S. Gliske

Universal automated high frequency oscillation detector for real-time, long term EEG.

OBJECTIVEnInterictal high frequency oscillations (HFOs) in intracranial EEG are a potential biomarker of epilepsy, but current automated HFO detectors require human review to remove artifacts. Our objective is to automatically redact false HFO detections, facilitating clinical use of interictal HFOs.nnnMETHODSnIntracranial EEG data from 23 patients were processed with automated detectors of HFOs and artifacts. HFOs not concurrent with artifacts were labeled quality HFOs (qHFOs). Methods were validated by human review on a subset of 2000 events. The correlation of qHFO rates with the seizure onset zone (SOZ) was assessed via (1) a retrospective asymmetry measure and (2) a novel quasi-prospective algorithm to identify SOZ.nnnRESULTSnHuman review estimated that less than 12% of qHFOs are artifacts, whereas 78.5% of redacted HFOs are artifacts. The qHFO rate was more correlated with SOZ (p=0.020, Wilcoxon signed rank test) and resected volume (p=0.0037) than baseline detections. Using qHFOs, our algorithm was able to determine SOZ in 60% of the ILAE Class I patients, with all algorithmically-determined SOZs fully within the resected volumes.nnnCONCLUSIONSnThe algorithm reduced false-positive HFO detections, improving the precision of the HFO-biomarker.nnnSIGNIFICANCEnThese methods provide a feasible strategy for HFO detection in real-time, continuous EEG with minimal human monitoring of data quality.

Network Mechanisms Generating Abnormal and Normal Hippocampal High-Frequency Oscillations: A Computational Analysis.

Abstract High-frequency oscillations (HFOs) are an intriguing potential biomarker for epilepsy, typically categorized according to peak frequency as either ripples (100–250 Hz) or fast ripples (>250 Hz). In the hippocampus, fast ripples were originally thought to be more specific to epileptic tissue, but it is still very difficult to distinguish which HFOs are caused by normal versus pathological brain activity. In this study, we use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by different mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples.

Effect of sampling rate and filter settings on High Frequency Oscillation detections

OBJECTIVEnHigh Frequency Oscillations (HFOs) are being studied as a biomarker of epilepsy, yet it is unknown how various acquisition parameters at different centers affect detection and analysis of HFOs. This paper specifically quantifies effects of sampling rate (FS) and anti-aliasing filter (AAF) positions on automated HFO detection.nnnMETHODSnHFOs were detected on intracranial EEG recordings (17 patients) with 5kHz FS. HFO detection was repeated on downsampled and/or filtered copies of the EEG data, mimicking sampling rates and low-pass filter settings of various acquisition equipment. For each setting, we compared the HFO detection sensitivity, HFO features, and ability to identify the ictal onset zone.nnnRESULTSnThe relative sensitivity remained above 80% for either FS ⩾2kHz or AAF ⩾500Hz. HFO feature distributions were consistent (AUROC<0.7) down to 1kHz FS or 200Hz AAF. HFO rate successfully identified ictal onset zone over most settings. HFO peak frequency was highly variable under most parameters (Spearman correlation<0.5).nnnCONCLUSIONSnWe recommend at least FS ⩾2kHz and AAF ⩾500Hz to detect HFOs. Additionally, HFO peak frequency is not robust at any setting: the same HFO event can be variably classified either as a ripple (<200Hz) or fast ripple (>250Hz) under different acquisition settings.nnnSIGNIFICANCEnThese results inform clinical centers on requirements to analyze HFO rates and features.

Production of two hadrons in semi-inclusive deep inelastic scattering

We present the general expression, in terms of structure functions, of the cross section for the production of two hadrons in semi-inclusive deep inelastic scattering. We analyze this process including full transverse-momentum dependence up to subleading twist and check, where possible, the consistency with existing literature.

The intrinsic value of HFO features as a biomarker of epileptic activity

High frequency oscillations (HFOs) are a promising biomarker of epileptic brain tissue and activity. HFOs additionally serve as a prototypical example of challenges in the analysis of discrete events in high-temporal resolution, intracranial EEG data. Two primary challenges are (1) dimensionality reduction, and (2) assessing feasibility of classification. Dimensionality reduction assumes that the data lie on a manifold with dimension less than that of the features space. However, previous HFO analysis have assumed a linear manifold, global across time, space (i.e. recording electrode/channel), and individual patients. Instead, we assess both (a) whether linear methods are appropriate and (b) the consistency of the manifold across time, space, and patients. We also estimate bounds on the Bayes classification error to quantify the distinction between two classes of HFOs (those occurring during seizures and those occurring due to other processes). This analysis provides the foundation for future clinical use of HFO features and guides the analysis for other discrete events, such as individual action potentials or multi-unit activity.

