Jean-Richard-Dit-Bressel Philip, Clifford Colin W G, McNally Gavan P
School of Psychology, University of New South Wales, Sydney, NSW, Australia.
Front Mol Neurosci. 2020 Feb 6;13:14. doi: 10.3389/fnmol.2020.00014. eCollection 2020.
Fiber photometry has enabled neuroscientists to easily measure targeted brain activity patterns in awake, freely behaving animal. A focus of this technique is to identify functionally-relevant changes in activity around particular environmental and/or behavioral events, i.e., event-related activity transients (ERT). A simple and popular approach to identifying ERT is to summarize peri-event signal [e.g., area under the curve (AUC), peak activity, etc.,] and perform standard analyses on this summary statistic. We highlight the various issues with this approach and overview straightforward alternatives: waveform confidence intervals (CIs) and permutation tests. We introduce the rationale behind these approaches, describe the results of Monte Carlo simulations evaluating their effectiveness at controlling Type I and Type II error rates, and offer some recommendations for selecting appropriate analysis strategies for fiber photometry experiments.
光纤光度法使神经科学家能够轻松测量清醒、自由活动动物的靶向脑活动模式。该技术的一个重点是识别特定环境和/或行为事件周围活动中与功能相关的变化,即事件相关活动瞬变(ERT)。识别ERT的一种简单且常用的方法是总结事件周围信号[例如曲线下面积(AUC)、峰值活动等],并对该汇总统计量进行标准分析。我们强调了这种方法存在的各种问题,并概述了直接的替代方法:波形置信区间(CIs)和置换检验。我们介绍了这些方法背后的基本原理,描述了评估它们在控制I型和II型错误率方面有效性的蒙特卡罗模拟结果,并为光纤光度法实验选择合适的分析策略提供了一些建议。