SOZ were compared in aggregate and for each of the onset patterns. Receiver Operating Characteristic (ROC) curves were constructed to compare the performance of each index. RESULTS: Median values of EI (0.056 vs 0.0087), SPS (0.27 vs 0.19), and CI (0.21 vs 0.12) were significantly higher for contacts inside the SOZ, all p < 0.0001. Analysis of AUC showed variable performance of these indices across seizure types, although AUC for EI and SPS was generally greatest for seizures with fast activity at onset. CONCLUSIONS: All indices were significantly higher for contacts inside the SOZ; however, the performance of these indices varied depending on the pattern of seizure onset. SIGNIFICANCE: These findings suggest that future studies of quantitative biomarkers of the SOZ should account for seizure onset pattern.

A minority of patients with functional seizures have abnormalities on neuroimaging.

OBJECTIVE: Functional seizures often are managed incorrectly as a diagnosis of exclusion. However, a significant minority of patients with functional seizures may have abnormalities on neuroimaging that typically are associated with epilepsy, leading to diagnostic confusion. We evaluated the rate of epilepsy-associated findings on MRI, FDG-PET, and CT in patients with functional seizures. METHODS: We studied radiologists' reports from neuroimages at our comprehensive epilepsy center from a consecutive series of patients diagnosed with functional seizures without comorbid epilepsy from 2006 to 2019. We summarized the MRI, FDG-PET, and CT results as follows: within normal limits, incidental findings, unrelated findings, non-specific abnormalities, post-operative study, epilepsy risk factors (ERF), borderline epilepsy-associated findings (EAF), and definitive EAF. RESULTS: Of the 256 MRIs, 23% demonstrated ERF (5%), borderline EAF (8%), or definitive EAF (10%). The most common EAF was hippocampal sclerosis, with the majority of borderline EAF comprising hippocampal atrophy without T2 hyperintensity or vice versa. Of the 87 FDG-PETs, 26% demonstrated borderline EAF (17%) or definitive EAF (8%). Epilepsy-associated findings primarily included focal hypometabolism, especially of the temporal lobes, with borderline findings including subtle or questionable hypometabolism. Of the 51 CTs, only 2% had definitive EAF. SIGNIFICANCE: This large case series provides further evidence that, while uncommon, EAF are seen in patients with functional seizures. A significant portion of these abnormal findings are borderline. The moderately high rate of these abnormalities may represent framing bias from the indication of the study being "seizures," the relative subtlety of EAF, or effects of antiseizure medications.

Extracorporeal Cardiopulmonary Resuscitation for Refractory Out-of-Hospital Cardiac Arrest (EROCA): Results of a Randomized Feasibility Trial of Expedited Out-of-Hospital Transport.

STUDY OBJECTIVE: Outcomes of extracorporeal cardiopulmonary resuscitation (ECPR) for out-of-hospital cardiac arrest depend on time to therapy initiation. We hypothesize that it would be feasible to select refractory out-of-hospital cardiac arrest patients for expedited transport based on real-time estimates of the 911 call to the emergency department (ED) arrival interval, and for emergency physicians to rapidly initiate ECPR in eligible patients. METHODS: In a 2-tiered emergency medical service with an ECPR-capable primary destination hospital, adults with refractory shockable or witnessed out-of-hospital cardiac arrest were randomized 4:1 to expedited transport or standard care if the predicted 911 call to ED arrival interval was less than or equal to 30 minutes. The primary outcomes were the proportion of subjects with 911 call to ED arrival less than or equal to 30 minutes and ED arrival to ECPR flow less than or equal to 30 minutes. RESULTS: Of 151 out-of-hospital cardiac arrest 911 calls, 15 subjects (10%) were enrolled. Five of 12 subjects randomized to expedited transport had an ED arrival time of less than or equal to 30 minutes (overall mean 32.5 minutes [SD 7.1]), and 5 were eligible for and treated with ECPR. Three of 5 ECPR-treated subjects had flow initiated in less than or equal to 30 minutes of ED arrival (overall mean 32.4 minutes [SD 10.9]). No subject in either group survived with a good neurologic outcome. CONCLUSION: The Extracorporeal Cardiopulmonary Resuscitation for Refractory Out-of-Hospital Cardiac Arrest trial did not meet predefined feasibility outcomes for selecting out-of-hospital cardiac arrest patients for expedited transport and initiating ECPR in the ED. Additional research is needed to improve the accuracy of predicting the 911 call to ED arrival interval, optimize patient selection, and reduce the ED arrival to ECPR flow interval.

Comparison of signal decomposition techniques for analysis of human cortical signals.

