Spatiotemporal patterns of high-frequency activity (80-170 Hz) in long-term intracranial EEG

AbstractObjectiveTo assess the variability in the rates and locations of high-frequency activity (HFA) and epileptiform spikes after electrode implantation, and to examine the long-term patterns of HFA using ambulatory intracranial EEG (iEEG) recordings.MethodsContinuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy were used in this study. HFA was defined as high-frequency events with amplitudes clearly larger than the background, which was automatically detected using a custom algorithm. High-frequency oscillations (HFOs) were also visually annotated by three neurologists in randomly sampled segments of the total data. The automatically detected HFA was compared with the visually marked HFOs. The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases.ResultsHFA was a more general event that encompassed HFOs manually annotated by different reviewers. HFA and spike rates had high amounts of intra- and inter-patient variability. The rates and locations of HFA and spikes took up to weeks to stabilize after electrode implantation in some patients. Both HFA and spike rates showed strong circadian rhythms in all patients and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes.ConclusionsAnalysis of HFA and epileptiform spikes should account for post-implantation variability. Like seizures, HFA and epileptiform spikes show circadian rhythms. However, the circadian profiles can vary spatially within patients and their correlations to seizures are patient-specific.

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Spatiotemporal Patterns of High-Frequency Activity (80-170 Hz) in Long-term Intracranial EEG

Neurology ◽

10.1212/wnl.0000000000011408 ◽

2020 ◽

pp. 10.1212/WNL.0000000000011408

Author(s):

Zhuying Chen ◽

David B. Grayden ◽

Anthony N. Burkitt ◽

Udaya Seneviratne ◽

Wendyl J. D'Souza ◽

...

Keyword(s):

Circadian Rhythms ◽

High Frequency ◽

Focal Epilepsy ◽

High Amplitude ◽

Oscillatory Behavior ◽

Patient Specific ◽

Intracranial Eeg ◽

Large Variability ◽

High Frequency Activity

Objective:To determine the utility of high-frequency activity (HFA) and epileptiform spikes as biomarkers for epilepsy, we examined the variability in their rates and locations using long-term ambulatory intracranial EEG (iEEG) recordings.Methods:This study used continuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy. HFA was defined as 80-170 Hz events with amplitudes clearly larger than the background, which was automatically detected using a custom algorithm. The automatically detected HFA was compared with visually annotated high-frequency oscillations (HFOs). The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases.Results:HFA included manually annotated HFOs and high-amplitude events occurring in the 80-170 Hz range without observable oscillatory behavior. HFA and spike rates had high amounts of intra- and inter-patient variability. Rates of HFA and spikes had large variability after electrode implantation in most of the patients. Locations of HFA and/or spikes varied up to weeks in more than one-third of the patients. Both HFA and spike rates showed strong circadian rhythms in all patients and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes.Conclusions:Analysis of HFA and epileptiform spikes should consider post-implantation variability. HFA and epileptiform spikes, like seizures, show circadian rhythms. However, the circadian profiles can vary spatially within patients and their correlations to seizures are patient-specific.

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Faculty Opinions recommendation of Value of electrical stimulation and high frequency oscillations (80-500 Hz) in identifying epileptogenic areas during intracranial EEG recordings.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.3024973.2703077 ◽

2010 ◽

Author(s):

Roderick Duncan

Keyword(s):

Electrical Stimulation ◽

High Frequency ◽

Intracranial Eeg ◽

High Frequency Oscillations ◽

Eeg Recordings

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Interictal high frequency oscillations (HFOs) in patients with focal epilepsy and normal MRI

Clinical Neurophysiology ◽

10.1016/j.clinph.2011.06.004 ◽

2012 ◽

Vol 123 (1) ◽

pp. 100-105 ◽

Cited By ~ 48

Author(s):

Luciana Andrade-Valença ◽

Francesco Mari ◽

Julia Jacobs ◽

Maeike Zijlmans ◽

André Olivier ◽

...

