LAUSR

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.