Automated classification of acoustic startle reflex waveforms in young CBA/CaJ mice using machine learning

Abstract

Background

The acoustic startle response (ASR) is a simple reflex that results in a whole body motor response after animals hear a brief loud sound and is used as a multisensory tool across many disciplines. Unfortunately, a method of how to record, process, and analyze ASRs has yet to be standardized, leading to high variability in the collection, analysis, and interpretation of ASRs within and between laboratories.

New method

ASR waveforms collected from young adult CBA/CaJ mice were normalized with features extracted from the waveform, the resulting power spectral density estimates, and the continuous wavelet transforms. The features were then partitioned into training and test/validation sets. Machine learning methods from different families of algorithms were used to combine startle-related features into robust predictive models to predict whether an ASR waveform is a startle or non-startle.

Results

An ensemble of several machine learning models resulted in an extremely robust model to predict whether an ASR waveform is a startle or non-startle with a mean ROC of 0.9779, training accuracy of 0.9993, and testing accuracy of 0.9301.

Comparison with existing methods

ASR waveforms analyzed using the threshold and RMS techniques resulted in over 80% of accepted startles actually being non-startles when manually classified versus 2.2% for the machine learning method, resulting in statistically significant differences in ASR metrics (such as startle amplitude and pre-pulse inhibition) between classification methods.

Conclusions

The machine learning approach presented in this paper can be adapted to nearly any ASR paradigm to accurately process, sort, and classify startle responses.

Introduction

The acoustic startle reflex (ASR) and modification using pre-pulse stimuli has consistently been one of the most used diagnostic tools for assessing laboratory animals’ internal state over the past several decades (Davis, 1984, Koch, 1999). Modification of this simple reflex resulting from a brief loud sound has been used in many neuropsychological disciplines for evaluating hearing (Lauer et al., 2017), tinnitus (Galazyuk and Hébert, 2015, Gerum et al., 2019, Turner et al., 2006), many neuropsychiatric disorders such as schizophrenia, bi-polar disorder, autism, and many other disorders that disrupt sensory-motor gating (Braff et al., 2001, Kohl et al., 2013, Kohl et al., 2014). Unfortunately, a standardized method of how to record, measure, process, and analyze startle reflex waveforms has yet to be standardized, which leads to high variability within and between laboratories using this assessment tool. Reasons for this variability within a lab are numerous but include animal awareness, habituation/sensitization rates, neural plasticity, anxiety/stress levels, and neuro-muscular interactions. Even more explanations for variability exists between laboratories and include: species/strain variations, loudspeaker/wav file sound quality, recording platform type, platform sensitivity, and mode of assessment which varies between whole body startle in mice/rats (Horlington, 1968, Grimsley et al., 2015), to the Preyer reflex (small ear movements) in guinea pigs (Berger et al., 2013, Böhmer, 1988), and eye blink reflex in humans and primate research (Säring and von Cramon, 1981, Filion et al., 1993, Grillon et al., 1997, Winslow et al., 2002). These factors result in variable ASR waveform measurements, which if processed/analyzed with the same methodology, could result in verifiable comparisons between experiments and laboratories using completely different techniques. This is due to likely differences in the characteristics of the startle waveforms which are included in the analysis.

Standardization of ASR waveform classification is extremely important for many reasons (Lauer et al., 2017). Previous work has included the elimination of the highest and lowest startle responses for each frequency (Longenecker and Galazyuk, 2011), using Grubb's test for outliers (Longenecker and Galazyuk, 2012), elimination of startle responses with maximum magnitude after startle presentation less than that before startle presentation, elimination of startle responses whose RMS after startle presentation is less than that before startle presentation, template matching (Grimsley et al., 2015), and discarding invalid trials containing movement in excess of a threshold prior to stimulus presentation (Schilling et al., 2017) as effective procedures for cleaning startle data by removing “non-startles,” which occur frequently in animal or humans continually presented with loud sounds. Non-startles occur more often when animals are presented low intensity startle stimuli as well as when an intense pre-pulse is presented prior to the startle stimulus (Longenecker et al., 2016). Since pre-pulse inhibition using stimuli placed before the startle elicitor is one of the most critical aspects of the startle reflex studied, proper classification of startles when pre-pulses are presented can dramatically influence the results. However, because each laboratory might utilize different hardware (sensors, filters, etc.) and/or waveform filter configurations which records animals startle-related movements, it makes it problematic to suggest a standardized template. Thus, an alternative approach should be used to classify startle response waveforms.

Machine learning is an evolving field of computational algorithms which learn from data in order to improve their performance on a particular classification or prediction task (Mjolsness and DeCoste, 2001, El Naqa and Murphy, 2015, Kotsiantis et al., 2006). Machine learning has been successfully used in genomics (Libbrecht and Noble, 2015), medical imaging and pathology (Komura and Ishikawa, 2019, Shen et al., 2017), as well as in the diagnosis and treatment of cancer (Goldenberg et al., 2019, Bejnordi et al., 2017). Machine learning could even be used in clinical settings to aid the navigation of the complex health trajectory of an individual patient through machine learning performed on data from many patients (Rajkomar et al., 2019). In this paper, we describe an automated, supervised machine learning approach to classify ASR waveforms acquired using various stimulus protocols and levels, eliciting ASRs of various magnitudes and shapes.