AECuration:自动事件管理的尖峰排序。

Xiang Li, Jay W Reddy, Vishal Jain, Mats Forssell, Zabir Ahmed, Maysamreza Chamanzar
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摘要

刺突分选是一种常用的分析方法,用于从细胞外记录中识别单单位和多单位。细胞外记录包含混合的信号成分,如神经和非神经事件,可能是由于运动和呼吸的伪影或电干扰。使用简单的阈值交叉方法识别单个和多单元尖峰可能导致区分实际神经尖峰与非神经尖峰的不确定性。从峰值分类结果中对神经和非神经单元进行分类的传统方法是由受过训练的人手动管理。这种主观的方法受到人为错误和可变性的影响,并且由于实验细胞外记录中缺乏基础事实而进一步复杂化。此外,由于细胞外数据集的规模和复杂性不断增长,人工管理过程非常耗时,并且变得难以处理。为了解决这些挑战,我们首次提出了一种基于自动编码器模型的新型自动管理方法,该模型基于模拟的细胞外尖峰波形的特征进行训练。然后将该模型应用于实验电生理数据集,其中重构误差用作分类神经和非神经尖峰的度量。作为传统频域和统计技术的替代方案,我们提出的方法提供了一个时域评估模型,可以基于学习到的时域特征自动分析细胞外记录。该模型在不需要任何再训练的情况下应用于现实世界的细胞外数据集,表现出优异的性能和吞吐量,突出了其泛化性。该方法可以作为预处理过滤步骤或后处理策展方法集成到尖峰排序管道中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AECuration: automated event curation for spike sorting.

Objective. This paper discusses a novel method for automating the curation of neural spike events detected from neural recordings using spike sorting methods. Spike sorting seeks to identify isolated neural events from extracellular recordings. This is critical for interpretation of electrophysiology recordings in neuroscience studies. Spike sorting analysis is vulnerable to errors because of non-neural events, such as experimental artifacts or electrical interference. To improve the specificity of spike sorting results, a manual postprocessing curation is typically used to examine the detected events and identify neural spikes based on their specific features. However, this manual curation process is subjective, prone to human errors and not scalable, especially for large datasets.Approach. To address these challenges, we introduce AECuration, a novel automatic curation method based on an autoencoder model trained on features of simulated extracellular spike waveforms. Using reconstruction error as a performance metric, our method classifies neural and non-neural events in experimental electrophysiology datasets.Main results. This paper demonstrates that AECuration can classify neural events with 97.46% accuracy on synthetic datasets. Moreover, our method can improve the sensitivity of different spike sorting pipelines on datasets with ground-truth recordings by up to 20%. The ratio of clustered units with low interspike interval violation rates is improved from 55.3% to 85.5% as demonstrated using our in-house experimental dataset.Significance. AEcuration is a time-domain evaluation method that automates the analysis of extracellular recordings based on learned time-domain features. Once trained on a synthetic dataset, this method can be applied to real extracellular datasets without the need for re-training. This highlights the generalizability of AECuration. It can be readily integrated with existing spike sorting pipelines as a preprocessing filtering or a postprocessing curation step to improve the overall accuracy and efficiency.

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