High-frequency oscillations as a new biomarker in epilepsy

Abstract

The discovery that electroencephalography (EEG) contains useful information at frequencies above the traditional 80Hz limit has had a profound impact on our understanding of brain function. In epilepsy, high-frequency oscillations (HFOs, >80Hz) have proven particularly important and useful. This literature review describes the morphology, clinical meaning, and pathophysiology of epileptic HFOs. To record HFOs, the intracranial EEG needs to be sampled at least at 2,000Hz. The oscillatory events can be visualized by applying a high-pass filter and increasing the time and amplitude scales, or EEG time-frequency maps can show the amount of high-frequency activity. HFOs appear excellent markers for the epileptogenic zone. In patients with focal epilepsy who can benefit from surgery, invasive EEG is often required to identify the epileptic cortex, but current information is sometimes inadequate. Removal of brain tissue generating HFOs has been related to better postsurgical outcome than removing the seizure onset zone, indicating that HFOs may mark cortex that needs to be removed to achieve seizure control. The pathophysiology of epileptic HFOs is challenging, probably involving populations of neurons firing asynchronously. They differ from physiological HFOs in not being paced by rhythmic inhibitory activity and in their possible origin from population spikes. Their link to the epileptogenic zone argues that their study will teach us much about the pathophysiology of epileptogenesis and ictogenesis. HFOs show promise for improving surgical outcome and accelerating intracranial EEG investigations. Their potential needs to be assessed by future research.

FIGURE 1

Examples of HFOs. (A) HFOs recorded with depth macroelectrode in human (1–4) and rat (5–7) hippocampal area. (1) Raw intracranial EEG with sharp wave from human hippocampal area (macroelectrode). (2 and 3) Filtered with high-pass filter of 80Hz and 250Hz. Note the differences in amplitude scales. Such an event would not stand out in normal EEG. (4) Wavelet transform of frequencies up to 500Hz. (5) Raw intracranial EEG data from rat with right intrahippocampal injection of tetanus toxin (microelectrode). A fast ripple with peak frequency 359Hz followed by activity at 240Hz is visible in the raw data. (6) Filtered with high-pass filter of 100Hz. (7) Spectrogram up to 500Hz after Fourier transformation. (B) HFOs recorded with depth and subdural macroelectrodes, in mesiotemporal areas and neocortical areas in patients with epilepsy. For each event the display shows the standard EEG signal, the same signal with extended time scale and this signal after 80Hz and 250Hz high-pass filtering. Different examples are shown from different sites. This illustrates that all combinations are possible: spike with ripple and fast ripple (1+4), ripple and fast ripple without spike (2), spike with ripple without fast ripple (3+5) and fast ripple without ripple or spike (6). An asterisk (*) means that the event was marked at this frequency. EEG = electroencephalogram; FRs = fast ripples; HFO = high-frequency oscillation.

FIGURE 2

Examples of EEG patterns and problems that can be encountered when filtering the EEG with a high-pass filter. (A) Baseline signal. First, 2 temporal neocortical channels with high-order FIR high-pass filter of 80Hz, extension of time and amplitude of 1μV/mm are shown. This is how the baseline should look. The period is shown from recording onset and shows the transient response of the filter (time required for the filter to settle). The same 2 channels are shown after IIR high-pass filter. In contrast to the FIR filter, the IIR filter has a longer transient response at the beginning of the trace and is not as effective in removing activity below 80Hz. IIR filters can be made more effective by increasing their order but IIR filters of high order can create important phase distortions, thus making the signal difficult to recognize. Beneath the channels are shown with a FIR low-order high-pass filter, where lower frequencies are not as well removed compared to a higher-order filter. Beneath the channels are shown with a FIR low-order high-pass filter, where lower frequencies are preserved compared to high order. The low-frequency drift makes the channels hard to assess, especially because with longer recording periods and with more activity the drift becomes even greater. (B) Examples of continuous artifacts that can be encountered. Channel 1 and 2 show channels with loose electrode connections. The third channel is a normal neocortical channel for comparison. Note that the amplitude is 10 times lower than the (normal) examples shown in A. The last channel shows artifact at 50Hz and harmonics, which can be recognized because of the regular pattern. (C) Examples of short-lasting artifacts. (1) The first channel shows a sharp artifact in a malfunctioning channel. Usually these artifacts are very sharp, which is not seen in regular baseline or HFOs. (2) The second and third channel show muscle artifact. This can be difficult to distinguish from HFOs (fourth channel), but can be recognized because it occurs repeatedly and simultaneously on channels that are superficial; ie, close to the skull (fifth channel). Also, muscle artifacts often show a less regular pattern than HFOs. (3) The 4 channels show short-lasting artifacts due to preoperative movement of the electrodes. This is hard to distinguish from HFO, but only happens during surgery and can then be noted. Also, the artifact can be recognized as it occurs over multiple channels and has sharp components. (4) Artifact due to a single-pulse stimulation of the electrode. Note the greater amplitude. EEG = electroencephalogram; FIR = finite impulse response; HFO = high-frequency oscillation; IIR = infinite impulse response.