Dehnen Gert, Kehl Marcel S, Darcher Alana, Müller Tamara T, Macke Jakob H, Borger Valeri, Surges Rainer, Mormann Florian
Department of Epileptology, University of Bonn Medical Center, Venusberg-Campus 1, 53127 Bonn, Germany.
Computational Neuroengineering, Department of Electrical and Computerengineering, TU Munich, 80333 Munich, Germany.
Brain Sci. 2021 Jun 8;11(6):761. doi: 10.3390/brainsci11060761.
Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.
在行为中的人类受试者大脑中进行单神经元记录，为推进我们对认知神经机制的理解提供了独特的机会。这些记录仅在医疗中心的诊断或治疗过程中进行。与动物实验室环境相比，医疗仪器的存在以及医院环境的其他方面限制了对电噪声的控制。在这里，我们强调了人类单神经元记录中不同记录通道上同时出现尖峰事件的发生率增加的问题。这些同时发生的事件大多在先前标记为伪迹的簇中被检测到，并且显示出相似的波形。这些事件可能源于共同的外部噪声源，或者源于从同一神经元记录活动的不同微电极。为了解决重复记录事件的问题，我们引入了一种开源算法，根据它们的同步性和波形相似性来识别这些人为的尖峰事件。将我们的方法应用于人类单神经元记录的综合数据集，我们证明我们的算法可以显著提高这些记录的数据质量。基于我们的发现，我们认为未来在嘈杂条件下记录的单神经元活动研究应采用此类算法来提高数据质量。
