This work supported in part by a NIH grant HL-65272. The authors thank Zachary T. Glovak and Clarence E. Locklear for their contributions to this project.

All procedures involving mice adhered to the Guide for the Care and Use of Laboratory Animals (8th edition, National Academies Press, Washington DC, 2011) and were reviewed and approved by the University of Tennessee Institutional Animal Care and Use Committee.
1. Implantation of Recording Electrodes and Initial Data Collection
<ol>
	<li>Purchase mice and keep them in a humidity- and temperature-controlled room with ad libitum access to food and water. Allow mice to adapt to their new environment for one week before surgical implantation of recording electrodes. The implantation procedure has been described in detail1,14.</li>
	<li>Sterilize all surgical equipment.</li>
	<li>Anesthetize mice with isoflurane at 2.5%&#8722;3% delivered in 100% oxygen.</li>
	<li>Following the loss of the righting reflex, remove the mouse from the anesthesia induction chamber and transfer it to a stereotaxic frame.</li>
	<li>Apply an ophthalmic ointment to both eyes.</li>
	<li>Reduce the isoflurane to 1.7%, continuously delivered via a mask.</li>
	<li>Make a midline scalp incision to expose the skull.</li>
	<li>Drill two craniotomies above the left and right cortex (each at stereotaxic coordinates anterior = 1.0 and lateral = 3.0 relative to the bregma15).</li>
	<li>Insert the EEG electrodes into each craniotomy and secure with dental acrylic.</li>
	<li>Implant bipolar electrodes in the dorsal trapezius muscle for recording the electromyogram (EMG). 
	NOTE: The four electrodes are led to a wireless telemeter implanted subcutaneously above the lower right body quadrant. These surgical techniques can be viewed here (https://www.datasci.com/services/dsi-surgical-services/surgical-videos).</li>
	<li>After the surgery, administer analgesic carprofen and place the mouse in a warm recovery cage. Observe the mouse until it is ambulatory. House implanted mice individually.</li>
	<li>Upon full recovery from the surgery, handle mice daily and evaluate the quality of the EEG and EMG recordings.</li>
	<li>Configure the data acquisition system to record all signals &#177; 1,000 mV.</li>
	<li>Obtain EEG and EMG recordings for the duration needed.</li>
	<li>Score each 10 s bin of the digital EEG and EMG recordings as wakefulness, rapid eye movement (REM) sleep, or non-REM (NREM) sleep using sleep scoring software. 
	NOTE: Among mouse strains there are genotype- and state-specific differences in EEG power expressed as a percentage of the total power16. In B6 mice, states of wakefulness are characterized by a 75&#8722;100 mV, mixed frequency EEG, and by EMG signals showing prominent muscle tone with large increases in amplitude during movement. Criteria for scoring NREM sleep include a reduction in EMG amplitude relative to the EMG amplitude of wakefulness. The NREM sleep EEG has a slower frequency and increased amplitude (100&#8722;150 mV) compared to wakefulness. REM sleep is characterized by muscle atonia and an EEG signal that is similar to the EEG of wakefulness.</li>
	<li>Instruct two individuals to independently score the same record. At least one individual should be blinded to the treatment condition. Concordance values between the two sleep scorers should be greater than 90%.</li>
</ol>
2. Facilities and Equipment
<ol>
	<li>Amplify and digitize unfiltered EEG and EMG signals using data acquisition instrumentation and software. 
	NOTE: The Chronux Spectral Analysis Toolbox developed in the Mitra Laboratory at Cold Spring Harbor Laboratory is used to express EEG signals as power in relation to time and frequency domains.</li>
</ol>
3. Spectrogram Computation
<ol>
	<li>If a Windows user, use the compiled program.</li>
	<li>If a Macintosh user, run the raw code file.</li>
	<li>Obtain raw, unprocessed EEG data in either EDF or CSV file format and place it in the same location as the compiled program file.
	<ol>
		<li>Name the data files using the following constraints: names must consist of only letters, numbers, underscores, or dashes.</li>
		<li>Name the data files using the following constraints: filenames must not contain periods, commas, spaces, or any other symbols.</li>
	</ol>
	</li>
	<li>Download the compiled Multitaper Spectrogram Program (<a href="https://drive.google.com/drive/folders/1cyb3T3IkjQvkc-H6lm__17CpRMqkTbNt?usp=sharing" target="_blank">https://drive.google.com/</a> or contact C. O'Brien at <a href="mailto:cobrien8@vols.utk.edu">cobrien8@vols.utk.edu</a>).</li>
	<li>Launch the spectrogram program and follow the pop-up prompts. Choose file type: *.CSV or *.EDF. 
	NOTE: Further program installation details are located in the readme.txt file.</li>
	<li>Type the entire EEG file name (e.g., 419eeg.edf or 419.eeg.csv).</li>
	<li>Select parameters for spectrogram calculation: Default or New. This step requires the longest processing time as the spectrogram is being computed. The mathematical windowing function (taper) provides statistically-independent estimates of the underlying spectrum. The longer the recording duration, the longer this step will take. On a PC platform running Windows 10 this required a maximum of about 3&#8722;4 min for a 4 h recording.
	<ol>
		<li>Use the following default spectrogram parameters: 
		Sampling frequency = 500 Hz. This represents the number of samples per second. 
		fpass = 0.3 Hz and 30 Hz. Fpass defines the frequencies of input and controls the range of frequencies supplied in the output. 
		Padding = 2. Padding works to finely interpolate the output without affecting the result calculation in any way. This may assist with visualization and the precise identification of spectral lines. The field is any integer from -1 and up. 
		Time-bandwidth product (NW) = 15. The product of the signal temporal duration and spectral width. 
		Number of tapers = 29. When choosing number of tapers, it is essential to use 2NW-1. There is no limit on the number of tapers used. The more tapers used will result in the inclusion of tapers with poor concentration in the specified frequency bandwidth. 
		Trial average = 1. This parameter governs whether or not trial or channel averaging is performed. If this parameter is set to 0, there is no channel averaging and the function will output independent results for each trial or channel passed as input data. However, if the trial average is set to 1, the results output to the user are averaged over trials or channels. 
		Time to compute FFT ~30 s. Used to follow the evolution of the spectrum by calculating the spectrum over many small windows. 
		Step size of window for FFT computation = 5. The amount the sliding time window advances after each spectrum calculation is performed. 
		NOTE: The default spectrogram parameters stated in step 3.7.1 can be changed as needed.</li>
	</ol>
	</li>
	<li>Enter titles for both the spectrogram and EEG.</li>
	<li>Save the resulting spectrogram and EEG.
	<ol>
		<li>Save figures by clicking File | Save in the figure window. 
		NOTE: The figures will provide program users with summaries that can be developed into publication-quality figures.</li>
	</ol>
	</li>
</ol>
4. Troubleshooting
<ol>
	<li>Download the sample mouse sleep EEG data for sample spectrogram computation.</li>
	<li>Run program with the sample data to ensure the user is correctly using the program. Find the figures for these sample data in the appendix to ensure that the figures created from the sample data are accurate. 
	NOTE: All equipment and materials used have been provided in the Table of Materials.</li>
</ol>

