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>

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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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>C57BL/6J&#160; 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/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/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/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/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/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/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 ...

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2.4K Views.  University of Rochester Medical Center. 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.

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

Preparation and Culture of Chicken Auditory Brainstem Slices

The chicken auditory brainstem is a well-established model system that has been widely used to study the anatomy and physiology of auditory processing at discreet periods of development 1-4 as well as mechanisms for temporal coding in the central nervous system 5-7.
Here we present a method to prepare chicken auditory brainstem slices that can be used for acute experimental procedures or to culture organotypic slices for long-term experimental manipulations.
The chicken auditory brainstem is composed of nucleus angularis, magnocellularis, laminaris and superior olive. These nuclei are responsible for binaural sound processing and single coronal slice preparations preserve the entire circuitry. Ultimately, organotypic slice cultures can provide the opportunity to manipulate several developmental parameters such as synaptic activity, expression of pre and postsynaptic components, expression of aspects controlling excitability and differential gene expression
This approach can be used to broaden general knowledge about neural circuit development, refinement and maturation.

The chicken auditory brainstem is comprised of nuclei responsible for binaural sound processing. A single coronal slice preparation maintains the entire circuitry while the cultured approach provides a unique preparation to study the development of neuronal structure and auditory function at the molecular, cellular and network levels.

The chicken auditory brainstem is a well-established model system that has been widely used to study the anatomy and physiology of auditory processing at ...

Visualization and Genetic Manipulation of Dendrites and Spines in the Mouse Cerebral Cortex and Hippocampus using In utero Electroporation

In utero electroporation (IUE) has become a powerful technique to study the development of different regions of the embryonic nervous system 1-5. To date this tool has been widely used to study the regulation of cellular proliferation, differentiation and neuronal migration especially in the developing cerebral cortex 6-8. Here we detail our protocol to electroporate in utero the cerebral cortex and the hippocampus and provide evidence that this approach can be used to study dendrites and spines in these two cerebral regions.
Visualization and manipulation of neurons in primary cultures have contributed to a better understanding of the processes involved in dendrite, spine and synapse development. However neurons growing in vitro are not exposed to all the physiological cues that can affect dendrite and/or spine formation and maintenance during normal development. Our knowledge of dendrite and spine structures in vivo in wild-type or mutant mice comes mostly from observations using the Golgi-Cox method 9. However, Golgi staining is considered to be unpredictable. Indeed, groups of nerve cells and fiber tracts are labeled randomly, with particular areas often appearing completely stained while adjacent areas are devoid of staining. Recent studies have shown that IUE of fluorescent constructs represents an attractive alternative method to study dendrites, spines as well as synapses in mutant / wild-type mice 10-11 (Figure 1A). Moreover in comparison to the generation of mouse knockouts, IUE represents a rapid approach to perform gain and loss of function studies in specific population of cells during a specific time window. In addition, IUE has been successfully used with inducible gene expression or inducible RNAi approaches to refine the temporal control over the expression of a gene or shRNA 12. These advantages of IUE have thus opened new dimensions to study the effect of gene expression/suppression on dendrites and spines not only in specific cerebral structures (Figure 1B) but also at a specific time point of development (Figure 1C).
Finally, IUE provides a useful tool to identify functional interactions between genes involved in dendrite, spine and/or synapse development. Indeed, in contrast to other gene transfer methods such as virus, it is straightforward to combine multiple RNAi or transgenes in the same population of cells. 
In summary, IUE is a powerful method that has already contributed to the characterization of molecular mechanisms underlying brain function and disease and it should also be useful in the study of dendrites and spines.

Visualization and Genetic Manipulation of Dendrites and Spines in the Mouse Cerebral Cortex and Hippocampus using In utero Electroporation

This article describes in detail a protocol to electroporate in utero the cerebral cortex and the hippocampus at E14.5 in mice. We also show that this is a valuable method to study dendrites and spines in these two cerebral regions.

In utero electroporation (IUE) has become a powerful technique to study the development of different regions of the embryonic nervous system 1-5. To date ...

