Addressing variability in the acoustic startle reflex for accurate gap detection assessment

Abstract

The acoustic startle reflex (ASR) is subject to substantial variability. This inherent variability consequently shapes the conclusions drawn from gap-induced prepulse inhibition of the acoustic startle reflex (GPIAS) assessments. Recent studies have cast doubt as to the efficacy of this methodology as it pertains to tinnitus assessment, partially, due to variability in and between data sets. The goal of this study was to examine the variance associated with several common data collection variables and data analyses with the aim to improve GPIAS reliability. To study this the GPIAS tests were conducted in adult male and female CBA/CaJ mice. Factors such as inter-trial interval, circadian rhythm, sex differences, and sensory adaptation were each evaluated. We then examined various data analysis factors which influence GPIAS assessment. Gap-induced facilitation, data processing options, and assessments of tinnitus were studied. We found that the startle reflex is highly variable in CBA/CaJ mice, but this can be minimized by certain data collection factors. We also found that careful consideration of temporal fluctuations of the ASR and controlling for facilitation can lead to more accurate GPIAS results. This study provides a guide for reducing variance in the GPIAS methodology - thereby improving the diagnostic power of the test.

Robust and replicable measurement for prepulse inhibition of the acoustic startle response

“…Using data from 72 rats across more than 100 stimuli, for a total of over 300,000 trials, we replicated previous work demonstrating that the aforementioned assumptions do not hold. Specifically, our findings confirm that (1) the startle response within animals is highly variable [46]; (2) the startle response within animals has a non-Gaussian distribution that is better represented by a log-normal distribution [47]; (3) the traditional metric used for PPI systematically decreases as a function of sound level [48]; (4) PPI is also a function of the baseline startle response [45].…”

A Novel Mouse Model of Aminoglycoside-Induced Hyperacusis and Tinnitus

“…Offline data processing with code written in visual basic was used to evaluate whether each trial was a startle or non-startle via template matching and startle magnitude data was converted from force to CMD ( Grimsley et al, 2015 ). Only legitimate startles were included and used in the final data analyses ( Longenecker et al, 2018 ).…”

“…Hyperacusis was observed immediately after the start of AG treatment and persisted until week 6 in the ebselen treated group, and week 14 in the vehicle group ( Figure 6A). The magnitude and consistency of the I/O startle response magnitudes were surprising considering that startle magnitude usually habituates over repeated testing sessions (Figures 5C,E; Davis, 1984;Longenecker et al, 2018). Since 14 out of the 17 mice demonstrated greatly exaggerated startle responses, behavioral evidence of hyperacusis could be a common outcome of AG treatment.…”

“…On each testing day, 3 GPIAS sessions were run on each mouse lasting roughly 45 min. The best performance ratio was used to determine an individual animal's daily gap detection performance (Longenecker et al, 2018). GPIAS assessments were collected before (baseline), as well as at weeks 1, 2, 3, 6, 10, and 14 from the start of AG treatment (Figure 1).…”

Open(G)PIAS: An Open-Source Solution for the Construction of a High-Precision Acoustic Startle Response Setup for Tinnitus Screening and Threshold Estimation in Rodents

“…At the beginning of each measurement session, five trials without pre-stimulus are presented to prevent adaptation effects in the response amplitudes of the animal during the subsequent trials. It could potentially be beneficial to perform some acclimatization sessions to reduce stress and thus to get more reliable results (Longenecker et al, 2018).…”