Research PaperAddressing variability in the acoustic startle reflex for accurate gap detection assessment
Introduction
A reliable animal model of tinnitus is a prerequisite for tinnitus related therapies. Due to its relatively minor time commitment gap-induced prepulse inhibition of the acoustic startle reflex (GPIAS) has quickly become one of the predominant behavioral assessments for tinnitus (Turner et al., 2006) in several animal models (see Eggermont, 2013; Hayes et al., 2014; Galazyuk and Hébert, 2015). Many groups have used GPIAS to behaviorally assess tinnitus in rats (Turner et al., 2006; Lobarinas et al., 2013; Singer et al., 2013; Ropp et al., 2014), mice (Longenecker and Galazyuk, 2011; Middleton et al., 2011; Hickox and Liberman, 2014; Lowe and Walton, 2015; Yu et al., 2016), guinea pigs (Dehmel et al., 2012; Berger et al., 2013), and hamsters (Salloum et al., 2016). However, some uncertainty of this method is rooted in a lack of consistency of methodologies and data assessment strategies across labs (Galazyuk and Hébert, 2015). While the acoustic startle reflex (Landis and Hunt, 1939; Fleshler, 1965), prepulse inhibition (Hoffman and Searle, 1965; Ison and Hammond, 1971; Carlson and Willott, 1996; Swerdlow et al., 2001), and gap-induced prepulse inhibition (Ison, 1982) have been studied for decades, many of the specifics of stimuli, hardware, technical, and analytic information related to tinnitus detection have not yet been solidified. For this reason, we have previously addressed some hardware and stimulus presentation issues (Longenecker and Galazyuk, 2012), as well an in-depth analysis of the startle response (Grimsley et al., 2015). Although these initial steps have improved the confidence of GPIAS assessments, the finer details concerning data collection and data analysis, as they specifically relate to tinnitus assessment, need further attention.
GPIAS studies have largely neglected to provide details on data collection when assessing tinnitus in laboratory animals. However, many aspects of these approaches can dramatically affect conclusions of GPIAS experiments. This is especially true for animals that have high startle reflex variability (Berger et al., 2013; Longenecker and Galazyuk, 2016; Salloum et al., 2016). Limiting ASR variability is critical in a repeated measure designs which compare an animal's performance before and after a tinnitus-inducing experimental treatment. Several factors influencing the ASR should be considered in order to control the inherent variability. These include issues that pertain to inter-trial intervals (ITI) (Ison and Hammond, 1971; Leitner et al., 1993; Willott and Carlson, 1995; Plappert et al., 2004), circadian rhythm (Chabot and Taylor, 1992a; Chabot and Taylor, 1992b), sex differences (Plappert et al., 2005; Koch, 1998), and sensory adaptation. A change to any of these factors can alter the startle response magnitude and startle response variability. These issues could be magnified when assessing the gap detection abilities in noise exposed animals, due to a suppressive effect of acoustic over-exposure on startle magnitude (Longenecker and Galazyuk, 2011; Lobarinas et al., 2013). Standardizing data collection efforts could decrease GPIAS data variability between animals, experiments, and lab groups.
One of the major confounds in GPIAS data analysis is gap-induced facilitation. A gap in a continuous background noise usually inhibits or reduces the startle response magnitude (Stitt et al., 1973; Ison, 1982). However, this is not always the case. Depending on stimulus conditions, a prepulse, gap, or alterations of the background noise can alternatively act as a facilitator of the startle reflex in mice (Plappert et al., 2004; Willott and Carlson, 1995), rats (Stitt et al., 1974; Ison et al., 1997), and humans (Aasen et al., 2005). While it is not fully understood why this dichotomy exists, both responses represent real sensory gating phenomenon but likely have separate complimentary biological circuit (Schmajuk and Larrauri, 2005). Thus, GPIAS assessments should be cognizant of this issue when examining “how well” an animal can detect a gap. If not appropriately addressed during data analysis, facilitation can limit the effectiveness of GPIAS as a tool to assess gap detection performance because of unnecessary variability. The exact details of data processing can also significantly affect GPIAS data interpretation. Unfortunately, this part of data analysis is typically neglected in method sections of the relevant papers. Nevertheless, details such as which data points are included/excluded, how many testing sessions/days were used in the control and experimental conditions, how ratios were calculated and/or averaged together, are critical to draw defendable conclusions based on GPIAS data.
Our results indicate that ITI, circadian cycle, sex, and sensory adaptation all play roles in the degree of variance present in GPIAS experiments. Our data also suggest that particular data analyses are critical to minimize the effect of variance in GPIAS data. Taken together, these findings suggest that the GPIAS method is more complicated than previously described, but can provide accurate assessments of gap detection performance.
Section snippets
Subjects
A total of 62 CBA/CaJ mice were used in this study. Group A contained fourteen male mice which were used in the majority of experiments. Group B contained an additional 48 mice (24 males and 24 females) which were used in the sex-based variation experiments. In group A, mice were divided into two groups of seven and were housed in separate rooms: seven of these mice were housed in a regular light dark cycle (lights on 10 a.m. to 10 p.m.) (inactive mice) and other seven were kept in a reverse
Data collection
Mice are known to have a highly variable ASR. To minimize the impact of this variability on experimental results, it is beneficial to identify experimental manipulations which might contribute to this variability. Mice were tested over multiple sessions to assess the amount of ASR variability caused by: ITI, circadian rhythm, sex differences, and sensory adaptation.
Current state of GPIAS for tinnitus assessment
Despite its popularity as a methodology for tinnitus assessment (Eggermont, 2013; Hayes et al., 2014; Galazyuk and Hébert, 2015), the GPIAS method is not well developed for several reasons. First, scientists who routinely use this method for tinnitus assessment rarely describe specific details of data collection and data analysis. Second, the major stimulus parameters used for GPIAS are not well justified. Criteria and statistical verification of the presence of tinnitus-like behavior, even
Conflicts of interest
The authors declare this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We would like to acknowledge Dr. Merri Rosen for her comments on earlier versions of this manuscript. The authors also thank Olga Galazyuk for developing software that allowed off-line data analysis. This research was supported by grant R01 DC011330 to AVG from the National Institute on Deafness and Other Communication Disorders of the U.S. Public Health Service.
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2019, Physiology and BehaviorCitation Excerpt :It has been also demonstrated that many factors like hearing loss, sensitivity to background noise or to startle stimuli can change gap-PPI and influence interpretation of test results with respect to the presence of tinnitus [33–35]. In addition to this, a wide range of criteria for positive tinnitus detection has been used across different laboratories and there still is no consensus on a best practice for statistical evaluation of gap-PPI results [36,37]. To make the evaluation of the gap-PPI results more objective, advanced statistical methods should be considered (specifically in mice tinnitus models) [37].
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