This work was supported by two grants from the NIDCD to PW: R01 DC018660 and an administrative supplementary award, R01 DC014261-05S1.

All procedures performed on animals were approved in advance by the University of Rochester Committee for Animal Research. The experimental subjects were 12 wild-type F1 male and female mice at 1 month of age. These F1 mice are the product of mating a CBA/CaJ dam and a C57BL/6J sire. The mice were bred and housed in the vivarium facility with a standard 12 h light/dark cycle, unlimited food and water, and ample nesting supplies. No more than five same-sex siblings were housed together in one cage.
1. Obtaining data for analysis
NOTE: This step must comply with institutional guidelines and be pre-approved by the institutional animal welfare committee. The detailed process for generating ABR data from mice has been described elsewhere18.
<ol>
	<li>Record the ABR with the platform of choice. 
	NOTE: In the instance shown here, the recordings were performed on mice.
	<ol>
		<li>Use a 5 ms click stimulus beginning at a 75 dB sound pressure level and decreasing in 5 dB steps to 5 dB. Record an average of 512 sweeps for each amplitude. Reject responses if their peak to trough amplitude is greater than 31 &#181;V at any instance between 1.3 ms and 12.5 ms after the stimulus. 
		NOTE: Recordings from tone pip presentations may also be used. We anticipate that the analysis routine will work for other species, including humans.</li>
	</ol>
	</li>
	<li>Export the ABR recording as an ASCII file.
	<ol>
		<li>For IHS, open the computer program.</li>
		<li>Load the file of interest, and display the desired waveforms on one page.</li>
		<li>Under the Data tab, select Save Page as ASCII to obtain a .txt file.</li>
		<li>After naming the data file appropriately (&#34;ID&#34;), record the ID and subject information in a metadata file entitled &#34;info.csv.&#34; Ensure that the &#34;ID&#34; does not include any information such as the genotype, sex, age, or treatment; this information is instead recorded in &#34;info.csv.&#34; 
		NOTE: A fair deck of playing cards can be used to randomly assign labels if needed.</li>
		<li>Repeat with all the files to be analyzed as separate &#34;ID&#34; files.</li>
	</ol>
	</li>
</ol>
2. Installing the required packages and loading the data onto the working computer
<ol>
	<li>Download and install R (https://www.r-project.org) and RStudio (https://www.rstudio.com).&#160; 
	NOTE: The protocol described here used R &#8805; 4.0.0.</li>
	<li>Install the required libraries, tidyverse, shiny, plotly, and zoo, by typing the following command in the command window of RStudio: 
	Install.packages(&#34;tidyverse&#34;) 
	Install.packages(&#34;shiny&#34;) 
	Install.packages(&#34;plotly&#34;) 
	Install.packages(&#34;zoo&#34;)</li>
	<li>Download the scripts FindPeaks.R and See_trace_click.R from the White lab GitHub (https://github.com/PWhiteLab/FindPeaks), as well as the associated file &#8220;Time.csv.&#8221;</li>
	<li>Create a new folder to contain all the ASCII files, &#34;info.csv&#34;, and &#34;Time.csv.&#34; For this example, name the folder &#34;Test_Folder.&#34; Within &#34;Test_Folder,&#34; place the ASCII files in a subfolder entitled &#34;ASCII_Folder.&#34;</li>
</ol>
3. Obtaining the preliminary analysis with FindPeaks.R
<ol>
	<li>Open the FindPeaks.R script in RStudio.</li>
	<li>Right-click on the tab for the script in the tool bar to select Set working directory, and set it to Test_Folder (see Supplementary Figure S1A).</li>
	<li>In the script window, click Source on the upper right-hand side of the toolbar to load the program (see Supplementary Figure S1B).</li>
	<li>In the command window, use the following commands to analyze the waveforms (see Supplementary Figure S2): 
	FindPeaks_single(&#34;ASCII_folder/ID.txt&#34;) for individual files (see Supplementary Figure S2A) 
	FindPeaks_group(&#34;ASCII_Folder&#34;) for batch processing (see Supplementary Figure S2B) 
	&#8203;NOTE: The script will output (1) pdf files displaying the waveforms with labeled peaks and troughs (see Supplementary Figure S2C) and (2) an ID.csv file containing the numerical data for the amplitude (&#181;V) and latency (ms). Both files will be placed in &#34;Test_Folder&#34;.</li>
</ol>
4. Verifying the preliminary analysis
NOTE: At low sound levels, parts of the waveform may become difficult to distinguish from noise, and FindPeaks.R may misidentify the peaks or troughs compared to the experimenter&#39;s opinion. If there is a discrepancy, then the .csv file can be amended with data obtained from the script See_trace_click.R.
<ol>
	<li>Load the waveform data for the specific individual using the command (see Supplementary Figure S3A): 
	Waveform &#60;- ASCII_extract(&#34;ASCII_Folder/ID.txt&#34;)</li>
	<li>Open the See_trace_click.R script in RStudio.</li>
	<li>In the header on the left, click the Run App button, and wait for a new interactive (Shiny) window to appear (see Supplementary Figure S3B).</li>
	<li>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.</li>
	<li>Move the cursor around the waveform to reveal the latency and amplitude at any point.</li>
	<li>Click on the correct peak and the following trough to record the data in the table below. Copy and paste the latency data into the .csv file (see Supplementary Figure S3C).</li>
	<li>To calculate the amplitude measurement, subtract the following trough amplitude from the peak amplitude in the spreadsheet cell.</li>
</ol>
5. Compiling and visualizing the dataset
<ol>
	<li>Transfer the verified .csv files to a new subfolder in Test_Folder entitled &#34;Peak_Data.&#34;</li>
	<li>Append the data into a single .csv file and name it &#34;Peak_Data.csv.&#34;</li>
	<li>Use the following command: 
	Compile (&#34;Peak_Data&#34;) 
	NOTE: This script combines the metadata from info.csv with Peak_Data.csv to label the data with group information. It also automatically calculates the interpeak latencies and amplitude ratios.</li>
	<li>Perform statistical analysis on the compiled data.
	<ol>
		<li>Use a test for normality, such as the Shapiro-Wilks test, to assess the distribution of the data with the following function: 
		shapiro.test()</li>
		<li>If the Shapiro-Wilks test is not significant, the data set has a normal distribution; hence, assess the data with a parametric test such as ANOVA&#160;with the following function: 
		aov()</li>
		<li>If the Shapiro-Wilks test is less than p = 0.05, use the Kruskal-Wallis rank sum test (with the function below) or another appropriate non-parametric measure (see other possibilities in the discussion). 
		kruskal.test()</li>
	</ol>
	</li>
	<li>To display average waveforms, use the following command: 
	Average_Waveform(&#34;ASCII_Folder &#34;, aes(x = Data_Pnt_ms, y = &#34;dB&#34;, group = Genotype, color = Genotype)) 
	NOTE: This command shows the average waveforms for different genotypes in different colors. For the y variable dB, insert the number that corresponds to the desired amplitude, such as 75, without quotation marks. For other comparisons, use the corresponding group label from the metadata.</li>
</ol>