Emergence of Narrowband High Frequency Oscillations from Asynchronous, Uncoupled Neural Firing

Previous experimental studies have demonstrated the emergence of narrowband local field potential oscillations during epileptic seizures in which the underlying neural activity appears to be completely asynchronous. We derive a mathematical model explaining how this counterintuitive phenomenon may occur, showing that a population of independent, completely asynchronous neurons may produce narrowband oscillations if each neuron fires quasi-periodically, without requiring any intrinsic oscillatory cells or feedback inhibition. This quasi-periodicity can occur through cells with similar frequency-current ([Formula: see text]-[Formula: see text]) curves receiving a similar, high amount of uncorrelated synaptic noise. Thus, this source of oscillatory behavior is distinct from the usual cases (pacemaker cells entraining a network, or oscillations being an inherent property of the network structure), as it requires no oscillatory drive nor any specific network or cellular properties other than cells that repetitively fire with continual stimulus. We also deduce bounds on the degree of variability in neural spike-timing which will permit the emergence of such oscillations, both for action potential- and postsynaptic potential-dominated LFPs. These results suggest that even an uncoupled network may generate collective rhythms, implying that the breakdown of inhibition and high synaptic input often observed during epileptic seizures may generate narrowband oscillations. We propose that this mechanism may explain why so many disparate epileptic and normal brain mechanisms can produce similar high frequency oscillations.

Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings

The rate of interictal high frequency oscillations (HFOs) is a promising biomarker of the seizure onset zone, though little is known about its consistency over hours to days. Here we test whether the highest HFO-rate channels are consistent across different 10-min segments of EEG during sleep. An automated HFO detector and blind source separation are applied to nearly 3000 total hours of data from 121 subjects, including 12 control subjects without epilepsy. Although interictal HFOs are significantly correlated with the seizure onset zone, the precise localization is consistent in only 22% of patients. The remaining patients either have one intermittent source (16%), different sources varying over time (45%), or insufficient HFOs (17%). Multiple HFO networks are found in patients with both one and multiple seizure foci. These results indicate that robust HFO interpretation requires prolonged analysis in context with other clinical data, rather than isolated review of short data segments.High frequency oscillations (HFO) are a promising biomarker for identifying epileptogenic zones without the need to monitor spontaneous seizure episodes. Here the authors report that there is much variability in the location of HFOs offering a note of caution toward using HFO locations from short recordings as a guide for surgery.

Chemical biomarkers of epileptogenesis and ictogenesis in experimental epilepsy

Epilepsy produces chronic chemical changes induced by altered cellular structures, and acute ones produced by conditions leading into individual seizures. Here, we aim to quantify 24 molecules simultaneously at baseline and during periods of lowered seizure threshold in rats. Using serial hippocampal microdialysis collections starting two weeks after the pilocarpine-induced status epilepticus, we evaluated how this chronic epilepsy model affects molecule levels and their interactions. Then, we quantified the changes occurring when the brain moves into a pro-seizure state using a novel model of physiological ictogenesis. Compared with controls, pilocarpine animals had significantly decreased baseline levels of adenosine, homovanillic acid, and serotonin, but significantly increased levels of choline, glutamate, phenylalanine, and tyrosine. Step-wise linear regression identified that choline, homovanillic acid, adenosine, and serotonin are the most important features to characterize the difference in the extracellular milieu between pilocarpine and control animals. When increasing the hippocampal seizure risk, the concentrations of normetanephrine, serine, aspartate, and 5-hydroxyindoleacetic acid were the most prominent; however, there were no specific, consistent changes prior to individual seizures.

Control of in vivo ictogenesis via endogenous synaptic pathways

The random nature of seizures poses difficult challenges for epilepsy research. There is great need for a reliable method to control the pathway to seizure onset, which would allow investigation of the mechanisms of ictogenesis and optimization of treatments. Our hypothesis is that increased random afferent synaptic activity (i.e. synaptic noise) within the epileptic focus is one endogenous method of ictogenesis. Building upon previous theoretical and in vitro work showing that synaptic noise can induce seizures, we developed a novel in vivo model of ictogenesis. By increasing the excitability of afferent connections to the hippocampus, we control the risk of temporal lobe seizures during a specific time period. The afferent synaptic activity in the hippocampus was modulated by focal microinjections of potassium chloride into the nucleus reuniens, during which the risk of seizure occurrence increased substantially. The induced seizures were qualitatively and quantitatively indistinguishable from spontaneous ones. This model thus allows direct control of the temporal lobe seizure threshold via endogenous pathways, providing a novel tool in which to investigate the mechanisms and biomarkers of ictogenesis, test for seizure threshold, and rapidly tune antiseizure treatments.

Author Correction: Control of in vivo ictogenesis via endogenous synaptic pathways

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.