<ol>
	<li>Wasilczuk, A. Z., Proekt, A., Kelz, M. B., McKinstry-Wu, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-density+electroencephalographic+acquisition+in+a+rodent+model+using+low-cost+and+open+source+resources.">High-density electroencephalographic acquisition in a rodent model using low-cost and open source resources.</a> Journal of Visualized Experiments. 117, 10 (2016).</li><li>Prerau, M. J., Brown, R. E., Bianchi, M. T., Ellenbogen, J. M., Purdon, P. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep+neurophysiological+dynamics+through+the+lens+of+multitaper+spectral+analysis.">Sleep neurophysiological dynamics through the lens of multitaper spectral analysis.</a> Physiology. 32, 60-92 (2017).</li><li>Akin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+wavelet+transform+and+FFT+methods+in+the+analysis+of+EEG+signals.">Comparison of wavelet transform and FFT methods in the analysis of EEG signals.</a> Journal of Medical Systems. 26 (3), 241-247 (2002).</li><li>Purdon, P. L., Sampson, A., Pavone, A., Brown, E. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+electroencephalography+for+anesthesiologists+Part+1:+Background+and+basic+signatures.">Clinical electroencephalography for anesthesiologists Part 1: Background and basic signatures.</a> Anesthesiology. 123 (4), 937-960 (2015).</li><li>Liu, Q., 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=Frontal+EEG+temporal+and+spectral+dynamics+similarity+analysis+between+propofol+and+desflurane+induced+anesthesia+using+Hilbert-Huang+Transform.">Frontal EEG temporal and spectral dynamics similarity analysis between propofol and desflurane induced anesthesia using Hilbert-Huang Transform.</a> BioMed Research International. 2018, 4939480 (2018).</li><li>Akeju, O., 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=Spatiotemporal+dynamics+of+dexmedetomidine-induced+electroencephalogram+oscillations.">Spatiotemporal dynamics of dexmedetomidine-induced electroencephalogram oscillations.</a> PLoS One. 11 (10), (2016).</li><li>Ogilvie, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+process+of+falling+asleep.">The process of falling asleep.</a> Sleep Medicine Reviews. 5 (3), 247-270 (2001).</li><li>Baghdoyan, H. A., Lydic, R., Brady, S. T., Albers, R. W., Price, D. L., Siegel, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Basic Neurochemistry. , 982-999 (2012).</li><li>Angel, C., 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=Buprenorphine+depresses+respiratory+variablity+in+obese+mice+with+altered+leptin+signaling.">Buprenorphine depresses respiratory variablity in obese mice with altered leptin signaling.</a> Anesthesiology. 128 (5), 984-991 (2018).</li><li>Glovak, Z. T., Mihalko, S., Baghdoyan, H. A., Lydic, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Leptin+status+alters+buprenorphine-induced+antinociception+in+obese+mice+with+dysfunctional+leptin+receptors.">Leptin status alters buprenorphine-induced antinociception in obese mice with dysfunctional leptin receptors.</a> Neuroscience Letters. 660, 29-33 (2017).</li><li>Zhang, X., 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=Morphine+and+fentanyl+delivered+to+prefrontal+cortex+of+behaving+mice+depress+breathing+and+alter+neurotransmitter+concentrations.">Morphine and fentanyl delivered to prefrontal cortex of behaving mice depress breathing and alter neurotransmitter concentrations.</a> Anesthesia &amp; Analgesia. , (2019).</li><li>Cornelissen, L., Kim, S. E., Purdon, P. L., Brown, E. N., Berde, C. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-dependent+electroencephalogram+(EEG)+patterns+during+sevoflurane+general+anesthesia+in+infants.">Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants.</a> eLIFE. 4, e06513 (2015).</li><li>Chini, M., 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=Neural+correlates+of+anesthesia+in+newborn+mice+and+humans.">Neural correlates of anesthesia in newborn mice and humans.</a> Front Neural Circuits. 13 (Article 38), 1-13 (2019).</li><li>Flint, R. R., Chang, T., Lydic, R., Baghdoyan, H. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GABA-A+receptors+in+the+pontine+reticular+formation+of+C57BL/6J+mouse+modulate+neurochemical,+electrographic,+and+behavioral+phenotypes+of+wakefulness.">GABA-A receptors in the pontine reticular formation of C57BL/6J mouse modulate neurochemical, electrographic, and behavioral phenotypes of wakefulness.</a> Journal of Neuroscience. 30 (37), 12301-12309 (2010).</li><li>Paxinos, G., Franklin, K. B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Mouse Brain in Stereotaxic Coordinates. , (2018).</li><li>Franken, P., Malafosse, A., Tafti, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+variation+in+EEG+activity+during+sleep+in+inbred+mice.">Genetic variation in EEG activity during sleep in inbred mice.</a> American Journal of Physiology. 257 (4), (1998).</li><li>Ko, C. H., 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=Emergence+of+noise-induced+oscillations+in+the+central+circadian+pacemaker.">Emergence of noise-induced oscillations in the central circadian pacemaker.</a> PLoS Biol. 8 (10), e1000513 (2010).</li><li>Steriade, M., Curr&#243; Dossi, R., Conteras, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrophysiological+properties+of+intralaminar+thalamocortical+cells+discharging+rhythmic+(40+Hz)+spike-bursts+at+1000+Hz+during+waking+and+rapid+eye+movement+sleep.">Electrophysiological properties of intralaminar thalamocortical cells discharging rhythmic (40 Hz) spike-bursts at 1000 Hz during waking and rapid eye movement sleep.</a> Neuroscience. 56, 1-9 (1993).</li></ol>

