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AECuration:用于尖峰排序的自动事件筛选

AECuration: automated event curation for spike sorting.

作者信息

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

机构信息

Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, United States of America.

出版信息

J Neural Eng. 2025 Mar 24;22(2):026027. doi: 10.1088/1741-2552/adaa1c.

Abstract

. 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.. 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.. 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.. 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.

摘要

本文讨论了一种新颖的方法,用于自动整理使用尖峰分类方法从神经记录中检测到的神经尖峰事件。尖峰分类旨在从细胞外记录中识别孤立的神经事件。这对于神经科学研究中电生理记录的解释至关重要。由于非神经事件,如实验伪迹或电干扰,尖峰分类分析容易出错。为了提高尖峰分类结果的特异性,通常使用手动后处理整理来检查检测到的事件,并根据其特定特征识别神经尖峰。然而,这种手动整理过程是主观的,容易出现人为错误且不可扩展,尤其是对于大型数据集。为了应对这些挑战,我们引入了AECuration,这是一种基于在模拟细胞外尖峰波形特征上训练的自动编码器模型的新颖自动整理方法。使用重建误差作为性能指标,我们的方法对实验电生理数据集中的神经和非神经事件进行分类。本文表明,AECuration在合成数据集上对神经事件的分类准确率可达97.46%。此外,我们的方法可以将不同尖峰分类管道在有真实记录的数据集上的灵敏度提高多达20%。使用我们的内部实验数据集表明,具有低尖峰间隔违反率的聚类单元比例从55.3%提高到了85.5%。AEcuration是一种时域评估方法,它基于学习到的时域特征自动分析细胞外记录。一旦在合成数据集上训练,该方法可以应用于真实的细胞外数据集而无需重新训练。这突出了AECuration的通用性。它可以很容易地与现有的尖峰分类管道集成,作为预处理过滤或后处理整理步骤,以提高整体准确性和效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/981b/11931169/8028469dd877/jneadaa1cf1_hr.jpg

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