Selective Tracing of Auditory Fibers in the Avian Embryonic Vestibulocochlear Nerve

The embryonic chick is a widely used model for the study of peripheral and central ganglion cell projections. In the auditory system, selective labeling of auditory axons within the VIIIth cranial nerve would enhance the study of central auditory circuit development. This approach is challenging because multiple sensory organs of the inner ear contribute to the VIIIth nerve 1. Moreover, markers that reliably distinguish auditory versus vestibular groups of axons within the avian VIIIth nerve have yet to be identified. Auditory and vestibular pathways cannot be distinguished functionally in early embryos, as sensory-evoked responses are not present before the circuits are formed. Centrally projecting VIIIth nerve axons have been traced in some studies, but auditory axon labeling was accompanied by labeling from other VIIIth nerve components 2,3. Here, we describe a method for anterograde tracing from the acoustic ganglion to selectively label auditory axons within the developing VIIIth nerve. First, after partial dissection of the anterior cephalic region of an 8-day chick embryo immersed in oxygenated artificial cerebrospinal fluid, the cochlear duct is identified by anatomical landmarks. Next, a fine pulled glass micropipette is positioned to inject a small amount of rhodamine dextran amine into the duct and adjacent deep region where the acoustic ganglion cells are located. Within thirty minutes following the injection, auditory axons are traced centrally into the hindbrain and can later be visualized following histologic preparation. This method provides a useful tool for developmental studies of peripheral to central auditory circuit formation.

Here we describe a microdissection technique followed by fluorescent dye injection into the acoustic ganglion of early chick embryos for selective tracing of auditory axon fibers in the nerve and hindbrain.

The embryonic chick is a widely used model for the study of peripheral and central ganglion cell projections. In the auditory system, selective labeling ...

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Current knowledge of sensory processing in the mammalian auditory system is mainly derived from electrophysiological studies in a variety of animal models, including monkeys, ferrets, bats, rodents, and cats. In order to draw suitable parallels between human and animal models of auditory function, it is important to establish a bridge between human functional imaging studies and animal electrophysiological studies. Functional magnetic resonance imaging (fMRI) is an established, minimally invasive method of measuring broad patterns of hemodynamic activity across different regions of the cerebral cortex. This technique is widely used to probe sensory function in the human brain, is a useful tool in linking studies of auditory processing in both humans and animals and has been successfully used to investigate auditory function in monkeys and rodents. The following protocol describes an experimental procedure for investigating auditory function in anesthetized adult cats by measuring stimulus-evoked hemodynamic changes in auditory cortex using fMRI. This method facilitates comparison of the hemodynamic responses across different models of auditory function thus leading to a better understanding of species-independent features of the mammalian auditory cortex.

Functional studies of the auditory system in mammals have traditionally been conducted using spatially-focused techniques such as electrophysiological recordings. The following protocol describes a method of visualizing large-scale patterns of evoked hemodynamic activity in the cat auditory cortex using functional magnetic resonance imaging.

Current knowledge of sensory processing in the mammalian auditory system is mainly derived from electrophysiological studies in a variety of animal ...

Optogenetic Stimulation of the Auditory Nerve

Direct electrical stimulation of spiral ganglion neurons (SGNs) by cochlear implants (CIs) enables open speech comprehension in the majority of implanted deaf subjects1-6. Nonetheless, sound coding with current CIs has poor frequency and intensity resolution due to broad current spread from each electrode contact activating a large number of SGNs along the tonotopic axis of the cochlea7-9. Optical stimulation is proposed as an alternative to electrical stimulation that promises spatially more confined activation of SGNs and, hence, higher frequency resolution of coding. In recent years, direct infrared illumination of&#160;the cochlea has been used to evoke responses in the auditory nerve10. Nevertheless it requires higher energies than electrical stimulation10,11 and uncertainty remains as to the underlying mechanism12. Here we describe a method based on optogenetics to stimulate SGNs with low intensity blue light, using transgenic mice with neuronal expression of channelrhodopsin 2 (ChR2)13 or virus-mediated expression of the ChR2-variant CatCh14. We used micro-light emitting diodes (&#181;LEDs) and fiber-coupled lasers to stimulate ChR2-expressing SGNs through a small artificial opening (cochleostomy) or the round window. We assayed the responses by scalp recordings of light-evoked potentials (optogenetic auditory brainstem response: oABR) or by microelectrode recordings from the auditory pathway and compared them with acoustic and electrical stimulation.

Cochlear implants (CIs) enable hearing by direct electrical stimulation of the auditory nerve. However, poor frequency and intensity resolution limits the quality of hearing with CIs. Here we describe optogenetic stimulation of the auditory nerve in mice as an alternative strategy for auditory research and developing future CIs.

