Analyzing Event-Related Transients: Confidence Intervals, Permutation Tests, and Consecutive Thresholds

Abstract

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.

Acetylcholine is released in the basolateral amygdala in response to predictors of reward and enhances the learning of cue-reward contingency

“…During Pre-Training, when there were high numbers of both correct and incorrect nose pokes, there was a large increase in ACh release following correct nose pokes, which were followed by reward delivery and receptacle light, but not incorrect nose pokes ( Figure 2C + Figure 2—figure supplement 1B–C ). We used bootstrapped confidence intervals (bCIs) to determine when transients were statistically significant (bCI did not contain the null of 0 [ Jean-Richard-Dit-Bressel et al, 2020 ]). Correct, but not incorrect, nose poke trials consistently showed a sustained, significant increase in fluorescence close to the time of nose poke onset ( Figure 2C ).…”

“…Bootstrapped confidence intervals (bCIs) of the Z-scored % ΔF/F0 fiber photometry data within and across mice were generated using the methods described in Jean-Richard-Dit-Bressel et al, 2020 for the following events: tone onset, correct nose poke, receptacle entry, and incorrect nose poke. For the within-mouse analysis by day, trials were aligned to event onset, and bCIs were generated for events that had at least 3 trials from 5 s before to 10 s after each event.…”

Instrumental aversion coding in the basolateral amygdala and its reversion by a benzodiazepine

“…Specifically, bootstrapped means were obtained by randomly resampling from subject mean waveforms with replacement (1000 iterations). CI limits were derived from 2.5 and 97.5 percentiles of bootstrap distribution, expanded by a factor of (Jean-Richard-dit-Bressel et al, 2020). A significant transient was identified as a period that CI limits did not contain 0 (moving average baseline) for at least 1/3secs (low-pass filter window; Jean-Richard-dit-Bressel et al, 2020).…”

The Mesolimbic Dopamine Activity Signatures of Relapse to Alcohol-Seeking

“…First, DF/F within a time window around events was compiled. To determine significant event-related transients within this window, a bootstrapping CI procedure (95% CI, 1000 bootstraps) was used (Jean-Richard-dit-Bressel et al, 2020). A distribution of bootstrapped DF/F means was generated by randomly resampling from trial DF/F waveforms, with replacement, for the same number of trials.…”

“…A distribution of bootstrapped DF/F means was generated by randomly resampling from trial DF/F waveforms, with replacement, for the same number of trials. A CI was obtained per time point using the 2.5 and 97.5 percentiles of the bootstrap distribution, which was then expanded by a factor of sqrt (n/(n -1)) to adjust for narrowness bias (Jean-Richard-dit-Bressel et al, 2020). Significant transients were defined as periods whose 95% CI did not contain 0 (baseline) for at least 0.5 s (low-pass filter threshold).…”