Continuous High Frequency Activity: A peculiar SEEG pattern related to specific brain regions

Melani, Federico; Zelmann, Rina; Mari, Francesco; Gotman, Jean

doi:10.1016/j.clinph.2012.11.016

Cited by 60 publications

(68 citation statements)

References 31 publications

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“…Despite the efforts of many investigators, the clinical utility of interictal HFOs still remains uncertain (Jobst, 2013), particularly because HFOs are frequently generated by nonepileptic visual, somatosensory, motor, and auditory cortices during resting periods (Blanco et al, 2011; Fukushima et al, 2012; Nagasawa et al, 2012; Melani et al, 2013; Wang et al, 2013; Alkawadri et al, 2014; van’t Klooster et al, 2015). The current recommendation is that one should not determine the resection margin solely based on the occurrence rate or spectral frequency band of interictal HFOs (Engel et al, 2009; Asano et al, 2013; Gliske et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Interictal high-frequency oscillations generated by seizure onset and eloquent areas may be differentially coupled with different slow waves

Nonoda

Miyakoshi

Ojeda

et al. 2016

Clinical Neurophysiology

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OBJECTIVE High-frequency oscillations (HFOs) can be spontaneously generated by seizure-onset and functionally-important areas. We determined if consideration of the spectral frequency bands of coupled slow-waves could distinguish between epileptogenic and physiological HFOs. METHODS We studied a consecutive series of 13 children with focal epilepsy who underwent extraoperative electrocorticography. We measured the occurrence rate of HFOs during slow-wave sleep at each electrode site. We subsequently determined the performance of HFO rate for localization of seizure-onset sites and undesirable detection of nonepileptic sensorimotor-visual sites defined by neurostimulation. We likewise determined the predictive performance of modulation index: MI(XHz)&(YHz), reflecting the strength of coupling between amplitude of HFOsXHz and phase of slow-waveYHz. The predictive accuracy was quantified using the area under the curve (AUC) on receiver-operating characteristics analysis. RESULTS Increase in HFO rate localized seizure-onset sites (AUC≥0.72; p<0.001), but also undesirably detected nonepileptic sensorimotor-visual sites (AUC≥0.58; p<0.001). Increase in MI(HFOs)&(3–4Hz) also detected both seizure-onset (AUC≥0.74; p<0.001) and nonepileptic sensorimotor-visual sites (AUC≥0.59; p<0.001). Increase in subtraction-MIHFOs [defined as subtraction of MI(HFOs)&(0.5–1Hz) from MI(HFOs)&(3–4Hz)] localized seizure-onset sites (AUC≥0.71; p<0.001), but rather avoided detection of nonepileptic sensorimotor-visual sites (AUC≤0.42; p<0.001). CONCLUSION Our data suggest that epileptogenic HFOs may be coupled with slow-wave3–4Hz more preferentially than slow-wave0.5–1Hz, whereas physiologic HFOs with slow-wave0.5–1Hz more preferentially than slow-wave3–4Hz during slow-wave sleep. SIGNIFICANCE Further studies in larger samples are warranted to determine if consideration of the spectral frequency bands of slow-waves coupled with HFOs can positively contribute to presurgical evaluation of patients with focal epilepsy.

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Section: Introductionmentioning

confidence: 99%

Interictal high-frequency oscillations generated by seizure onset and eloquent areas may be differentially coupled with different slow waves

Nonoda

Miyakoshi

Ojeda

et al. 2016

Clinical Neurophysiology

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“…Some have suggested a bias of larger-diameter electrodes to sample a larger volume of tissue and capture pathological (ripple-frequency) HFOs versus normal HFO [23,63,67]. Alternatively, recent work has described prolonged (> 500 ms in duration) episodes of highfrequency activity (80-250 Hz) in mesial temporal lobe and neocortex of presurgical patients [104,105]. Continuous episodes of interictal high-frequency activity itself does not appear specific to the SOZ, but compared with irregular or sporadic episodes was associated with higher rates of IIS and bursts of HFOs, which could, in part, explain the higher rates of ripple-frequency HFO occurrence on larger electrodes.…”