Direct electrical stimulation of spiral ganglion neurons (SGNs) by cochlear implants (CIs) enables open speech comprehension in the majority of implanted ...

Electroretinogram Analysis of the Visual Response in Zebrafish Larvae

The electroretinogram (ERG) is a noninvasive electrophysiological method for determining retinal function. Through the placement of an electrode on the surface of the cornea, electrical activity generated in response to light can be measured and used to assess the activity of retinal cells in vivo. This manuscript describes the use of the ERG to measure visual function in zebrafish. Zebrafish have long been utilized as a model for vertebrate development due to the ease of gene suppression by morpholino oligonucleotides and pharmacological manipulation. At 5-10 dpf, only cones are functional in the larval retina. Therefore, the zebrafish, unlike other animals, is a powerful model system for the study of cone visual function in vivo. This protocol uses standard anesthesia, micromanipulation and stereomicroscopy protocols that are common in laboratories that perform zebrafish research. The outlined methods make use of standard electrophysiology equipment and a low light camera to guide the placement of the recording microelectrode onto the larval cornea. Finally, we demonstrate how a commercially available ERG stimulator/recorder originally designed for use with mice can easily be adapted for use with zebrafish. ERG of larval zebrafish provides an excellent method of assaying cone visual function in animals that have been modified by morpholino oligonucleotide injection as well as newer genome engineering techniques such as Zinc Finger Nucleases (ZFNs), Transcription Activator-Like Effector Nucleases (TALENs), and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9, all of which have greatly increased the efficiency and efficacy of gene targeting in zebrafish. In addition, we take advantage of the ability of pharmacological agents to penetrate zebrafish larvae to evaluate the molecular components that contribute to the photoresponse. This protocol outlines a setup that can be modified and used by researchers with various experimental goals.

We present a method for the electroretinographic (ERG) analysis of zebrafish larvae utilizing micromanipulation and electroretinography techniques. This is a simple and straightforward method for assaying visual function of zebrafish larvae in vivo.

The electroretinogram (ERG) is a noninvasive electrophysiological method for determining retinal function. Through the placement of an electrode on the ...

Extracellular Recording of Neuronal Activity Combined with Microiontophoretic Application of Neuroactive Substances in Awake Mice

Differences in the activity of neurotransmitters and neuromodulators, and consequently different neural responses, can be found between anesthetized and awake animals. Therefore, methods allowing the manipulation of synaptic systems in awake animals are required in order to determine the contribution of synaptic inputs to neuronal processing unaffected by anesthetics. Here, we present methodology for the construction of electrodes to simultaneously record extracellular neural activity and release multiple neuroactive substances at the vicinity of the recording sites in awake mice. By combining these procedures, we performed microiontophoretic injections of gabazine to selectively block GABAA receptors in neurons of the inferior colliculus of head-restrained mice. Gabazine successfully modified neural response properties such as the frequency response area and stimulus-specific adaptation. Thus, we demonstrate that our methods are suitable for recording single-unit activity and for dissecting the role of specific neurotransmitter receptors in auditory processing.
The main limitation of the described procedure is the relatively short recording time (~3 hr), which is determined by the level of habituation of the animal to the recording sessions. On the other hand, multiple recording sessions can be performed in the same animal. The advantage of this technique over other experimental procedures used to manipulate the level of neurotransmission or neuromodulation (such as systemic injections or the use of optogenetic models), is that the drug effect is confined to the local synaptic inputs to the target neuron. In addition, the custom-manufacture of electrodes allows adjustment of specific parameters according to the neural structure and type of neuron of interest (such as the tip resistance for improving the signal-to-noise ratio of the recordings).

We present methods for the construction of electrodes to simultaneously record extracellular neural activity and release multiple neuroactive substances at the vicinity of the recording sites in awake mice. This technique allows the detailed analysis of putative local synaptic inputs to the neuron of interest.

Differences in the activity of neurotransmitters and neuromodulators, and consequently different neural responses, can be found between anesthetized and ...