<ol>
	<li>Gopal, K. V., Pierel, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Binaural+interaction+component+in+children+at+risk+for+central+auditory+processing+disorders.">Binaural interaction component in children at risk for central auditory processing disorders.</a> Scandinavian Audiology. 28 (2), 77-84 (1999).</li><li>Kujawa, S. G., Liberman, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adding+insult+to+injury:+Cochlear+nerve+degeneration+after+&amp;#34;temporary&amp;#34;+noise-induced+hearing+loss.">Adding insult to injury: Cochlear nerve degeneration after &amp;#34;temporary&amp;#34; noise-induced hearing loss.</a> Journal of Neuroscience. 29 (45), 14077-14085 (2009).</li><li>Salvi, R. J., Wang, J., Ding, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Auditory+plasticity+and+hyperactivity+following+cochlear+damage.">Auditory plasticity and hyperactivity following cochlear damage.</a> Hearing Research. 147 (1-2), 261-274 (2000).</li><li>Schrode, K. M., Muniak, M. A., Kim, Y. H., Lauer, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Central+compensation+in+auditory+brainstem+after+damaging+noise+exposure.">Central compensation in auditory brainstem after damaging noise exposure.</a> eNeuro. 5 (4), (2018).</li><li>Patel, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SIRT3+promotes+auditory+function+in+young+adult+FVB/nJ+mice+but+is+dispensable+for+hearing+recovery+after+noise+exposure.">SIRT3 promotes auditory function in young adult FVB/nJ mice but is dispensable for hearing recovery after noise exposure.</a> PLoS One. 15 (7), 0235491 (2020).</li><li>Milinkeviciute, G., Chokr, S. M., Castro, E. M., Cramer, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CX3CR1+mutation+alters+synaptic+and+astrocytic+protein+expression,+topographic+gradients,+and+response+latencies+in+the+auditory+brainstem.">CX3CR1 mutation alters synaptic and astrocytic protein expression, topographic gradients, and response latencies in the auditory brainstem.</a> Journal of Comparative Neurology. 529 (11), 3076-3097 (2021).</li><li>Ison, J. R., Allen, P. D., Oertel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deleting+the+HCN1+subunit+of+hyperpolarization-activated+ion+channels+in+mice+impairs+acoustic+startle+reflexes,+gap+detection,+and+spatial+localization.">Deleting the HCN1 subunit of hyperpolarization-activated ion channels in mice impairs acoustic startle reflexes, gap detection, and spatial localization.</a> Journal of the Association for Research in Otolaryngology. 18 (3), 427-440 (2017).</li><li>Chen, F., Zhao, F., Mahafza, N., Lu, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+noise-induced+cochlear+synaptopathy+by+auditory+brainstem+response+in+tinnitus+patients+with+normal+hearing+thresholds:+A+meta-analysis.">Detecting noise-induced cochlear synaptopathy by auditory brainstem response in tinnitus patients with normal hearing thresholds: A meta-analysis.</a> Frontiers in Neuroscience. 15, 778197 (2021).</li><li>Santos-Filha, V. A., Samelli, A. G., Matas, C. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Noise-induced+tinnitus:+Auditory+evoked+potential+in+symptomatic+and+asymptomatic+patients.">Noise-induced tinnitus: Auditory evoked potential in symptomatic and asymptomatic patients.</a> Clinics. 69 (7), 487-490 (2014).</li><li>Kochar, K., Srivastava, T., Maurya, R. K., Jain, R., Aggarwal, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+evoked+potential+&amp;+brainstem+auditory+evoked+potentials+in+acute+attack+&amp;+after+the+attack+of+migraine.">Visual evoked potential &amp; brainstem auditory evoked potentials in acute attack &amp; after the attack of migraine.</a> Electromyography and Clinical Neurophysiology. 42 (3), 175-179 (2002).</li><li>Gedik, O., Husam, H., Basoz, M., Tas, N., Aksoy, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+coronavirus+disease+2019+on+the+hearing+system.">The effect of coronavirus disease 2019 on the hearing system.</a> Journal of Laryngology and Otology. 135 (9), 810-814 (2021).</li><li>Ozturk, B., Kavruk, H., Aykul, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Audiological+findings+in+individuals+diagnosed+with+COVID-19.">Audiological findings in individuals diagnosed with COVID-19.</a> American Journal of Otolaryngology. 43 (3), 103428 (2022).</li><li>Dror, A. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Auditory+performance+in+recovered+SARS-COV-2+patients.">Auditory performance in recovered SARS-COV-2 patients.</a> Otology &amp; Neurotology. 42 (5), 666-670 (2021).</li><li>Lin, F. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hearing+loss+and+incident+dementia.">Hearing loss and incident dementia.</a> Archives of Neurology. 68 (2), 214-220 (2011).</li><li>Jafari, Z., Kolb, B. E., Mohajerani, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Auditory+dysfunction+in+Parkinson's+disease.">Auditory dysfunction in Parkinson's disease.</a> Movement Disorders. 35 (4), 537-550 (2020).</li><li>Swords, G. M., Nguyen, L. T., Mudar, R. A., Llano, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Auditory+system+dysfunction+in+Alzheimer+disease+and+its+prodromal+states:+A+review.">Auditory system dysfunction in Alzheimer disease and its prodromal states: A review.</a> Ageing Research Reviews. 44, 49-59 (2018).</li><li>Konrad-Martin, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-related+changes+in+the+auditory+brainstem+response.">Age-related changes in the auditory brainstem response.</a> Journal of the American Academy of Audiology. 23 (1), 18-35 (2012).</li><li>Navntoft, C. A., Marozeau, J., Barkat, T. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cochlear+implant+surgery+and+electrically-evoked+auditory+brainstem+response+recordings+in+C57BL/6+mice.">Cochlear implant surgery and electrically-evoked auditory brainstem response recordings in C57BL/6 mice.</a> Journal of Visualized Experiments. (143), e59073 (2019).</li><li>Gilels, F., Paquette, S. T., Beaulac, H. J., Bullen, A., White, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Severe+hearing+loss+and+outer+hair+cell+death+in+homozygous+Foxo3+knockout+mice+after+moderate+noise+exposure.">Severe hearing loss and outer hair cell death in homozygous Foxo3 knockout mice after moderate noise exposure.</a> Scientific Reports. 7, 1054 (2017).</li><li>Rubio, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Auditory+brainstem+development+and+plasticity.">Auditory brainstem development and plasticity.</a> Current Opinion in Physiology. 18, 7-10 (2020).</li><li>Polonenko, M. J., Maddox, R. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+parallel+auditory+brainstem+response.">The parallel auditory brainstem response.</a> Trends in Hearing. 23, 2331216519871395 (2019).</li><li>Shi, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Noise-induced+damage+to+ribbon+synapses+without+permanent+threshold+shifts+in+neonatal+mice.">Noise-induced damage to ribbon synapses without permanent threshold shifts in neonatal mice.</a> Neuroscience. 304, 368-377 (2015).</li><li>Lin, H. W., Furman, A. C., Kujawa, S. G., Liberman, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Primary+neural+degeneration+in+the+Guinea+pig+cochlea+after+reversible+noise-induced+threshold+shift.">Primary neural degeneration in the Guinea pig cochlea after reversible noise-induced threshold shift.</a> Journal of the Association for Research in Otolaryngology. 12 (5), 605-616 (2011).</li></ol>

