Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
December 9th, 2022
This article describes the semi-automated measurement of the amplitudes and latencies of the first five peaks and troughs in the auditory brainstem response waveform. An additional routine compiles and annotates the data into a spreadsheet for experimenter analysis. These free computer routines are executed using the open-source statistical package R.
Auditory brainstem responses are routinely collected to determine hearing thresholds. The ABR waveform contains additional information on brainstem activity, but previously, many researchers have had to analyze these manually. R scripts work with R software in the RStudio framework to semi-automate ABR waveform analysis.
They are free and easy to use. Our hope is that routine analysis of the ABR waveform will enable later studies to assess the relevance of this pre-cognitive auditory processing in diagnosis. To begin, export the auditory brainstem response or ABR recording as an ASCII file.
For IHS, open the computer program, load the file of interest and display the desired waveforms on one page. Under the data Tab, select Save Page as ASCII to obtain a txt file. After naming the data file appropriately with an ID, record the ID and subject information in a metadata file named info.
csv, ensuring not to include any information such as the genotype, sex, age, or treatment in the ID.Repeat the steps with all the files to be analyzed as separate ID files and obtain the metadata. Download and install the R and RStudio programs from their respective websites. Then install the required libraries, tidyverse, Shiny, Plotly, and Zoo, with the required commands in the command window of RStudio.
Next, download the scripts findpeaks. r and see_trace_click. r from the White Lab GitHub, as well as the associated file time.csv.
Create a new folder named Test Folder to place all the ASCII files, info. csv, and time.csv. Within the Test Folder, create a subfolder named ASCII Folder and place the ASCII files.
Open the findpeaks. r script in RStudio by right-clicking on the tab for the script in the toolbar to select the set working directory and set it to Test Folder. In the script window, click Source on the upper right-hand side of the toolbar to load the program.
Then in the command window, use the commands to analyze the waveforms in individual and batch processing modes. Load the the waveform data for the specific individual using the command. Next, in the opened window of see_trace_click.
r script in RStudio, click the Run App button in the header and wait for a new interactive window to appear. In the box on the top left, input the sound level for the waveform that requires revision and look for the waveform displayed in the window. Move the cursor around the waveform to reveal the latency and amplitude at any point.
To record the latency and amplitude data, click on the correct peak and then copy/paste the data into the csv file. Repeat the process to record the data for the trough following an immediate peak and calculate the amplitude measurement by subtracting the trough amplitude from the peak amplitude in the spreadsheet cell. Transfer the verified.
csv files to a new subfolder named Peak Data in the Test Folder to append the data. Next, combine the metadata by executing the command. To perform statistical analysis on the compiled data, use a test for normality such as the Shapiro-Wilk test to assess the distribution of the data by executing the function.
If the Shapiro-Wilk test is not significant, it means the dataset has a normal distribution. Hence, assess the data with a parametric test such as ANOVA by executing the function. If the Shapiro-Wilk test is less than P equals to 0.05, use the Kruskal-Wallis rank-sum test or another appropriate non-parametric measure.
Finally, display the average waveforms by executing the command. In the waveform response of 75 decibels click stimulus for a young F1 mouse, the peaks and troughs were automatically identified with findpeaks.r. The waveforms between 5 and 75 decibels obtained from the manually identified peaks and findpeaks.
r were compared. The averages are plotted as heavy lines with the shaded region representing one standard deviation and the results obtained by findpeaks. r correlates strongly with the manual results.
When attempting this procedure, remember to set the working directory as it is highly crucial. R script designates peaks and troughs based on the latency windows for the mouse ABR implemented with the time. csv file.
This may be modified by users to quantify other EEG waveforms.
Overview
This article describes a semi-automated method for measuring the amplitudes and latencies of the first five peaks and troughs in the auditory brainstem response (ABR) waveform. Using the open-source statistical package R, researchers can analyze ABR data more efficiently than manual methods, facilitating further study of pre-cognitive auditory processing.
Key Study Components
Area of Science
- Auditory neuroscience
- Electrophysiology
- Data analysis
Background
- Auditory brainstem responses are important for determining hearing thresholds.
- The ABR waveform contains valuable information about brainstem activity.
