Evaluation of Spike Sorting Algorithms: Application to Human Subthalamic Nucleus Recordings and Simulations
Evaluation of Spike Sorting Algorithms: Application to Human Subthalamic Nucleus Recordings and Simulations
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DOI：
10.1016/j.neuroscience.2019.07.005
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发表时间：
2019-08
期刊：
影响因子：
3.3
通讯作者：
Jeyathevy Sukiban;N. Voges;T. Dembek;Robin Pauli;V. Visser-Vandewalle;M. Denker;Immo Weber;L. Timmermann;S. Grün
中科院分区：
文献类型：
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作者：
Jeyathevy Sukiban;N. Voges;T. Dembek;Robin Pauli;V. Visser-Vandewalle;M. Denker;Immo Weber;L. Timmermann;S. Grün
分析细胞外记录中的棘波同步性的一个重要前提是从多个单位信号中提取单个单位的活动。为了识别单个单位，根据潜在的神经元来源分离潜在的棘波(“棘波排序”)。然而，不同的排序算法会产生不一致的单元分配，这严重影响了后续的脉冲序列分析。我们的目标是确定帕金森病患者丘脑底核记录的最佳分类算法(实验数据ED)。因此，我们应用了Plexon Offline Sorter提供的各种流行算法，并对排序结果进行了评估。由于这种评估让我们不确定最好的算法，我们再次将所有方法应用于具有已知基本事实的人工数据(AD)。AD由嵌入在ED的背景噪声中的形状相似程度不同的单单元对组成。排序评价描述了各自的方法对单个单元分配的显著影响。我们发现，不同算法得到的排序具有很高的可变性，并且随着单个单元形状相似度的增加而增加。我们还发现，由此产生的发射特性存在显著差异。我们的结论是，如果将人工产物排除为未排序事件是重要的，则山谷搜索算法产生的结果最准确。如果后者不那么重要(“干净的”数据)，K-Means算法是更好的选择。我们的结果有力地证明了基于地面真实数据的标准化验证程序的必要性。这里建议的食谱非常简单，足以成为标准程序。
An important prerequisite for the analysis of spike synchrony in extracellular recordings is the extraction of single-unit activity from the multi-unit signal. To identify single units, potential spikes are separated with respect to their potential neuronal origins (‘spike sorting’). However, different sorting algorithms yield inconsistent unit assignments, which seriously influences subsequent spike train analyses. We aim to identify the best sorting algorithm for subthalamic nucleus recordings of patients with Parkinson's disease (experimental data ED). Therefore, we apply various prevalent algorithms offered by the ‘Plexon Offline Sorter’ and evaluate the sorting results. Since this evaluation leaves us unsure about the best algorithm, we apply all methods again to artificial data (AD) with known ground truth. AD consists of pairs of single units with different shape similarity embedded in the background noise of the ED. The sorting evaluation depicts a significant influence of the respective methods on the single unit assignments. We find a high variability in the sortings obtained by different algorithms that increases with single units shape similarity. We also find significant differences in the resulting firing characteristics. We conclude that Valley-Seeking algorithms produce the most accurate result if the exclusion of artifacts as unsorted events is important. If the latter is less important (‘clean’ data) the K-Means algorithm is a better option. Our results strongly argue for the need of standardized validation procedures based on ground truth data. The recipe suggested here is simple enough to become a standard procedure.