在尖端排序的电生理学数据中评估交叉污染
Jack P Vincent1,2, Michael N Economo3,2,4
1Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215.
eNeuro
|August 2, 2024
概括
一个新的分析解决方案预测了神经记录中的间接间隔 (ISI) 违规的错误发现率 (FDR). 这种方法考虑了发射速度和神经元相关性等因素,改善了尖端分类准确性评估.
科学领域:
- 计算神经科学是一种神经科学.
- 电子生理学 电子生理学
- 数据分析 数据分析
背景情况:
- 细胞外电生理学可以同时记录成千上万个神经元,需要先进的尖端分类计算方法.
- 评估尖端分类精度,特别是错误发现率 (FDR),对于可靠的神经数据解释至关重要.
- 间距间隔 (ISI) 违规通常用于估计FDR,但它们的精确关系和最佳使用仍然不清楚.
研究的目的:
- 开发和验证一个分析解决方案,直接从ISI违规率 (ISIv) 预测尖端排序FDR.
- 调查ISIv和FDR之间的非线性关系,考虑影响这种关联的因素.
- 为研究人员提供一个以原则为基础的方法,以评估大规模电生理学数据集中的尖端分类准确性.
主要方法:
- 基于ISIv,开发了一个分析模型来预测FDR.
- 使用in silico蒙特卡洛模拟验证了模型.
- 将模型应用于从小鼠记录中公开可用的细胞外电生理学数据集.
主要成果:
- ISIv和FDR之间的关系高度非线性,受到发射频率,神经元活动相关性和污染物神经元数量的影响.
- 公共小鼠数据集中预测的FDR中位数差异很大,从3.1%到50.0%不等.
- 虽然由于随机性和参数不确定性,预测单个集群的FDR是具有挑战性的,但精确的人口水平FDR估计是可行的.
结论:
- 开发的分析解决方案提供了一种可靠的方法,用于估计ISI违规行为中的峰值分类FDR.
- 了解非线性依赖是正确解释ISIv作为质量指标的关键.
- 这项工作提供了一种以原则为基础的方法,以提高尖端排序精度评估,用于大规模神经记录的日益增长领域.
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