The authors have no conflicts of interest.

The program described here was developed to create a spectrogram using the nine steps outlined in protocol section 3, Spectrogram Computation. These steps involve acquiring the spectrogram program, ensuring the correct file format, and altering the computational parameters for the generation of unique user spectrograms. Users can create spectrograms that are tailored to a range of conceptual questions and experimental designs. In order to enhance the ease and efficiency of this development process, it is essential to provide the raw EEG data in the correct file format, named according to the constraints described above. Although example signals have been provided for mouse EEG data, the spectrogram program is readily applicable to human and non-human EEG data that are free of signal processing limitations.
The recommended approach for troubleshooting and method modification is to begin by analyzing a small data set. The major program outputs to consider include plots of the filtered EEG as well as the spectrogram. An appealing aspect of the tapered spectrogram is that it can be applied to a wide variety of periodic, biological signals. The variety ranges from long duration circadian (24 h) rhythms17 to very fast rhythms such as 1,000 Hz discharge rates of a Renshaw cell18.
Data formatting is a constraint of this spectrogram protocol. European data format (EDF) is widely used with EEG data. However, there are many other formatting options. For this reason, the raw code file has been included (see 3.2 above) in case the user would like to alter the file format. With regard to the raw program file, another limitation is the need for experience with the computer programming language in order to alter the file format. Not all investigators have access to the proprietary software and the full array of plug-ins. This protocol was developed to circumvent this issue by providing a compiled program that runs on a WINDOWS-based device without software licensing. This is achieved through the RUNTIME plugin which is included in the compiled program and does not require any software registration by the user.
This EEG spectrogram routine is a novel, open source, computer-based program that allows users to create personalized, multitaper spectrograms from a wide range of data. The user has complete control over all computational aspects of spectrogram generation. Without prior signal processing and computer programing knowledge, spectrograms can be difficult to generate. The protocol described here will facilitate spectrogram generation. Please see the supplemental material section for further signal processing readings and multitaper spectrogram guidance.
Supplemental Material 
<a href="http://chronux.org" target="_blank">http://chronux.org</a> 
<a href="http://www-users.med.cornell.edu/~jdvicto/pdfs/pubo08.pdf" target="_blank">http://www-users.med.cornell.edu/~jdvicto/pdfs/pubo08.pdf</a> 
<a href="http://www.fieldtriptoolbox.org/tutorial/timefrequencyanalysis/" target="_blank">http://www.fieldtriptoolbox.org/tutorial/timefrequencyanalysis/</a> 
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4502759/#SD3-data" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4502759/#SD3-data</a>