The authors have no conflicts of interest.

The protocol described in this publication should help streamline the acquisition of data describing voltage amplitude ratios and latency intervals for ABRs to clicks and tone pips. By employing single commands in RStudio, an experimenter may extract, compile, and display this information in a single document for statistical analysis. By making this analysis routine, we hope that the field will discover new ways that the ABR can be altered in development, in aging, or by insult in different species. Such information could be valuable for identifying important mechanisms similar to synaptopathy from noise2. The young mice used for this experiment had highly variable responses, likely because the auditory brainstem is still maturing at this age20. Nonetheless, the two methods of quantification showed very strong correlations (Figure 2). 
The script uses a file called "Time.csv" to set intervals within the data for peak identification. Briefly, a maximal voltage amplitude occurring in a specified time interval is labeled "peak 1", a voltage minimum occurring in the following interval is labeled "trough 1", and so on. We chose the intervals to encompass the latencies of both the click and tone pip responses for CBA/CaJ mice aged 1 month to 12 months old using frequencies spanning 8 kHz to 32 kHz. We successfully used the tool to also measure tone pip responses in mice. Other species, including humans, also have ABR responses within similar windows, and we anticipate that this tool can also be used for data from other species. We would recommend using the new parallel ABR method for humans21, which produces excellent waveforms. The time interval limitation restricts the use of this tool to assessing immediate ABR responses. We note, however, that the interval data in this file could be altered by users to automate the measurements of ABR responses to speech or of event-related potentials (ERPs) that characteristically occur at different times in response to sound.
Some features of the statistical treatment of this data are worth highlighting. To our knowledge, the field does not have a standardized treatment for distinguishing amplitude progressions. Early studies used ANOVA22,23. The data from the click series here (Figure 2) were non-parametric, leading to the use of the Kruskal-Wallis rank sum test. Similar to ANOVA, the Kruskal-Wallis rank sum test assesses differences in the values obtained at a given level of a stimulus; that is, it compares the lines obtained on the graph. However, other treatments are also possible. Biologically, amplitude progressions reflect the additional recruitment of higher-threshold neurons as the stimulus level increases. This suggests that the area under the curve, which represents the integrals of the lines, could be the more relevant measure. Generalized estimating equations (GEE) may be used to model individual data for an integral analysis, as in Patel et al.5. Notably, GEE analysis can take into consideration the repeated measures design of these experiments. As more researchers discuss the data analysis methods, we anticipate the emergence of a consensus on best practices.
In conclusion, this paper presents free and easy-to-use tools for measuring, compiling, and visualizing ABR waveforms. These tools can be used by novice students of RStudio by following this protocol, and they incorporate a blinding step for improved rigor and reproducibility. We foresee that routine ABR waveform analysis will enable the discovery of insults, genetic variants, and other treatments that can affect auditory function.