Keyword(s):

High Frequency ◽

Focal Epilepsy ◽

High Frequency Oscillations

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Patient-specific optimization of automated detection improves seizure onset zone localization based on high frequency oscillations

10.1101/2020.09.14.297309 ◽

2020 ◽

Author(s):

Casey L. Trevino ◽

Jack J. Lin ◽

Indranil Sen-Gupta ◽

Beth A. Lopour

Keyword(s):

High Frequency ◽

Classification Accuracy ◽

Patient Specific ◽

Seizure Onset ◽

Data Set ◽

Localization Accuracy ◽

High Frequency Oscillations ◽

Seizure Onset Zone ◽

Wide Range ◽

The Impact

AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptogenicity, and automated algorithms are critical tools for their detection. However, previously validated algorithms often exhibit decreased HFO detection accuracy when applied to a new data set, if the parameters are not optimized. This likely contributes to decreased seizure localization accuracy, but this has never been tested. Therefore, we evaluated the impact of parameter selection on seizure onset zone (SOZ) localization using automatically detected HFOs. We detected HFOs in intracranial EEG from twenty medically refractory epilepsy patients with seizure free surgical outcomes using an automated algorithm. For each patient, we assessed classification accuracy of channels inside/outside the SOZ using a wide range of detection parameters and identified the parameters associated with maximum classification accuracy. We found that only three out of twenty patients achieved maximal localization accuracy using conventional HFO detection parameters, and optimal parameter ranges varied significantly across patients. The parameters for amplitude threshold and root-mean-square window had the greatest impact on SOZ localization accuracy; minimum event duration and rejection of false positive events did not significantly affect the results. Using individualized optimal parameters led to substantial improvements in localization accuracy, particularly in reducing false positives from non-SOZ channels. We conclude that optimal HFO detection parameters are patient-specific, often differ from conventional parameters, and have a significant impact on SOZ localization. This suggests that individual variability should be considered when implementing automatic HFO detection as a tool for surgical planning.

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Evoked versus spontaneous high frequency oscillations in the chronic electrocorticogram in focal epilepsy

Clinical Neurophysiology ◽

10.1016/j.clinph.2017.01.017 ◽

2017 ◽

Vol 128 (5) ◽

pp. 858-866 ◽

Cited By ~ 7

Author(s):

M.A. van 't Klooster ◽

N.E.C. van Klink ◽

D. van Blooijs ◽

C.H. Ferrier ◽

K.P.J. Braun ◽

...

Keyword(s):

High Frequency ◽

Focal Epilepsy ◽

High Frequency Oscillations

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High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy

Scientific Reports ◽

10.1038/s41598-019-52700-w ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Ece Boran ◽

Johannes Sarnthein ◽

Niklaus Krayenbühl ◽

Georgia Ramantani ◽

Tommaso Fedele

Keyword(s):

Seizure Frequency ◽

Epilepsy Surgery ◽

High Frequency ◽

Focal Epilepsy ◽

Low Noise ◽

Drug Resistant ◽

Scalp Eeg ◽

High Frequency Oscillations ◽

Custom Made ◽

Active Epilepsy

Abstract High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2 = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.

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Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy

Brain ◽

10.1093/brain/awx374 ◽

2018 ◽

Vol 141 (3) ◽

pp. 713-730 ◽

Cited By ~ 24

Author(s):

Su Liu ◽

Candan Gurses ◽

Zhiyi Sha ◽

Michael M Quach ◽

Altay Sencer ◽

...

Keyword(s):

High Frequency ◽

Focal Epilepsy ◽

Machine Learning Techniques ◽

Seizure Onset ◽

Functional Regions ◽

Clinical Biomarkers ◽

High Frequency Oscillations ◽

Potential Association ◽

Waveform Similarity ◽

Seizure Onset Zones

Abstract High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80–500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers. 5721572971001 awx374media1 5721572971001

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