EEG data can be productively analyzed in the frequency domain to characterize levels of behavioral and neurophysiological arousal1. Multitaper spectrograms transform the EEG waveform into time and frequency domains, resulting in the visualization of the dynamic signal power at different frequencies across time. The multitaper spectrogram uses Fourier analysis to produce spectral density estimations. Spectral density estimation separates a waveform into the pure sinusoidal waves comprising the signal and is analogous to the diffraction of white light through a prism to see the entire spectrum of colors2. The multitaper spectrogram of the EEG represents the combined activity of multiple networks of neurons with discharge patterns that oscillate at different frequencies2. Due to its time shift invariant, the Fourier transform is considered the best transformation between time and frequency domains3. Fourier analysis also has a number of limitations. EEG signals are nonstationary. Therefore, small changes may not be perceived under Fourier methods and the analysis may change depending on the size of the data set. However, windowing is used when applying a Fourier transform to a nonstationary signal. This assumes that the spectrum of the signal changes only marginally over short periods of time. An alternate method for spectral analysis is wavelet transform which may be more appropriate for detecting brain disease3.
From a functional perspective, the different oscillations comprising an EEG signal are lower-level, trait phenotypes characteristic of higher-level, state phenotypes such as sleep and wakefulness2, or the loss of wakefulness caused by general anesthetics4,5,6. Regarding states of sleep and wakefulness, the spectrogram clearly illustrates that endogenously generated rhythms of sleep are continuous and dynamic7. Quantitative descriptions of states of sleep and wakefulness have traditionally involved a binning process that assigns a sleep or wake classification to each specifically defined epoch (e.g., 10 s) of EEG recording. These state bins are then plotted as a function of time. Time course data plots, often referred to as hypnograms, are used to differentiate normal sleep from sleep that is disrupted by disease, drug administration, changes in circadian rhythms, shift work, etc. A limitation of hypnogram plots is that they misrepresent EEG signals by expressing arousal states as square waveforms. Hypnogram plotting involves a discretization of arousal states2 and does not permit a finely grained display of intermediate or transition stages. Furthermore, 10 s scoring epochs produce a discretization of time by imposing a lower limit on the time scale. The result of the discretization of both state and time is the loss of neurophysiological information regarding the dynamic interplay between states of consciousness2 and drug-induced disruption of these states4. For example, different anesthetic agents act on different molecular targets and neural networks. Pharmacological manipulation of these neural networks reliably produces spectrograms unique to the drug, dose, and route of administration4.
The present protocol was developed to facilitate research regarding the mechanisms by which opioids alter sleep8, breathing9, nociception10, and brain neurochemistry11. This protocol describes the steps required to create a multitapered spectrogram for EEG analyses that can be completed using proprietary software or a system that does not have MATLAB licensing. C57BL/6J (B6) mice were used to validate the ability of this computer-based method to create novel EEG spectrograms during normal, undisturbed states of sleep and wakefulness and after systemic administration of opiates. The reliability and validity of the analyses were confirmed by systematic comparisons of differences between EEG spectrograms after B6 mice received intraperitoneal injections of saline (vehicle control) and antinociceptive doses of morphine, buprenorphine, and fentanyl.
Quantitative studies of neonatal mouse EEG dynamics have translational relevance by providing a model for studies aiming to achieve a better understanding of neonatal human EEG12. Quantifying EEG dynamics is not merely descriptive and can contribute to machine learning approaches that can predict arousal based in part on EEG data13. The goal of the present report is to promote translational science by providing a widely accessible, user friendly code for computing multitaper spectrograms that characterize drug-induced changes in mouse EEG.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Dental acrylic</td>
			<td>Lang Dental Manufacturing Co</td>
			<td>Jet powder and liquid</td>
			<td></td>
		</tr>
		<tr>
			<td>EEG/EMG Amplifier</td>
			<td>Data Science International</td>
			<td>model MX2</td>
			<td></td>
		</tr>
		<tr>
			<td>macOS Mojave</td>
			<td>Apple</td>
			<td>v10.14.4</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>Mathworks</td>
			<td>v9.4.0.813654</td>
			<td>software for spectrogram comp.</td>
		</tr>
		<tr>
			<td>Mouse anesthesia mask</td>
			<td>David Kopf Instruments</td>
			<td>model 907</td>
			<td></td>
		</tr>
		<tr>
			<td>Neuroscore</td>
			<td>Data Science International</td>
			<td>v3.3.9317-1</td>
			<td>software for scoring sleep and wakefulness</td>
		</tr>
		<tr>
			<td>Ponemah</td>
			<td>Data Science International</td>
			<td>v5.32</td>
			<td>software for EEG/EMG Data Acquisition</td>
		</tr>
		<tr>
			<td>Stereotaxic frame</td>
			<td>David Kopf Instruments</td>
			<td>model 962</td>
			<td></td>
		</tr>
		<tr>
			<td>Stereotaxic frame, mouse adapter</td>
			<td>David Kopf Instruments</td>
			<td>model 921</td>
			<td></td>
		</tr>
		<tr>
			<td>Windows 10</td>
			<td>Microsoft</td>
			<td>v10.0.17763.503</td>
			<td></td>
		</tr>
		<tr>
			<td>Wireless Telemeter</td>
			<td>Data Science International</td>
			<td>model HD-X02</td>
			<td></td>
		</tr>
	</tbody>
</table>