The auditory brainstem response (ABR) is frequently used to determine hearing thresholds in animal subjects and human infants. As the ABR is an electroencephalogram (EEG) record of the first responses of the nervous system to auditory stimuli, it carries additional information that reflects the coordinated firing of cochlear spiral ganglion neurons and early signal processing in the auditory brainstem, including bilateral processing1. These responses may be affected by noise trauma. For example, noise exposure that is sufficient to induce a temporary threshold shift in mice can also permanently reduce the amplitude of ABR peak 12. Moreover, such trauma may reduce the interpeak latencies and increase the relative amplitudes of the later peaks3, possibly due to a loss of inhibitory regulation4. In addition to these findings, specific genetic mutations have been shown to alter the ABR waveform in the absence of trauma5,6,7. Thus, the routine analysis of ABR waveforms can provide insight into the auditory system in experimental models.
There has also been interest in using ABR waveforms as a diagnostic tool for patients. Previous reports have assessed whether the ABR peak 1 is reduced in human patients after noise exposure or in tinnitus patients8,9. Notably, migraine attacks have been reported to temporarily increase the interpeak latencies for several weeks, after which the ABR waveform returns to normal in affected individuals10. COVID-19 has been reported to drive long-term alterations in ABR interpeak latencies11,12, although another study reported different results13. Hearing loss is often co-morbid with dementia in aging, and individuals with greater hearing loss tend to experience dementia that advances more rapidly14. Researchers have investigated ABR waveform changes in neurodegenerative diseases, such as Parkinson&#39;s disease (reviewed in Jafari et al.15) and Alzheimer&#39;s disease (reviewed in Swords et al.16), as well as in normal aging17. As more researchers and clinicians investigate sensory deficits as biomarkers for common diseases in aging, techniques such as ABR may become routine in health care.
An examination of the methods sections in the literature reveals that labs often write custom scripts in MatLab for analyzing ABR waveforms. The ABR platform made by Intelligent Hearing Systems has a function for waveform analysis, but it requires an operator to manually select the peaks and troughs. Here, we have written semi-automated analysis routines for the open-source, freely available statistical environment R and the RStudio interface. This report compares the data obtained using our routines to the data obtained by having an experimenter manually identify the peaks and troughs and shows that the data from the two methods are strongly correlated. Importantly, the routines incorporate a blinding function, in which the metadata for the samples are placed in a separate file that is not incorporated until the end. These functions have streamlined waveform analysis for our lab.

<table><tbody><tr><td>C57BL/6J&amp;nbsp; mice</td><td>Jackson Labs</td><td>664</td><td></td></tr><tr><td>CBA/CaJ mice</td><td>Jackson Labs</td><td>654</td><td></td></tr><tr><td>E-series PC</td><td>Dell</td><td>n/a (this equipment was discontinued)</td><td>This runs the IHS system.</td></tr><tr><td>Mini-anechoic chamber</td><td>Industrial Acoustics Company</td><td>Special order number 104306</td><td>This enclosure reduces noise levels for auditory testing of animals.</td></tr><tr><td>Optiplex 7040</td><td>Dell</td><td>i5-6500</td><td>Rstudio may also be run on a Mac or Linux system.</td></tr><tr><td>Universal Smart Box</td><td>Intelligent Hearing Systems</td><td>n/a (this equipment was discontinued)</td><td>Both TDT and IHS can output hearing data as ASCII files.</td></tr></tbody></table>

semi-automated analysis of peak amplitude and latency for auditory brainstem response waveforms using r

Many reports in the last 15 years have assessed changes in the auditory brainstem response (ABR) waveform after insults such as noise exposure. Common changes include reductions in the peak 1 amplitude and the relative latencies of the later peaks, as well as increased central gain, which is reflected by a relative increase in the amplitudes of the later peaks compared to the amplitude of peak 1. Many experimenters identify the peaks and troughs visually to assess their relative heights and latencies, which is a laborious process when the waveforms are collected in 5 dB increments throughout the hearing range for each frequency and condition. This paper describes free routines that may be executed in the open-source platform R with the RStudio interface to semi-automate the measurements of the peaks and troughs of auditory brainstem response (ABR) waveforms. The routines identify the amplitudes and latencies of peaks and troughs, display these on a generated waveform for inspection, collate and annotate the results into a spreadsheet for statistical analysis, and generate averaged waveforms for figures. In cases when the automated process misidentifies the ABR waveform, there is an additional tool to assist in correction. The goal is to reduce the time and effort needed to analyze the ABR waveform so that more researchers will include these analyses in the future.

