Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe.

ORIGINAL RESEARCH published: 19 October 2020 doi: 10.3389/fneur.2020.563577 Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe Aljoscha Thomschewski 1,2,3*, Nathalie Gerner 1 , Patrick B. Langthaler 1,2 , Eugen Trinka 1 , Arne C. Bathke 2,4 , Jürgen Fell 5 and Yvonne Höller 6 1 Department of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria, 2 Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria, 3 Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria, 4 Intelligent Data Analytics Lab Salzburg, Paris-Lodron University of Salzburg, Salzburg, Austria, 5 Department of Epileptology, University Hospital Bonn, Bonn, Germany, 6 Faculty of Psychology, University of Akureyri, Akureyri, Iceland Edited by: Julia Jacobs, University of Freiburg Medical Center, Germany Reviewed by: Giovanni Pellegrino, McGill University, Canada Xiaofeng Yang, Beijing Institute for Brain Disorders, China *Correspondence: Aljoscha Thomschewski Specialty section: This article was submitted to Epilepsy, a section of the journal Frontiers in Neurology Received: 22 May 2020 Accepted: 26 August 2020 Published: 19 October 2020 Citation: Thomschewski A, Gerner N, Langthaler PB, Trinka E, Bathke AC, Fell J and Höller Y (2020) Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe. Front. Neurol. 11:563577. doi: 10.3389/fneur.2020.563577 Frontiers in Neurology | www.frontiersin.org 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. 1 October 2020 | Volume 11 | Article 563577 Thomschewski et al. Detection Approaches of Temporal 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. Keywords: high-frequency oscillations, visual memory, invasive EEG, electroencephalography, epilepsy 1. INTRODUCTION In the study at hand, we analyzed such a dataset. 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. For this purpose, we assessed for both detection approaches: (1) whether ripple occurrence rates during encoding or retrieval phases differed between correct and incorrect responses in the memory task; (2) whether the event rates detected during encoding were predictive for the performance in the subsequent retrieval trials on a trial level; and (3) whether the amount of detected events was related to the response times in the memory task. We hypothesized the results to differ between automatically detected and manually detected events. Assuming that automatic detection results in less valid detections, we hypothesized that event rates revealed no or less of an association with memory performance as compared to events detected visually. Confirming our hypothesis would emphasize the importance for an accurate detection in order to differentiate physiologic, e.g., memory-related, from pathologic HFOs. High frequency oscillations (HFOs) have gained considerable interest amongst neurologists and neuroscientists in the last decade. These relatively new electroencephalographic (EEG) markers are defined as single events of at least four oscillations with a frequency above 80 Hz that clearly stand out from the background EEG (1). Classically, 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. Hence, the first findings of HFOs stem from invasive EEG (iEEG) recordings with micro- or macroelectrodes (2–7). 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 (10–12). Besides there association with epilepsy, several studies also suggested an existence of a second HFO population, reflecting physiologic processes (3, 13–17). Especially entorhinal and hippocampal ripples have been associated with memory consolidation in animals (18, 19) and humans (20–23). Albeit these numerous investigations, the detection of HFOs remains a highly debatable subject, and many aspects need to be considered. Besides tech (...truncated)