computer-based multitaper spectrogram program for electroencephalographic data

Current web resources provide limited, user friendly tools to compute spectrograms for visualizing and quantifying electroencephalographic (EEG) data. This paper describes a Windows-based, open source code for creating EEG multitaper spectrograms. The compiled program is accessible to Windows users without software licensing. For Macintosh users, the program is limited to those with a MATLAB software license. The program is illustrated via EEG spectrograms that vary as a function of states of sleep and wakefulness, and opiate-induced alterations in those states. The EEGs of C57BL/6J mice were wirelessly recorded for 4 h after intraperitoneal injection of saline (vehicle control) and antinociceptive doses of morphine, buprenorphine, and fentanyl. Spectrograms showed that buprenorphine and morphine caused similar changes in EEG power at 1&#8722;3 Hz and 8&#8722;9 Hz. Spectrograms after administration of fentanyl revealed maximal average power bands at 3 Hz and 7 Hz. The spectrograms unmasked differential opiate effects on EEG frequency and power. These computer-based methods are generalizable across drug classes and can be readily modified to quantify and display a wide range of rhythmic biological signals.

The following figures illustrate the type of novel insights into EEG indices of brain excitability that are provided by spectrograms.
Figure 1A illustrates similarities and differences in the cortical EEG during wakefulness, NREM sleep, and REM sleep. Many investigators use these kinds of traces, along with EMG recordings (not shown), to quantify sleep and wakefulness. Figure 1B uses a hypnogram plot to convey the temporal organization of states of sleep and wakefulness based on assessments of EEG and EMG recordings. States were scored in 10 s epochs and these epochs were plotted as the hypnogram during the 14,400 s comprising the 4 h recording. Hypnogram plots do not illustrate the fact that transitions between states are continuous and nonlinear. In contrast to a hypnogram plot, the spectrogram (Figure 1C) illustrates highly dynamic changes in EEG frequency and power as a function of time. The spectrogram also highlights the similarities between the cortical EEG signal during wakefulness and during REM sleep. The three boxes superimposed on the spectrogram (Figure 1C) mark states identified as wakefulness (WAKE), NREM sleep, and REM sleep in the hypnogram above (Figure 1B) and are provided to assist with visualizing the detailed changes in EEG frequency and power. The spectrogram for the entire recording provides a nuanced appreciation of the EEG as a continuous process.
Figure 2 provides four multitaper spectrograms, each summarizing 4 h of EEG recordings after intraperitoneal administration of saline, morphine, buprenorphine, and fentanyl. All four recordings are from the same mouse and were begun 2 h after light onset. The opiates, but not saline, inhibited NREM and REM sleep, and increased the amount of wakefulness. A number of novel features are visualized by the spectrograms. The detection of novel EEG features suggests the potential application for opiate differentiation in a chemical threat environment. After the saline injection (Figure 2A) the greatest amount of power resided in the 2&#8722;4 Hz range, indicating NREM sleep. Note that the EEG spectrograms were fundamentally altered by opiate administration, and that each opiate caused unique spectral changes.
Figure 3 demonstrates that EEG changes illustrated by the spectrograms can be quantified and expressed as average dominate spectral power of each half frequency (Figure 3A) and as the average spectral power within specific EEG frequency bands (Figure 3B). The greatest differences from saline were caused by buprenorphine and occurred in the delta and theta ranges.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60333/60333fig01.jpg" /> 
Figure 1: Cortical EEG recordings used to create hypnograms and spectrograms. (A) EEG waveforms recorded during wakefulness, NREM sleep, and REM sleep during a baseline (no injection) recording. Each trace shows 90 s of recording. (B) The hypnogram uses the height of the bars to convey state of consciousness (ordinate) versus 4 h of recording (abscissa). (C) Tapered spectrogram using a color bar to convey EEG power in decibels (dB, right ordinate) or spectral power density at different EEG frequencies in Hertz (Hz, left ordinate) as a function of 4 h recording time (abscissa). Black vertical lines have been added to the spectrogram to delineate one episode each of wakefulness, NREM sleep, and REM sleep. (Spectrogram parameters: sampling frequency = 500 Hz, fpass = 0.3 Hz and 30 Hz, padding = 2, time-bandwidth = 15, number of tapers 29, trial average = 1, duration of time to compute FFT ~30 s, step size of window for FFT computation = 5). <a href="https://www.jove.com/files/ftp_upload/60333/60333fig1large.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/60333/60333fig02.jpg" /> 
Figure 2: Spectrograms illustrating changes in EEG power and frequency caused by opiate administration. Each spectrogram plots EEG frequency in Hertz (Hz, left ordinate) and decibels (dB) of EEG power using a color bar (right ordinate) for 4 h (abscissa) after administration of (A) saline, (B) morphine, (C) buprenorphine, and (D) fentanyl. (Spectrogram parameters: sampling frequency = 500 Hz, fpass = 0.3 Hz and 30 Hz, padding = 2, time-bandwidth = 15, number of tapers 29, trial average = 1, time to compute FFT ~30 s, step size of window for FFT computation = 5). <a href="https://www.jove.com/files/ftp_upload/60333/60333fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60333/60333fig03.jpg" /> 
Figure 3: Opiates differentially altered the average EEG power within the delta and theta EEG frequency bands. (A) Summarizes the average EEG power during each 4 h recording shown in Figure 2. Ordinate plots average EEG power at each half frequency (abscissa). Relative to the saline control, each of the other three functions show opiate-specific alterations in average EEG power. (B) Illustrates the average EEG power in four EEG frequency bands (delta, theta, alpha, and beta) after administration of saline (S), buprenorphine (B), morphine (M), and fentanyl (F). Color coding is the same for power functions in A and average power bands in B. <a href="https://www.jove.com/files/ftp_upload/60333/60333fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Computer-based Multitaper Spectrogram Program for Electroencephalographic Data at JoVE.com

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

This protocol provides an open source, compiled MATLAB program that generates multitaper spectrograms for electroencephalographic data.