OBJECTIVE: Conventional neural signal analysis methods assume that features of interest are linear, time-invariant signals confined to well-delineated spectral bands. However, new evidence suggests that neural signals exhibit important non-stationary characteristics with ill-defined spectral distributions. These features pose a need for signal processing algorithms that can characterize temporal and spectral features of non-linear time series. This study compares the effectiveness of four algorithms in extracting neural information for use in decoding cortical signals: Fast Fourier Transform bandpass filtering (FFT), principal spectral component analysis (PSCA), wavelet analysis (WA), and empirical mode decomposition (EMD). APPROACH: Electrocorticographic signals were recorded from the motor and sensory cortex of two epileptic patients performing finger movements. Each signal processing algorithm was used to extract beta (10-30 Hz) and gamma (66-114 Hz) band power to detect thumb movement and decode finger flexions, respectively. Naïve-Bayes (NB), support vector machine (SVM), and linear discriminant analysis (LDA) classifiers using each signal were validated using leave-one-out cross-validation. MAIN RESULTS: Decoders using all four signal decompositions achieved above 90% average accuracy in finger movement detection using beta power. When decoding individual finger flexion using gamma, the PSCA NB classifiers achieved 78 ± 4% accuracy while FFT, WA, and EMD analysis achieved accuracies of 73 ± 8%, 68 ± 7%, and 62 ± 3% respectively, with similar results using SVM and LDA. SIGNIFICANCE: These results illustrate the relative levels of useful information contributed by each decomposition method in the case of finger movement decoding, which can inform the development of effective neural decoding pipelines. Further analyses could compare performance using more specific non-sinusoidal features, such as transients and phase-amplitude coupling.

Preictal variability of high-frequency oscillation rates in refractory epilepsy.

OBJECTIVE: High-frequency oscillations (HFOs) have shown promising utility in the spatial localization of the seizure onset zone for patients with focal refractory epilepsy. Comparatively few studies have addressed potential temporal variations in HFOs, or their role in the preictal period. Here, we introduce a novel evaluation of the instantaneous HFO rate through interictal and peri-ictal epochs to assess their usefulness in identifying imminent seizure onset. METHODS: Utilizing an automated HFO detector, we analyzed intracranial electroencephalographic data from 30 patients with refractory epilepsy undergoing long-term presurgical evaluation. We evaluated HFO rates both as a 30-minute average and as a continuous function of time and used nonparametric statistical methods to compare individual and population-level differences in rate during peri-ictal and interictal periods. RESULTS: Mean HFO rate was significantly higher for all epochs in seizure onset zone channels versus other channels. Across the 30 patients of our cohort, we found no statistically significant differences in mean HFO rate during preictal and interictal epochs. For continuous HFO rates in seizure onset zone channels, however, we found significant population-wide increases in preictal trends relative to interictal periods. Using a data-driven analysis, we identified a subset of 11 patients in whom either preictal HFO rates or their continuous trends were significantly increased relative to those of interictal baseline and the rest of the population. SIGNIFICANCE: These results corroborate existing findings that HFO rates within epileptic tissue are higher during interictal periods. We show this finding is also present in preictal, ictal, and postictal data, and identify a novel biomarker of preictal state: an upward trend in HFO rate leading into seizures in some patients. Overall, our findings provide preliminary evidence that HFOs can function as a temporal biomarker of seizure onset.

Distinguishing false and true positive detections of high frequency oscillations.

OBJECTIVE: High frequency oscillations (HFOs) are a promising biomarker of tissue that instigates seizures. However, ambiguous data and random background fluctuations can cause any HFO detector (human or automated) to falsely label non-HFO data as an HFO (a false positive detection). The objective of this paper was to identify quantitative features of HFOs that distinguish between true and false positive detections. APPROACH: Feature selection was performed using background data in multi-day, interictal intracranial recordings from ten patients. We selected the feature most similar between randomly selected segments of background data and HFOs detected in surrogate background data (false positive detections by construction). We then compared these results with fuzzy clustering of detected HFOs in clinical data to verify the feature's applicability. We validated the feature is sensitive to false versus true positive HFO detections by using an independent data set (six subjects) scored for HFOs by three human reviewers. Lastly, we compared the effect of redacting putative false positive HFO detections on the distribution of HFOs across channels and their association with seizure onset zone (SOZ) and resected volume (RV). MAIN RESULTS: Of the 15 analyzed features, the analysis selected only skewness of the curvature (skewCurve). The feature was validated in human scored data to be associated with distinguishing true and false positive HFO detections. Automated HFO detections with higher skewCurve were more focal based on entropy measures and had increased localization to both the SOZ and RV. SIGNIFICANCE: We identified a quantitative feature of HFOs which helps distinguish between true and false positive detections. Redacting putative false positive HFO detections improves the specificity of HFOs as a biomarker of epileptic tissue.

Quantifying epileptogenesis in rats with spontaneous and responsive brain state dynamics.

There is a crucial need to identify biomarkers of epileptogenesis that will help predict later development of seizures. This work identifies two novel electrophysiological biomarkers that quantify epilepsy progression in a rat model of epileptogenesis. The long-term tetanus toxin rat model was used to show the development and remission of epilepsy over several weeks. We measured the response to periodic electrical stimulation and features of spontaneous seizure dynamics over several weeks. Both biomarkers showed dramatic changes during epileptogenesis. Electrically induced responses began to change several days before seizures began and continued to change until seizures resolved. These changes were consistent across animals and allowed development of an algorithm that could differentiate which animals would later develop epilepsy. Once seizures began, there was a progression of seizure dynamics that closely follows recent theoretical predictions, suggesting that the underlying brain state was changing over time. This research demonstrates that induced electrical responses and seizure onset dynamics are useful biomarkers to quantify dynamical changes in epileptogenesis. These tools hold promise for robust quantification of the underlying epileptogenicity and prediction of later development of seizures.