We tested the routines on ABR waveform responses to a click series, beginning at 75 dB and stepping down in 5 dB increments to 5 dB. These data were obtained as previously described19. We also tested the tool on tone pip data and obtained similar results. ABR data from most ABR systems can be exported as .txt (ASCII) files. We loaded ABR ASCII files onto a computer and opened them in RStudio as described in the protocol. After running the FindPeaks.R routine in batch form, we obtained sample waveforms with automated labeling (Figure 1) and a .csv file with the results. The results were reviewed to remove irrelevant peaks. To validate the automated labeling, we also used the ABR program capability to manually label the first five peaks and troughs on each waveform obtained with the click series described above. The experimenter performing this task had 2 years of experience recording and analyzing ABR data. Figure 2 shows this comparison, with the automated FindPeaks.R data in red and the manually obtained data in black. Each trace represents the data from a single mouse. The average for both methods with one standard deviation is also displayed. The results obtained by FindPeaks.R correlate strongly with results obtained manually (see Supplementary Figure S4).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64737/64737fig01.jpg" /> 
Figure 1: Representative waveform response to a 75 dB click stimulus for a young F1 mouse. Latency in milliseconds is plotted on the x-axis, and amplitude in microvolts is plotted on the y-axis. The peaks were automatically identified with FindPeaks.R and are labeled in red, whereas the troughs are labeled in blue. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64737/64737fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64737/64737fig02.jpg" /> 
Figure 2: Comparison of the data obtained from the manually identified peaks to the data provided by the FindPeaks.R analysis. (A,C,E,G,I) Amplitudes in microvolts and (B,D,F,H,J) latencies in milliseconds are plotted for sound levels between 5 dB and 75 dB (x-axis, all graphs) for peaks I-V in the waveforms obtained for click stimuli presented to 12 mice. The manually obtained values (black) are compared to the same datasets analyzed with FindPeaks.R (red). The averages are plotted as heavy lines, with the shaded region representing one standard deviation. No differences were seen between the methods when assessed with the Kruskal-Wallis rank sum test (A, difference = 0.0547977 &#177; 0.0010028, max = 0.96, p = 0.9216; B, difference = &#8722;0.0001734 &#177; 0.0001214, max = 0.04, p = 0.8289; C, difference = &#8722;0.0212209 &#177; 0.0006806, max = 0.92, p = 0.9687; D, difference = &#8722;0.0011047 &#177; 0.0001556, max = 0.06, p = 0.771; E, difference = &#8722;0.0323077 &#177; 0.0006169, max = 0.66, p = 0.899; F, difference = &#8722;0.0072189 &#177; 0.0001460, max = 0.04, p = 0.8644; G, difference = 0.201754 &#177; 0.0007407, max = 0.64, p = 0.9312; H, difference = &#8722;0.0007018 &#177; 0.0001717, max = 0.09, p = 0.8013; I, difference = 0.0347561 &#177; 0.0007343, max = 1.05, p = 0.8856; J, difference = &#8722;0.0078049 &#177; 0.0002762, max = 0.16, p = 0.886), and the results were highly correlated (chi-squared values: A, 0.009696; B, 0.046684; C, 0.0015395; D, 0.084742; E, 0.016102; F, 0.029153; G, 0.0074604; H, 0.063322; I, 0.020699; J, 0.020544; differences presented as mean &#177; SEM; max = absolute maximum difference). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64737/64737fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure S1: Analysis with FindPeaks.R. (A) Selecting the working directory (see protocol step 3.2); (B) loading the program (see protocol step 3.3). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64737/64737_SuppFig1.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure S2: Script output and commands for the analysis of the waveforms. Commands for (A) individual files and (B) batch processing. (C) Output PDF file showing waveforms with labeled peaks and troughs. See protocol step 3.4. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64737/64737_SuppFig2.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure S3: Verification of the analysis. (A) Loading the waveform data (see protocol step 4.1). (B) Location of the Run App button. The sample data file is also indicated. (C) Shiny window with a waveform. In this instance, the sound level is 75 dB, as entered in the top window. Clicking on a desired peak and the following trough records the data for the amplitude and latency in the table (protocol step 4.6). Peak 3 data are shown. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64737/64737_SuppFig3.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure S4: Comparison of the individual data obtained from the manually identified peaks to the data provided by the FindPeaks.R analysis. (A,C,E,G,I) Amplitudes in microvolts and (B,D,F,H,J) latencies in milliseconds are plotted for sound levels between 5 dB and 75 dB (x-axis, all graphs) for peaks I-V in waveforms obtained for click stimuli presented to 12 mice. Each animal is labeled with a unique color, as shown in the legend. The data obtained with FindPeaks.R are labeled with solid colors, while the data obtained manually are labeled with less saturated versions of the same colors. While both data sets are plotted in this figure, when they are identical, only one line is apparent. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64737/64737_SuppFig4.pdf" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R at JoVE.com

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

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.