Current web resources provide limited, user friendly tools to compute spectrograms for visualizing and quantifying electroencephalographic (EEG) data. ...

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JoVE Journal

Neuroscience

11.9K Views.  University of Tennessee. This protocol provides an open source, compiled MATLAB program that generates multitaper spectrograms for electroencephalographic data.

Video: Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources

Advanced electroencephalographic analysis techniques requiring high spatial resolution, including electrical source imaging and measures of network connectivity, are applicable to an expanding variety of questions in neuroscience. Performing these kinds of analyses in a rodent model requires higher electrode density than traditional screw electrodes can accomplish. While higher-density electroencephalographic montages for rodents exist, they are of limited availability to most researchers, are not robust enough for repeated experiments over an extended period of time, or are limited to use in anesthetized rodents.1-3 A proposed low-cost method for constructing a durable, high-count, transcranial electrode array, consisting of bilaterally implantable headpieces is investigated as a means to perform advanced electroencephalogram analyses in mice or rats.

Procedures for headpiece fabrication and surgical implantation necessary to produce high signal to noise, low-impedance electroencephalographic and electromyographic signals are presented. While the methodology is useful in both rats and mice, this manuscript focuses on the more challenging implementation for the smaller mouse skull. Freely moving mice are only tethered to cables via a common adapter during recording. One version of this electrode system that includes 26 electroencephalographic channels and 4 electromyographic channels is described below.

Instructions for the low-cost construction and surgical implantation of a chronic transcranial high-density electroencephalographic montage into mice are provided. Signal recording, extraction, and processing techniques are also described.

Advanced electroencephalographic analysis techniques requiring high spatial resolution, including electrical source imaging and measures of network ...

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at the synaptic level. The Active Zone (AZ) is the region at the presynaptic membrane that mediates neurotransmitter release and is composed of a dense collection of scaffold proteins. AZs of Drosophila melanogaster (Drosophila) photoreceptors undergo molecular remodeling after prolonged exposure to natural ambient light. Thus the level of neuronal activity can rearrange the molecular composition of the AZ and contribute to the regulation of the functional output.
Starting from the light exposure set-up preparation to the immunohistochemistry, this protocol details how to quantify the number, the spatial distribution, and the delocalization level of synaptic molecules at AZs in Drosophila photoreceptors. Using image analysis software, clusters of the GFP-fused AZ component Bruchpilot were identified for each R8 photoreceptor (R8) axon terminal. Detected Bruchpilot spots were automatically assigned to individual R8 axons. To calculate the distribution of spot frequency along the axon, we implemented a customized software plugin. Each axon&#39;s start-point and end-point were manually defined and the position of each Bruchpilot spot was projected onto the connecting line between start and end-point. Besides the number of Bruchpilot clusters, we also quantified the delocalization level of Bruchpilot-GFP within the clusters. These measurements reflect in detail the spatially resolved synaptic dynamics in a single neuron under different environmental conditions to stimuli.

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

Here we show how to quantify the number and spatial distribution of synaptic active zones in Drosophila melanogaster&#160;photoreceptors, highlighted with a genetically encoded molecular marker, and their modulation after prolonged exposure to light.

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at ...

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Central dopaminergic (DAergic) pathways have an important role in a wide range of functions, such as attention, motivation, and movement. Dopamine (DA) is implicated in diseases and disorders including attention deficit hyperactivity disorder, Parkinson's disease, and traumatic brain injury. Thus, DA neurotransmission and the methods to study it are of intense scientific interest. In vivo fast-scan cyclic voltammetry (FSCV) is a method that allows for selectively monitoring DA concentration changes with fine temporal and spatial resolution. This technique is commonly used in conjunction with electrical stimulations of ascending DAergic pathways to control the impulse flow of dopamine neurotransmission. Although the stimulated DA neurotransmission paradigm can produce robust DA responses with clear morphologies, making them amenable for kinetic analysis, there is still much debate on how to interpret the responses in terms of their DA release and clearance components. To address this concern, a quantitative neurobiological (QN) framework of stimulated DA neurotransmission was recently developed to realistically model the dynamics of DA release and reuptake over the course of a stimulated DA response. The foundations of this model are based on experimental data from stimulated DA neurotransmission and on principles of neurotransmission adopted from various lines of research. The QN model implements 12 parameters related to stimulated DA release and reuptake dynamics to model DA responses. This work describes how to simulate DA responses using QNsim1.0 and also details principles that have been implemented to systematically discern alterations in the stimulated dopamine release and reuptake dynamics.

