Slow modulations of high-frequency activity (40–140 Hz) discriminate preictal changes in human focal epilepsy

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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Thalamocortical dysrhythmia in intraoperative recordings of focal epilepsy

Journal of Neurophysiology ◽

10.1152/jn.00079.2019 ◽

2019 ◽

Vol 121 (6) ◽

pp. 2020-2027 ◽

Cited By ~ 1

Author(s):

Daniel J. Martire ◽

Simeon Wong ◽

Mirriam Mikhail ◽

Ayako Ochi ◽

Hiroshi Otsubo ◽

...

Keyword(s):

Functional Connectivity ◽

High Frequency ◽

Large Scale ◽

Focal Epilepsy ◽

Critical Role ◽

Epileptogenic Zone ◽

Thalamocortical Dysrhythmia ◽

High Frequency Activity ◽

Frequency Coupling ◽

Cross Frequency Coupling

Resonant interactions between the thalamus and cortex subserve a critical role for maintenance of consciousness as well as cognitive functions. In states of abnormal thalamic inhibition, thalamocortical dysrhythmia (TCD) has been described. The characteristics of TCD include a slowing of resting oscillations, ectopic high-frequency activity, and increased cross-frequency coupling. Here, we demonstrate the presence of TCD in four patients who underwent resective epilepsy surgery with chronically implanted electrodes under anesthesia, continuously recording activity from brain regions at the periphery of the epileptogenic zone before and after resection. Following resection, we report an acceleration of the large-scale network resting frequency coincident with decreases in cross-frequency phase-amplitude coupling. Interregional functional connectivity in the surrounding cortex was also increased following resection of the epileptogenic focus. These findings provide evidence for the presence of TCD in focal epilepsy and highlight the importance of reciprocal thalamocortical oscillatory interactions in defining novel biomarkers for resective surgeries. NEW & NOTEWORTHY Thalamocortical dysrhythmia (TCD) occurs in the context of thalamic dysfacilitation and is characterized by slowing of resting oscillations, ectopic high-frequency activity, and cross-frequency coupling. We provide evidence for TCD in focal epilepsy by studying electrophysiological changes occurring at the periphery of the resection margin. We report acceleration of resting activity coincident with decreased cross-frequency coupling and increased functional connectivity. The study of TCD in epilepsy has implications as a biomarker and therapeutic target.

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

10.1101/2020.03.26.999425 ◽

2020 ◽

Author(s):

Zhuying Chen ◽

David B. Grayden ◽

Anthony N. Burkitt ◽

Udaya Seneviratne ◽

Wendyl J. D’Souza ◽

...

Keyword(s):

Circadian Rhythms ◽

High Frequency ◽

Focal Epilepsy ◽

Patient Specific ◽

Intracranial Eeg ◽

Electrode Implantation ◽

High Frequency Oscillations ◽

Total Data ◽

High Frequency Activity

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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Visualization of Phase-Amplitude Coupling Using Rhythmic High-Frequency Activity

10.1101/2020.08.12.247775 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hiroaki Hashimoto ◽

Hui Ming Khoo ◽

Takufumi Yanagisawa ◽

Naoki Tani ◽

Satoru Oshino ◽

...

Keyword(s):

High Frequency ◽

Focal Epilepsy ◽

Low Frequency ◽

Θ Rhythm ◽

Human Epilepsy ◽

High Frequency Activity ◽

Phase Amplitude ◽

Intracranial Electrode ◽

Intracranial Electrodes ◽

Frequency Phase

AbstractObjectiveHigh-frequency activities (HFAs) and phase-amplitude coupling (PAC) are gaining attention as key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures.MethodsWe used intracranial electrodes to record seizures of symptomatic focal epilepsy (15 seizures in seven patients). Ripples (80–250 Hz), as representative of HFAs, were evaluated along with PAC. The synchronization index (SI), representing PAC, was used to analyze the coupling between the amplitude of ripples and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were represented by circles that were scaled linearly to the power changes of ripple.ResultsThe main low frequency band modulating ictal-ripple activities was the θ band (4–8 Hz), and after completion of ictal-ripple burst, δ (1–4 Hz)-ripple PAC occurred. The video showed that fluctuation of the diameter of these circles indicated the rhythmic changes during significant high values of θ-ripple PAC.ConclusionsWe inferred that ripple activities occurring during seizure evolution were modulated by θ rhythm. In addition, we concluded that rhythmic circles’ fluctuation presented in the video represents the PAC phenomenon. Our video is thus a useful tool for understanding how ripple activity is modulated by the low-frequency phase in relation with PAC.

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A study on spike focus dependence of high-frequency activity in idiopathic focal epilepsy in childhood

Epilepsia Open ◽

10.1002/epi4.12014 ◽

2016 ◽

Vol 1 (3-4) ◽

pp. 121-129 ◽

Cited By ~ 6

Author(s):

Takashi Shibata ◽

Harumi Yoshinaga ◽

Tomoyuki Akiyama ◽

Katsuhiro Kobayashi

Keyword(s):

High Frequency ◽

Focal Epilepsy ◽

High Frequency Activity

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Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions

Scientific Reports ◽

10.1038/s41598-021-87304-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zekun Xu ◽

Eric Laber ◽

Ana-Maria Staicu ◽

B. Duncan X. Lascelles

Keyword(s):

High Frequency ◽

Activity Patterns ◽

Treatment Strategies ◽

Therapeutic Effects ◽

Activity Levels ◽

Activity Data ◽

Novel Approach ◽

High Frequency Activity ◽

Using Data ◽

Chronic Pain Conditions

AbstractOsteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model.

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