Many reports in the last 15 years have assessed changes in the auditory brainstem response (ABR) waveform after insults such as noise exposure. Common ...

semi-automated-analysis-peak-amplitude-latency-for-auditory-brainstem

Nanyang Technological University

Research

JoVE Journal

Neuroscience

2.4K Views.  University of Rochester Medical Center. Auditieve hersenstamreacties worden routinematig verzameld om gehoordrempels te bepalen. De ABR-golfvorm bevat aanvullende informatie over hersenstamactiviteit, maar eerder hebben veel onderzoekers deze handmatig moeten analyseren. R-scripts werken met R-software in het RStudio-framework om ABR-golfvormanalyse semi-automatiseren.Ze zijn gratis en gemakkelijk te gebruiken. Onze hoop is dat routinematige analyse van de ABR-golfvorm latere studies in staat zal stellen om de relevantie van...

Video: Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Infant Auditory Processing and Event-related Brain Oscillations

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral and temporal changes that are characteristic of human language. These abilities lay the foundation for effective language acquisition; allowing infants to hone in on the sounds of their native language. Invasive procedures in animals and scalp-recorded potentials from human adults suggest that simultaneous, rhythmic activity (oscillations) between and within brain regions are fundamental to sensory development; determining the resolution with which incoming stimuli are parsed. At this time, little is known about oscillatory dynamics in human infant development. However, animal neurophysiology and adult EEG data provide the basis for a strong hypothesis that rapid auditory processing in infants is mediated by oscillatory synchrony in discrete frequency bands. In order to investigate this, 128-channel, high-density EEG responses of 4-month old infants to frequency change in tone pairs, presented in two rate conditions (Rapid: 70 msec ISI and Control: 300 msec ISI) were examined. To determine the frequency band and magnitude of activity, auditory evoked response averages were first co-registered with age-appropriate brain templates. Next, the principal components of the response were identified and localized using a two-dipole model of brain activity. Single-trial analysis of oscillatory power showed a robust index of frequency change processing in bursts of Theta band (3 - 8 Hz) activity in both right and left auditory cortices, with left activation more prominent in the Rapid condition. These methods have produced data that are not only some of the first reported evoked oscillations analyses in infants, but are also, importantly, the product of a well-established method of recording and analyzing clean, meticulously collected, infant EEG and ERPs. In this article, we describe our method for infant EEG net application, recording, dynamic brain response analysis, and representative results.

High-density electroencephalography (dEEG) is being used increasingly to study brain development and plasticity in the early years of life. Here we present an application of sophisticated analysis techniques that builds on traditional EEG recording to understand the oscillatory dynamics of rapid auditory processing in the infant brain.

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral ...

Behavior

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

In spite of the success in applying non-invasive electroencephalography (EEG), magneto-encephalography (MEG) and functional magnetic resonance imaging (fMRI) for extracting crucial information about the mechanism of the human brain, such methods remain insufficient to provide information about physiological processes reflecting cognitive and emotional functions at the subcortical level. In this respect, modern invasive clinical approaches in humans, such as deep brain stimulation (DBS), offer a tremendous possibility to record subcortical brain activity, namely local field potentials (LFPs) representing coherent activity of neural assemblies from localized basal ganglia or thalamic regions. Notwithstanding the fact that invasive approaches in humans are applied only after medical indication and thus recorded data correspond to altered brain circuits, valuable insight can be gained regarding the presence of intact brain functions in relation to brain oscillatory activity and the pathophysiology of disorders in response to experimental cognitive paradigms. In this direction, a growing number of DBS studies in patients with Parkinson&#39;s disease (PD) target not only motor functions but also higher level processes such as emotions, decision-making, attention, memory and sensory perception. Recent clinical trials also emphasize the role of DBS as an alternative treatment in neuropsychiatric disorders ranging from obsessive compulsive disorder (OCD) to chronic disorders of consciousness (DOC). Consequently, we focus on the use of combined invasive (LFP) and non-invasive (EEG) human brain recordings in assessing the role of cortical-subcortical structures in cognitive and emotional processing trough experimental paradigms (e.g. speech stimuli with emotional connotation or paradigms of cognitive control such as the Flanker task), for patients undergoing DBS treatment.

The present protocol aims at assessing cognitive-emotional functions in the basal ganglia by simultaneous neurophysiological recording of local field potentials and non-invasive brain cortical activity (EEG). The procedure is exemplified by the use of paradigms involving speech stimuli with emotional connotation or the Flanker task involving cognitive control.

In spite of the success in applying non-invasive electroencephalography (EEG), magneto-encephalography (MEG) and functional magnetic resonance imaging ...

EEG Study on Recognizing Familiar Speakers in Expressive Speech

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Recognizing familiar speakers from vocal streams is a fundamental aspect of human verbal communication. However, it remains unclear how listeners can still discern the speaker's identity in expressive speech. This study develops a memorization-based individual speaker identity recognition approach and an accompanying electroencephalogram (EEG) data analysis pipeline, which monitors how listeners recognize familiar speakers and tell unfamiliar ones apart. EEG data captures online cognitive processes during new versus old speaker distinction based on voice, offering a real-time measure of brain activity, overcoming limits of reaction times and accuracy measurements. The paradigm comprises three steps: listeners establish associations between three voices and their names (training); listeners indicate the name corresponding to a voice from three candidates (checking); listeners distinguish between three old and three new speaker voices in a two-alternative forced-choice task (testing). The speech prosody in testing was either confident or doubtful. EEG data were collected using a 64-channel EEG system, followed by preprocessing and imported into RStudio for ERP and statistical analysis and MATLAB for brain topography. Results showed an enlarged late positive component (LPC) was elicited in the old-talker compared to the new-talker condition in the 400-850 ms window in the Pz and other wider range of electrodes in both prosodies. Yet, the old/new effect was robust in central and posterior electrodes for doubtful prosody perception, whereas the anterior, central, and posterior electrodes are for confident prosody condition. This study proposes that this experiment design can serve as a reference for investigating speaker-specific cue-binding effects in various scenarios (e.g., anaphoric expression) and pathologies in patients like phonagnosia.