Section: Interictal Hfosmentioning

confidence: 99%

Electrophysiological Biomarkers of Epilepsy

2014

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In patients being evaluated for epilepsy and in animal models of epilepsy, electrophysiological recordings are carried to capture seizures to determine the existence of epilepsy. Electroencephalography recordings from the scalp, or sometimes directly from the brain, are also used to locate brain areas where seizure begins, and in surgical treatment help plan the area for resection. As seizures are unpredictable and can occur infrequently, ictal recordings are not ideal in terms of time, cost, or risk when, for example, determining the efficacy of existing or new anti-seizure drugs, evaluating potential anti-epileptogenic interventions, or for prolonged intracerebral electrode studies. Thus, there is a need to identify and validate other electrophysiological biomarkers of epilepsy that could be used to diagnose, treat, cure, and prevent epilepsy. Electroencephalography recordings in the epileptic brain contain other interictal electrophysiological disturbances that can occur more frequently than seizures, such as interictal spikes (IIS) and sharp waves, and from invasive studies using wide bandwidth recording and small diameter electrodes, the discovery of pathological high-frequency oscillations (HFOs) and microseizures. Of IIS, HFOs, and microseizures, a significant amount of recent research has focused on HFOs in the pathophysiology of epilepsy. Results from studies in animals with epilepsy and presurgical patients have consistently found a strong association between HFOs and epileptogenic brain tissue that suggest HFOs could be a potential biomarker of epileptogenicity and epileptogenesis. Here, we discuss several aspects of HFOs, as well as IIS and microseizures, and the evidence that supports their role as biomarkers of epilepsy.

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“…The authors showed that resection of areas with ripples occurring in a flat background activity correlates with a good postsurgical outcome, whereas resection of areas generating ripples in a continuously oscillating background did not show an association with the surgical outcome. Melani et al (2013) investigated continuous high frequency activity in the ripple band.…”

Section: Introductionmentioning

confidence: 99%

The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

Burnos

Frauscher

Zelmann

et al. 2016

Clinical Neurophysiology

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OBJECTIVE: We hypothesized that high frequency oscillations (HFOs) with irregular amplitude and frequency more specifically reflect epileptogenicity than HFOs with stable amplitude and frequency. METHODS: We developed a fully automatic algorithm to detect HFOs and classify them based on their morphology, with types defined according to regularity in amplitude and frequency: type 1 with regular amplitude and frequency; type 2 with irregular amplitude, which could result from filtering of sharp spikes; type 3 with irregular frequency; and type 4 with irregular amplitude and frequency. We investigated the association of different HFO types with the seizure onset zone (SOZ), resected area and surgical outcome. RESULTS: HFO rates of all types were significantly higher inside the SOZ than outside. HFO types 1 and 2 were strongly correlated to each other and showed the highest rates among all HFOs. Their occurrence was highly associated with the SOZ, resected area and surgical outcome. The automatic detection emulated visual markings with 93% true positives and 57% false detections. CONCLUSIONS: HFO types 1 and 2 similarly reflect epileptogenicity. SIGNIFICANCE: For clinical application, it may not be necessary to separate real HFOs from "false oscillations" produced by the filter effect of sharp spikes. Also for automatically detected HFOs, surgical outcome is better when locations with higher HFO rates are included in the resection. We hypothesized that high frequency oscillations (HFOs) with irregular amplitude and frequency more specifically reflect epileptogenicity than HFOs with stable amplitude and frequency. Methods:We developed a fully automatic algorithm to detect HFOs and classify them based on their morphology, with types defined according to regularity in amplitude and frequency: type 1 with regular amplitude and frequency; type 2 with irregular amplitude, which could result from filtering of sharp spikes; type 3 with irregular frequency; and type 4 with irregular amplitude and frequency. We investigated the association of different HFO types with the seizure onset zone (SOZ), resected area and surgical outcome. Results:HFO rates of all types were significantly higher inside the SOZ than outside. HFO types 1 and 2 were strongly correlated to each other and showed the highest rates among all HFOs. Their occurrence was highly associated with the SOZ, resected area and surgical outcome. The automatic detection emulated visual markings with 93% true positives and 57% false detections. Conclusions:HFO types 1 and 2 similarly reflect epileptogenicity. Significance:For clinical application, it may not be necessary to separate real HFOs from "false oscillations" produced by the filter effect of sharp spikes. Also for automatically detected HFOs, surgical outcome is better when locations with higher HFO rates are included in the resection.

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Continuous High Frequency Activity: A peculiar SEEG pattern related to specific brain regions

Cited by 60 publications

References 31 publications

Interictal high-frequency oscillations generated by seizure onset and eloquent areas may be differentially coupled with different slow waves

Interictal high-frequency oscillations generated by seizure onset and eloquent areas may be differentially coupled with different slow waves

Electrophysiological Biomarkers of Epilepsy

The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

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