Department of Biology, Swarthmore College, Swarthmore, PA, USA.
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Nat Methods. 2022 Apr;19(4):470-478. doi: 10.1038/s41592-022-01422-5. Epub 2022 Mar 28.
Population recordings of calcium activity are a major source of insight into neural function. Large datasets require automated processing, but this can introduce errors that are difficult to detect. Here we show that popular time course-estimation algorithms often contain substantial misattribution errors affecting 10-20% of transients. Misattribution, in which fluorescence is ascribed to the wrong cell, arises when overlapping cells and processes are imperfectly defined or not identified. To diagnose misattribution, we develop metrics and visualization tools for evaluating large datasets. To correct time courses, we introduce a robust estimator that explicitly accounts for contaminating signals. In one hippocampal dataset, removing contamination reduced the number of place cells by 15%, and 19% of place fields shifted by over 10 cm. Our methods are compatible with other cell-finding techniques, empowering users to diagnose and correct a potentially widespread problem that could alter scientific conclusions.
群体钙活动记录是深入了解神经功能的主要来源。大型数据集需要自动化处理，但这可能会引入难以检测的错误。在这里，我们表明，流行的时程估计算法通常包含大量的错误归因错误，影响 10-20%的瞬态。当重叠的细胞和过程定义不完美或未被识别时，就会发生荧光被错误归因于错误的细胞的错误归因。为了诊断错误归因，我们开发了用于评估大型数据集的指标和可视化工具。为了纠正时程，我们引入了一个稳健的估计器，该估计器明确考虑了污染信号。在一个海马数据集，去除污染减少了 15%的位置细胞，19%的位置场移动超过 10 厘米。我们的方法与其他细胞检测技术兼容，使用户能够诊断和纠正可能改变科学结论的潜在广泛问题。