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

The study introduces a training-testing paradigm to investigate old/new effects of event-related potentials in confident and doubtful prosodic scenarios. Data reveals an enhanced late positive component between 400-850 ms at Pz and other electrodes. This pipeline can explore factors beyond speech prosody and their influence on cue-binding target identification.

Recognizing familiar speakers from vocal streams is a fundamental aspect of human verbal communication. However, it remains unclear how listeners can ...

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Brainstem evoked response audiometry (BERA) is of central relevance in the clinical neurophysiology. As other evoked potential (EP) techniques, such as visually evoked potentials (VEPs) or somatosensory evoked potentials (SEPs), the auditory evoked potentials (AEPs) are triggered by the repetitive presentation of identical stimuli, the electroencephalographic (EEG) response of which is subsequently averaged resulting in distinct positive (p) and negative (n) deflections. In humans, both the amplitude and the latency of individual peaks can be used to characterize alterations in synchronization and conduction velocity in the underlying neuronal circuitries. Importantly, AEPs are also applied in basic and preclinical science to identify and characterize the auditory function in pharmacological and genetic animal models. Even more, animal models in combination with pharmacological testing are utilized to investigate for potential benefits in the treatment of sensorineural hearing loss (e.g., age- or noise-induced hearing deficits). Here we provide a detailed and integrative description of how to record auditory brainstem-evoked responses (ABRs) in mice using click and tone-burst application. A specific focus of this protocol is on pre-experimental animal housing, anesthesia, ABR recording, ABR filtering processes, automated wavelet-based amplitude growth function analysis, and latency detection.

Brainstem evoked response audiometry is an important tool in clinical neurophysiology. Nowadays, brainstem evoked response audiometry is also applied in the basic science and preclinical studies involving both pharmacological and genetic animal models. Here we provide a detailed description of how auditory brainstem responses can be successfully recorded and analyzed in mice.

Brainstem evoked response audiometry (BERA) is of central relevance in the clinical neurophysiology. As other evoked potential (EP) techniques, such as ...

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Neural entrainment refers to the synchronization of neural activity to the periodicity of sensory stimuli. This synchronization defines the generation of steady-state evoked responses (i.e., oscillations in the electroencephalogram phase-locked to the driving stimuli). The classic interpretation of the amplitude of the steady-state evoked responses assumes a stereotypical time-invariant neural response plus random background fluctuations, such that averaging over repeated presentations of the stimulus recovers the stereotypical response. This approach ignores the dynamics of the steady-state, as in the case of the adaptation elicited by prolonged exposures to the stimulus. To analyze the dynamics of steady-state responses, it can be assumed that the time evolution of the response amplitude is the same in different stimulation runs separated by sufficiently long breaks. Based on this assumption, a method to characterize the time evolution of steady-state responses is presented. A sufficiently large number of recordings are acquired in response to the same experimental condition. Experimental runs (recordings) are column-wise averaged (i.e., runs are averaged but epoch within recordings are not averaged with the preceding segments). The column-wise averaging allows analysis of steady-state responses in recordings with remarkably high signal-to-noise ratios. Therefore, the averaged signal provides an accurate representation of the time evolution of the steady-state response, which can be analyzed in both the time and frequency domains. In this study, a detailed description of the method is provided, using steady-state visually evoked potentials as an example of a response. Advantages and caveats are evaluated based on a comparison with single-trial methods designed to analyze neural entrainment.

A protocol to assess the time evolution of the neural entrainment to external repetitive stimuli is presented. Steady-state recordings of the same experimental condition are acquired and averaged in the time-domain. The steady-state dynamics are analyzed by plotting the response amplitude as a function of time.

Neural entrainment refers to the synchronization of neural activity to the periodicity of sensory stimuli. This synchronization defines the generation of ...

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

A portable system capable of measuring steady-state visual-evoked potentials (SSVEP) was developed to provide an objective, quantifiable method of electroencephalogram (EEG) testing following a traumatic event. In this study, the portable system was used on 65 healthy rugby players throughout a season to determine whether SSVEP are a reliable electrophysiological biomarker for concussion. Preceding the competition season, all players underwent a baseline SSVEP assessment. During the season, players were re-tested within 72 h of a match for either test-retest reliability or post-injury assessment. In the case of a medically diagnosed concussion, players were reassessed again once deemed recovered by a physician. The SSVEP system consisted of a smartphone housed in a VR-frame delivering a 15 Hz flicker stimulus, while a wireless EEG headset recorded occipital activity. Players were instructed to stare at the screen's fixation point while remaining seated and quiet. Electrodes were arranged according to the 10-20 EEG-positioning nomenclature, with O1-O2 being the recording channels while P1-P2 the references and bias, respectively. All EEG data was processed using a Butterworth bandpass filter, Fourier transformation, and normalization to convert data for frequency analysis. Players SSVEP responses were quantified into a signal-to-noise ratio (SNR), with 15 Hz being the desired signal, and summarized into respective study groups for comparison. Concussed players were seen to have a significantly lower SNR compared to their baseline; however, post-recovery, their SNR was not significantly different from the baseline. Test-retest indicated high device reliability for the portable system. An improved portable SSVEP system was also validated against an established EEG amplifier to ensure the investigative design is capable of obtaining research quality EEG measurements. This is the first study to identify differences in SSVEP responses in amateur athletes following a concussion and indicates the potential for SSVEP as an aid in concussion assessment and management.

