Abstract
Background: High frequency oscillations (HFOs) have attracted great interest among neuroscientists and epileptologists in recent years. Not only has their occurrence been linked to epileptogenesis, but also to physiologic processes, such as memory consolidation. There are at least two big challenges for HFO research. First, detection, when performed manually, is time consuming and prone to rater biases, but when performed automatically, it is biased by artifacts mimicking HFOs. Second, distinguishing physiologic from pathologic HFOs in patients with epilepsy is problematic. Here we automatically and manually detected HFOs in intracranial EEGs (iEEG) of patients with epilepsy, recorded during a visual memory task in order to assess the feasibility of the different detection approaches to identify task-related ripples, supporting the physiologic nature of HFOs in the temporal lobe.Methods: Ten patients with unclear seizure origin and bilaterally implanted macroelectrodes took part in a visual memory consolidation task. In addition to iEEG, scalp EEG, electrooculography (EOG), and facial electromyography (EMG) were recorded. iEEG channels contralateral to the suspected epileptogenic zone were inspected visually for HFOs. Furthermore, HFOs were marked automatically using an RMS detector and a Stockwell classifier. We compared the two detection approaches and assessed a possible link between task performance and HFO occurrence during encoding and retrieval trials.Results: HFO occurrence rates were significantly lower when events were marked manually. The automatic detection algorithm was greatly biased by filter-artifacts. Surprisingly, EOG artifacts as seen on scalp electrodes appeared to be linked to many HFOs in the iEEG. Occurrence rates could not be associated to memory performance, and we were not able to detect strictly defined “clear” ripples.Conclusion: Filtered graphoelements in the EEG are known to mimic HFOs and thus constitute a problem. So far, in invasive EEG recordings mostly technical artifacts and filtered epileptiform discharges have been considered as sources for these “false” HFOs. The data at hand suggests that even ocular artifacts might bias automatic detection in invasive recordings. Strict guidelines and standards for HFO detection are necessary in order to identify artifact-derived HFOs, especially in conditions when cognitive tasks might produce a high amount of artifacts.
Highlights
High frequency oscillations (HFOs) have gained considerable interest amongst neurologists and neuroscientists in the last decade
As these recordings are only performed during presurgical evaluation in patients with drug resistant epilepsies, their occurrence has naturally been studied and linked to epilepsy and many findings indicate a link between HFOs and epileptogenity, both during ictal [8, 9] and interictal states [10,11,12]
Using a dataset described by Axmacher et al [20], we investigated stimulusinduced HFOs during encoding and retrieval to demonstrate possible differences between the two approaches of HFO detection, as well as to take advantage of the high sensitivity of automatic detectors and the specificity of a manual review when trying to link ripple occurrence to memory performance
Summary
High frequency oscillations (HFOs) have gained considerable interest amongst neurologists and neuroscientists in the last decade. HFOs have further been divided into two subgroups: ripples (80–250 Hz) and fast ripples (250– 500 Hz; 2) Given these criteria, a high signal-to-noise ratio is key when attempting to detect HFOs. the first findings of HFOs stem from invasive EEG (iEEG) recordings with micro- or macroelectrodes [2,3,4,5,6,7]. High frequency oscillations (HFOs) have attracted great interest among neuroscientists and epileptologists in recent years Has their occurrence been linked to epileptogenesis, and to physiologic processes, such as memory consolidation. We automatically and manually detected HFOs in intracranial EEGs (iEEG) of patients with epilepsy, recorded during a visual memory task in order to assess the feasibility of the different detection approaches to identify task-related ripples, supporting the physiologic nature of HFOs in the temporal lobe
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