Thomschewski Aljoscha, Gerner Nathalie, Langthaler Patrick B, Trinka Eugen, Bathke Arne C, Fell Jürgen, Höller Yvonne
Department of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria.
Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria.
Front Neurol. 2020 Oct 19;11:563577. doi: 10.3389/fneur.2020.563577. eCollection 2020.
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. 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. 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. 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.
近年来，高频振荡（HFOs）引起了神经科学家和癫痫学家的极大兴趣。其出现不仅与癫痫发生有关，还与诸如记忆巩固等生理过程相关。HFO研究至少面临两大挑战。其一，手动检测HFO耗时且容易出现评分者偏差，而自动检测则会受到模仿HFO的伪迹影响。其二，区分癫痫患者的生理性和病理性HFO存在问题。在此，我们对癫痫患者在视觉记忆任务期间记录的颅内脑电图（iEEG）中的HFO进行了自动和手动检测，以评估不同检测方法识别与任务相关的涟漪的可行性，支持颞叶中HFO的生理性本质。十名癫痫发作起源不明且双侧植入宏观电极的患者参与了视觉记忆巩固任务。除iEEG外，还记录了头皮脑电图、眼电图（EOG）和面部肌电图（EMG）。对疑似致痫区对侧的iEEG通道进行视觉检查以寻找HFO。此外，使用均方根（RMS）检测器和斯托克韦尔分类器自动标记HFO。我们比较了两种检测方法，并评估了编码和检索试验期间任务表现与HFO出现之间的可能联系。手动标记事件时，HFO出现率显著更低。自动检测算法受到滤波伪迹的极大影响。令人惊讶的是，头皮电极上出现的EOG伪迹似乎与iEEG中的许多HFO有关。出现率与记忆表现无关，并且我们无法检测到严格定义的“清晰”涟漪。脑电图中经过滤波的图形元素已知会模仿HFO，因此构成一个问题。到目前为止，在侵入性脑电图记录中，大多将技术伪迹和经过滤波的癫痫样放电视为这些“假”HFO的来源。现有数据表明，即使是眼部伪迹也可能会使侵入性记录中的自动检测产生偏差。为了识别源自伪迹的HFO，尤其是在认知任务可能产生大量伪迹的情况下，需要严格的HFO检测指南和标准。