A portable system capable of measuring steady-state visual-evoked potentials was developed and trialed on 65 amateur rugby players over 18 weeks to investigate SSVEP as a potential electrophysiological biomarker for concussion. Players' baselines were measured pre-season, with retesting for reliability, concussion, and recovery assessment being conducted within controlled time-periods, respectively.

A portable system capable of measuring steady-state visual-evoked potentials (SSVEP) was developed to provide an objective, quantifiable method of ...

Implantation of Combined Telemetric ECG and Blood Pressure Transmitters to Determine Spontaneous Baroreflex Sensitivity in Conscious Mice

Blood pressure (BP) and heart rate (HR) are both controlled by the autonomic nervous system (ANS) and are closely intertwined due to reflex mechanisms. The baroreflex is a key homeostatic mechanism to counteract acute, short-term changes in arterial BP and to maintain BP in a relatively narrow physiological range. BP is sensed by baroreceptors located in the aortic arch and carotid sinus. When BP changes, signals are transmitted to the central nervous system and are then communicated to the parasympathetic and sympathetic branches of the autonomic nervous system to adjust HR. A rise in BP causes a reflex decrease in HR, a drop in BP causes a reflex increase in HR.
Baroreflex sensitivity (BRS) is the quantitative relationship between changes in arterial BP and corresponding changes in HR. Cardiovascular diseases are often associated with impaired baroreflex function. In various studies reduced BRS has been reported in e.g., heart failure, myocardial infarction, or coronary artery disease. 
Determination of BRS requires information from both BP and HR, which can be recorded simultaneously using telemetric devices. The surgical procedure is described beginning with the insertion of the pressure sensor into the left carotid artery and positioning of its tip in the aortic arch to monitor arterial pressure followed by the subcutaneous placement of the transmitter and ECG electrodes. We also describe postoperative intensive care and analgesic management. After a two-week period of post-surgery recovery long-term ECG and BP recordings are performed in conscious and unrestrained mice. Finally, we include examples of high-quality recordings and the analysis of spontaneous baroreceptor sensitivity using the sequence method.

The baroreflex is a heart-rate regulation mechanism by the autonomic nervous system in response to blood-pressure changes. We describe a surgical technique to implant telemetry transmitters for continuous and simultaneous measurement of electrocardiogram and blood pressure in mice. This can determine spontaneous baroreflex sensitivity, an important prognostic marker for cardiovascular disease.

Blood pressure (BP) and heart rate (HR) are both controlled by the autonomic nervous system (ANS) and are closely intertwined due to reflex mechanisms. ...

Medicine

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

This paper examines the application of electroencephalogram-based methods to assess the effects of audio-tactile substitution training in young, profoundly deaf (PD) participants, with the aim of analyzing the neural mechanisms associated with vibrotactile complex sound discrimination. Electrical brain activity reflects dynamic neural changes, and the temporal precision of event-related potentials (ERPs) has proven to be key in studying time-locked processes while performing behavioral tasks that involve attention and working memory. 
The current protocol was designed to study electrophysiological activity in PD subjects while they performed a continuous performance task (CPT) using complex-sound stimuli, consisting of five different animal sounds delivered through a portable stimulator system worn on the right index finger. As a repeated-measures design, electroencephalogram (EEG) recordings in standard conditions were performed before and after a brief training program (five 1 h sessions over 15 days), followed by offline artifact correction and epoch averaging, to obtain individual and grand-mean waveforms. Behavioral results show a significant improvement in discrimination and a more robust P3-like centroparietal positive waveform for the target stimuli after training. In this protocol, ERPs contribute to the further understanding of learning-related neural changes in PD subjects associated with audio-tactile discrimination of complex sounds.

This protocol is designed to explore underlying learning-related electrophysiological changes in subjects with profound deafness after a short training period in audio-tactile sensory substitution by applying the event-related potential technique.

This paper examines the application of electroencephalogram-based methods to assess the effects of audio-tactile substitution training in young, ...

Unveiling Brain-Heart Interplay with EEG and Cardio Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

The BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It includes heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. The protocol will aid in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human ...

Recording Auditory Brainstem Responses in a Rat Pup

Source: Chang, A. et al., <a href="https://app-jove-com.remotexs.ntu.edu.sg/v/56678">Auditory Brainstem Response and Outer Hair Cell Whole-cell Patch Clamp Recording in Postnatal Rats.</a> J. Vis. Exp. (2018).
This video demonstrates a method for recording auditory brainstem responses (ABRs) from rat pups. It outlines the steps for electrode placement, sound stimulus application, and signal processing analysis in the brainstem to assess neural responses.

All procedures involving animal models have been reviewed by the local institutional animal care committee and the JoVE veterinary review board.
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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

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