Using an Adapted Microfluidic Olfactory Chip for the Imaging of Neuronal Activity in Response to Pheromones in Male C. Elegans Head Neurons

The use of calcium indicators has greatly enhanced our understanding of neural dynamics and regulation. The nematode Caenorhabditis elegans, with its completely mapped nervous system and transparent anatomy, presents an ideal model for understanding real-time neural dynamics using calcium indicators. In combination with microfluidic technologies and experimental designs, calcium-imaging studies using these indicators are performed in both free-moving and trapped animals. However, most previous studies utilizing trapping devices, such as the olfactory chip described in Chronis et al., have devices designed for use in the more common hermaphrodite, as the less common male is both morphologically and structurally dissimilar. An adapted olfactory chip was designed and fabricated for increased efficiency in male neuronal imaging with using young adult animals. A turn was incorporated into the worm loading port to rotate the animals and to allow for the separation of the individual neurons within a bilateral pair in 2D imaging. Worms are exposed to a controlled flow of odorant within the microfluidic device, as described in previous hermaphrodite studies. Calcium transients are then analyzed using the open-source software ImageJ. The procedure described herein should allow for an increased amount of male-based C. elegans calcium imaging studies, deepening our understanding of the mechanisms of sex-specific neuronal signaling.

Using an Adapted Microfluidic Olfactory Chip for the Imaging of Neuronal Activity in Response to Pheromones in Male C. Elegans Head Neurons

The use of an adapted &#34;olfactory chip&#34; for the efficient calcium imaging of C. elegans males is described here. Studies of male exposure to glycerol and a pheromone are also shown.

The use of calcium indicators has greatly enhanced our understanding of neural dynamics and regulation. The nematode Caenorhabditis elegans, with its ...

Auditory Brainstem Response and Outer Hair Cell Whole-cell Patch Clamp Recording in Postnatal Rats

The outer hair cell is one of the two types of sensory hair cells in the mammalian cochlea. They alter their cell length with the receptor potential to amplify the weak vibration of low-level sound signal. The morphology and electrophysiological property of outer hair cells (OHCs) develop in early postnatal ages. The maturation of outer hair cell may contribute to the development of the auditory system. However, the process of OHCs development is not well studied. This is partly because of the difficulty to measure their function by an electrophysiological approach. With the purpose of developing a simple method to address the above issue, here we describe a step-by-step protocol to study the function of OHCs in acutely dissociated cochlea from postnatal rats. With this method, we can evaluate the cochlear response to pure tone stimuli and examine the expression level and function of the motor protein prestin in OHCs. This method can also be used to investigate the inner hair cells (IHCs).

This protocol describes methods to record the auditory brainstem response from postnatal rat pups. To examine the functional development of outer hair cells, the experimental procedure of whole-cell patch clamp recording in isolated outer hair cells is described step-by-step.

The outer hair cell is one of the two types of sensory hair cells in the mammalian cochlea. They alter their cell length with the receptor potential to ...

Cochlear Implant Surgery and Electrically-evoked Auditory Brainstem Response Recordings in C57BL/6 Mice

Cochlear implants (CIs) are neuroprosthetic devices that can provide a sense of hearing to deaf people. However, a CI cannot restore all aspects of hearing. Improvement of the implant technology is needed if CI users are to perceive music and perform in more natural environments, such as hearing out a voice with competing talkers, reflections, and other sounds. Such improvement requires experimental animals to better understand the mechanisms of electric stimulation in the cochlea and its responses in the whole auditory system. The mouse is an increasingly attractive model due to the many genetic models available. However, the limited use of this species as a CI model is mainly due to the difficulty of implanting small electrode arrays. More details about the surgical procedure are therefore of great interest to expand the use of mice in CI research.
In this report, we describe in detail the protocol for acute deafening and cochlear implantation of an electrode array in the C57BL/6 mouse strain. We demonstrate the functional efficacy of this procedure with electrically-evoked auditory brainstem response (eABR) and show examples of facial nerve stimulation. Finally, we also discuss the importance of including a deafening procedure when using a normally hearing animal. This mouse model provides a powerful opportunity to study genetic and neurobiological mechanisms that would be of relevance for CI users.

Animal models of cochlear implants can advance knowledge of the technological bases of treating permanent sensorineural hearing loss with electrical stimulation. This study presents a surgical protocol for acute deafening and cochlear implantation of an electrode array in mice as well as the functional assessment with auditory brainstem response.

Cochlear implants (CIs) are neuroprosthetic devices that can provide a sense of hearing to deaf people. However, a CI cannot restore all aspects of ...