Fast-scan cyclic voltammetry can monitor in vivo dopamine neurotransmission in the context of drugs, disease, and other experimental manipulations. This work describes the implementation of QNsim1.0, a software to model electrically stimulated dopamine responses according to the quantitative neurobiological model to quantify estimates of dopamine release and reuptake dynamics.

Central dopaminergic (DAergic) pathways have an important role in a wide range of functions, such as attention, motivation, and movement. Dopamine (DA) is ...

Electroencephalographic, Heart Rate, and Galvanic Skin Response Assessment for an Advertising Perception Study: Application to Antismoking Public Service Announcements

The evaluation of advertising, products, and packaging is traditionally performed through methods based on self-reports and focus groups, but these approaches often appear poorly accurate in scientific terms. Neuroscience is increasingly applied to the investigation of the neurophysiological bases of the perception of and reaction to commercial stimuli to support traditional marketing methods. In this context, a particular sector or marketing is represented by public service announcements (PSAs). The objective of this protocol is to apply electroencephalography (EEG) and autonomic signal analysis to study responses to selected antismoking PSAs. Two EEG indices were employed: the frontal alpha band EEG asymmetry (the Approach Withdrawal (AW) index) and the frontal theta (effort index). Furthermore, the autonomic Emotional Index (EI) was calculated, as derived from the Galvanic Skin Response (GSR) and Heart Rate (HR) signals. The present protocol describes a series of operational and computational steps required to properly estimate, through the aforementioned indices, the emotional and cerebral reaction of a group of subjects towards a selected number of antismoking PSAs. In particular, a campaign characterized by a symbolic communication style (classified as "awarded" on the basis of the prizes received by specialized committees) obtained the highest approach values, as estimated by the AW index. A spot and an image belonging to the same PSA campaign based on the "fear arousing appeal" and with a narrative/experiential communication style (classified as "effective" on the basis of the economical/health-related improvements promoted) reported the lowest and highest effort values, respectively. This is probably due to the complexity of the storytelling (spot) and to the immediateness of the image (a lady who underwent a tracheotomy). Finally, the same "effective" campaign showed the highest EI values, possibly because of the empathy induced by the testimonial and the explicitness of the message.

The following protocol describes a series of operational and computational steps required to properly estimate the emotional and cerebral reaction of a group of subjects towards a selected number of Public Service Announcements (PSAs) against smoking, aired in the USA and Europe during the period between 1998 and 2015.

The evaluation of advertising, products, and packaging is traditionally performed through methods based on self-reports and focus groups, but these ...

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 ...

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

We describe a rapid tissue donation program for individuals with multiple sclerosis (MS) that requires scientists and technicians to be on-call 24/7, 365 days a year. Participants consent to donate their brain and spinal cord. Most patients were followed by neurologists at the Cleveland Clinic Mellen Center for MS Treatment and Research. Their clinical courses and neurological disabilities are well-characterized. Soon after death, the body is transported to the MS Imaging Center, where the brain is scanned in situ by 3 T magnetic resonance imaging (MRI). The body is then transferred to the autopsy room, where the brain and spinal cord are removed. The brain is divided into two hemispheres. One hemisphere is immediately placed in a slicing box and alternate 1 cm-thick slices are either fixed in 4% paraformaldehyde for two days or rapidly frozen in dry ice and 2-methylbutane. The short-fixed brain slices are stored in a cryopreservation solution and used for histological analyses and immunocytochemical detection of sensitive antigens. Frozen slices are stored at -80 &#176;C and used for molecular, immunocytochemical, and in situ hybridization/RNA scope studies. The other hemisphere is placed in 4% paraformaldehyde for several months, placed in the slicing box, re-scanned in the 3 T magnetic resonance (MR) scanner and sliced into centimeter-thick slices. Postmortem in situ MR images (MRIs) are co-registered with 1 cm-thick brain slices to facilitate MRI-pathology correlations. All brain slices are photographed and brain white-matter lesions are identified. The spinal cord is cut into 2 cm segments. Alternate segments are fixed in 4% paraformaldehyde or rapidly frozen. The rapid procurement of postmortem MS tissues allows pathological and molecular analyses of MS brains and spinal cords and pathological correlations of brain MRI abnormalities. The quality of these rapidly-processed postmortem tissues (usually within 6 h of death) is of great value to MS research and has resulted in many high-impact discoveries.

Multiple sclerosis is an inflammatory demyelinating disease with no cure. Analysis of brain tissue provides important clues to understanding the pathogenesis of the disease. Here we discuss the methodology and downstream analysis of MS brain tissue collected through a unique rapid autopsy program in operation at the Cleveland Clinic.

We describe a rapid tissue donation program for individuals with multiple sclerosis (MS) that requires scientists and technicians to be on-call 24/7, 365 ...