- Many researchers previously performed manual analysis of the ABR waveforms.
- This study aims to simplify the analysis using R scripts.
Purpose of Study
- To automate the measurement of amplitudes and latencies in ABR waveforms.
- To enhance the ability to evaluate auditory processing in diagnostics.
- To provide free and accessible analysis tools for researchers.
Methods Used
- The platform used is R software within the RStudio framework.
- The main biological model consists of auditory brainstem response data exported from recording software.
- Researchers create a Test Folder for data organization and run specific R scripts for analysis.
- Detailed command-line instructions guide users through loading and analyzing waveform data.
- Statistical methods such as ANOVA and non-parametric tests assess data distributions.
Main Results
- The automated analysis correlates strongly with manual results for ABR waveform characteristics.
- Statistical tests validate the significance of identified peaks and troughs.
- The study highlights the reliability of the automated method in quantifying brainstem activity.
Conclusions
- This study demonstrates the efficacy of R scripts in automating ABR waveform analysis.
- The routine analysis allows for greater understanding of auditory processing mechanisms.
- The provided methods can be adapted for various EEG waveform quantifications as needed.
Frequently Asked Questions
Executive Industry Relevance
Semi-automated analysis of auditory brainstem response (ABR) waveforms using R enables scalable, quantitative assessment of neural function in preclinical models. This approach reduces manual workload and increases reproducibility, supporting robust target validation and mechanistic de-risking in auditory and neuropharmacology pipelines. Integrating standardized waveform analytics enhances predictive confidence at early discovery and translational inflection points.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables objective quantification of neural response amplitudes and latencies for functional target validation.
- Supports mechanistic de-risking by providing reproducible, quantitative neural readouts.
- Facilitates portfolio triage by standardizing data across experimental conditions and cohorts.
Screening & Assay Development
- Prepares validated electrophysiological assays for downstream compound screening workflows.
- Improves assay reproducibility and standardization through semi-automated data extraction.
- Generates quantitative outputs suitable for high-throughput or comparative studies.
- Enables reliable evaluation of auditory or neuroactive compound effects.
Translational & Preclinical Research
- Aligns preclinical neural response data with translational biomarker strategies when relevant.
- Supports continuity from early discovery through preclinical validation by enabling consistent waveform analytics.
- Provides risk-adjusted data for advancement decisions in neuropharmacology and hearing research portfolios.
Pipeline & Workflow Integration
This semi-automated ABR analysis method fits from early discovery through preclinical research, supporting lead identification and translational biomarker development when neural endpoints are relevant.
- Discovery Biology: Enables hypothesis testing and pathway clarification via quantitative neural response metrics.
- Screening: Delivers reproducible, assay-ready outputs for compound evaluation and cross-condition comparison.
- Analytics: Provides statistical outputs, including normality testing and parametric/non-parametric comparisons, to inform decision-making.
- Translational Research: Supports biomarker alignment and continuity when ABR or similar waveforms are used as translational endpoints.
- Enterprise Reuse: Offers a reusable, open-source analytic capability adaptable to other electrophysiological datasets.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in neural target validation.
- Operational Value: Standardizes and streamlines waveform analysis, improving reproducibility and scalability.
- Strategic Value: Enables more informed go/no-go decisions and capital-efficient portfolio management.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of neuropharmacology and hearing research assets.
Implementation Considerations
- Requires expertise in electrophysiology and R-based data analysis.
- Needs access to R, RStudio, and relevant open-source libraries and scripts.
- Demands cross-team standardization of data formats and analysis workflows.
- Adaptable to other EEG or neural waveform models with user modification of latency windows.
- Dependent on correct data formatting and adherence to analytic protocols for reliable outputs.
Biopharma FAQs
Chapters in this video
0:04
Introduction
0:50
Preparing the Auditory Brainstem Response (ABR) Data for Analysis
1:39
Installing the Required Packages and Loading the Data
2:26
Obtaining the Preliminary Analysis with FindPeaks.R
2:58
Verifying the Preliminary Analysis
3:53
Compiling and Visualizing the Dataset
4:51
Results: Analysis of Auditory Brainstem Response (ABR) Data by Manual and FindPeaks.R
5:25
Conclusion