Analyses of Actin Dynamics, Clutch Coupling and Traction Force for Growth Cone Advance

To establish functional networks, neurons must migrate to their appropriate destinations and then extend axons toward their target cells. These processes depend on the advances of growth cones that located at the tips of neurites. Axonal growth cones generate driving forces by sensing their local microenvironment and modulating cytoskeletal dynamics and actin-adhesion coupling (clutch coupling). Decades of research have led to the identification of guidance molecules, their receptors, and downstream signaling cascades for regulating neuronal migration and axonal guidance; however, the molecular machineries required for generating forces to drive growth cone advance and navigation are just beginning to be elucidated. At the leading edge of neuronal growth cones, actin filaments undergo retrograde flow, which is powered by actin polymerization and actomyosin contraction. A clutch coupling between F-actin retrograde flow and adhesive substrate generates traction forces for growth cone advance. The present study describes a detailed protocol for monitoring F-actin retrograde flow by single speckle imaging. Importantly, when combined with an F-actin marker Lifeact, this technique can quantify 1) the F-actin polymerization rate and 2) the clutch coupling efficiency between F-actin retrograde flow and the adhesive substrate. Both are critical variables for generating forces for growth cone advance and navigation. In addition, the present study describes a detailed protocol of traction force microscopy, which can quantify 3) traction force generated by growth cones. Thus, by coupling the analyses of&#160;single speckle imaging and traction force microscopy, investigators can monitor the molecular mechanics underlying growth cone advance and navigation.

To advance, growth cones must exert traction forces against the external environment. The generation of traction forces is dependent on actin dynamics and clutch coupling. The present study describes methods for analyzing actin dynamics, clutch coupling and traction forces for growth cone advance.

To establish functional networks, neurons must migrate to their appropriate destinations and then extend axons toward their target cells. These processes ...

Stimulating Mouse Brain with a Low-Cost EEG and Millimeter-Sized Coil

Low-Cost Electroencephalographic Recording System Combined with a Millimeter-Sized Coil to Transcranially Stimulate the Mouse Brain In Vivo

A low-cost electroencephalographic (EEG) recording system is proposed here to drive transcranial magnetic stimulation (TMS) of the mouse brain in vivo, utilizing a millimeter-sized coil. Using conventional screw electrodes combined with a custom-made, flexible, multielectrode array substrate, multi-site recording can be carried out from the mouse brain. In addition, we explain how a millimeter-sized coil is produced using low-cost equipment usually found in laboratories. Practical procedures for fabricating the flexible multielectrode array substrate and the surgical implantation technique for screw electrodes are also presented, which are necessary to produce low-noise EEG signals. Although the methodology is useful for recording from the brain of any small animal, the present report focuses on electrode implementation in an anesthetized mouse skull. Furthermore, this method can be easily extended to an awake small animal that is connected with tethered cables via a common adapter and fixed with a TMS device to the head during recording.The present version of the EEG-TMS system, which can include a maximum of 32 EEG channels (a device with 16 channels is presented as an example with fewer channels) and one TMS channel device, is described. Additionally, typical results obtained by the application of the EEG-TMS system to anesthetized mice are briefly reported.

Author Spotlight: Low-Cost Electroencephalographic Recording System Combined with a Millimeter-Sized Coil to Transcranially Stimulate the Mouse Brain In Vivo  

A low-cost electroencephalographic recording system combined with a millimeter-sized coil is proposed to drive transcranial magnetic stimulation of the mouse brain in vivo. Using conventional screw electrodes with a custom-made, flexible, multielectrode array substrate, multi-site recording can be carried out from the mouse brain in response to transcranial magnetic stimulation.

A low-cost electroencephalographic (EEG) recording system is proposed here to drive transcranial magnetic stimulation (TMS) of the mouse brain in vivo, ...

Mouse Brain Histone Modification Quantification Using Flow Cytometry

Quantification of Global Histone Post Translational Modifications Using Intranuclear Flow Cytometry in Isolated Mouse Brain Microglia

Gene expression control occurs partially by modifications in chromatin structure, including the addition and removal of posttranslational modifications to histone tails. Histone post-translational modifications (HPTMs) can either facilitate gene expression or repression. For example, acetylation of histone tail lysine residues neutralizes the positive charge and reduces interactions between the tail and negatively charged DNA. The decrease in histone tail-DNA interactions results in increased accessibility of the underlying DNA, allowing for increased transcription factor access. The acetylation mark also serves as a recognition site for bromodomain-containing transcriptional activators, together resulting in enhanced gene expression. Histone marks can be dynamically regulated during cell differentiation and in response to different cellular environments and stimuli. While next-generation sequencing approaches have begun to characterize genomic locations for individual histone modifications, only one modification can be examined concurrently. Given that there are hundreds of different HPTMs, we have developed a high throughput, quantitative measure of global HPTMs that can be used to screen histone modifications prior to conducting more extensive genome sequencing approaches. This protocol describes a flow cytometry-based method to detect global HPTMs and can be conducted using cells in culture or isolated cells from in vivo tissues. We present example data from isolated mouse brain microglia to demonstrate the sensitivity of the assay to detect global shifts in HPTMs in response to a bacteria-derived immune stimulus (lipopolysaccharide). This protocol allows for the rapid and quantitative assessment of HPTMs and can be applied to any transcriptional or epigenetic regulator that can be detected by an antibody.

Author Spotlight: Enhancements in Gene Expression Regulation Research

This work describes a protocol for the quantification of global histone modifications using intranuclear flow cytometry in isolated brain microglia. The work also contains the microglia isolation protocol that was used for data collection.

Gene expression control occurs partially by modifications in chromatin structure, including the addition and removal of posttranslational modifications to ...