We thank Kerena Yan and Jingwen Hu for manually scoring sleep stages and Eunah and Soonwiik for the recordings.

This study used data collected from in vivo experiments in mice. No human experiments were involved in the study. All the experiments with animals were approved by the Institutional Animal Care and Use Committee at the Broad Institute. All experiments were performed in accordance with relevant guidelines and regulations. The ARRIVE guidelines are not applicable to this study because the focus of this study is to develop machine learning models rather than comparing different treatment groups.
1. Data preparation
NOTE: Data compatibility: the recorded data can have any sampling rate higher than 40 Hz. There is no need to bandpass filter the signal because the software bandpass filters the EEG and EMG signals at the first step. The LightGBM models were developed and tested using data from mice. No evidence regarding the performance of the LightGBM models in other types of lab animals is available. The recording electrodes need to be placed at the frontal and the parietal cortex, or either place if only one EEG channel is recorded.
<ol>
	<li>EDF/EDF+ format arrangement and requirement 
	NOTE: The software used in this study only reads EDF/EDF+ files using the MNE-Python package. The standard EDF/EDF+ specification needs to be applied to generate the EDF/EDF+ files. In addition to the standard specification, ensure that the EDF/EDF+ annotations are encoded in UTF-8. Otherwise, the software application will crash.
	<ol>
		<li>Convert another file (non-EDF/EDF+ file) format into EDF/EDF+ format with online free tools. 
		NOTE: There is no requirement for the apparatus filter when obtaining EEG and EMG signals. As long as users sample their EEG and EMG data at a frequency of 40 Hz or higher, the software will function correctly. This is because, in the initial preprocessing step, the signals undergo bandpass filtering between 1 Hz and 40 Hz. This bandpass filtering is integrated into the software&#39;s preprocessing pipeline, eliminating users needing to perform any additional signal processing.</li>
	</ol>
	</li>
	<li>There are two models inside the software for scoring. One is LightGBM-2EEG, and the other is LightGBM-1EEG. The LightGBM-2EEG model is designated for recording data that has 2 EEG channels and 1 EMG channel. Perform the following steps depending on the model.
	<ol>
		<li>Organize data files for LightGBM-2EEG specifically in the following order: 1) EEG channel recorded in the parietal area; 2) EEG channel recorded in the frontal area; 3) EMG channel. The LightGBM-1EEG is designated for data that only contains 1 EEG channel (electrode placement on either parietal or frontal area) and 1 EMG channel.</li>
		<li>Organize the channels in EDF/EDF+ files for LightGBM-1EEG in the following order: 1) EEG channel; 2) EMG channel.</li>
	</ol>
	</li>
</ol>
2. Downloading IntelliSleepScorer for Windows, Mac, and Linux users
<ol>
	<li>For Windows users, a Windows Executable for the software is available using PyInstaller. Find the download link on the Pan group research page&#160;https://sites.broadinstitute.org/pan-lab/resources. For MacOS or Linux users, use the source code on the GitHub repository https://github.com/broadinstitute/IntelliSleepScorer to launch the software.&#160;</li>
	<li>Access two recorded example data to test the program saved as EDF files by downloading them through the GitHub repository.</li>
	<li>The source code repository does not include the models folder due to size limits. Instead, download models.zip, unzip it, and copy the models folder inside the repository for the program to run. Otherwise, the software will crash due to missing model files.</li>
</ol>
3. Workflow and Program launch and operation
<ol>
	<li>Launch IntelliSleepScorer
	<ol>
		<li>To launch the software in Windows, double-click IntelliSleepScorer.exe located in the root folder. To launch the software in MacOS or Linux, open a terminal emulator, change the directory to the root folder of the software, and then launch the software using the command: python3 IntelliSleepScorer.py.</li>
	</ol>
	</li>
	<li>Once the software opens, click Select EDF/EDF+ File(s) to select the intended file(s) to score. If files were selected by mistake, click the Clear button to clear the selected file list. 
	NOTE: By default, the software encodes the sleep stages as Wake:1, NREM:2, and REM:3 in the output score files. The default epoch length is set at 10 s. The current version (v1.2) of the GUI allows users to change stage encodings or epoch lengths to 4 s, 10 s, or 20 s with the dropdown menu.</li>
	<li>Select the desired epoch length. Use the provided dropdown menu to select the intended epoch length among options of 4 s, 10 s, and 20 s for sleep stage scoring.</li>
	<li>Select the model that is to be used for sleep scoring. LightGBM-2EEG is intended for data files with two EEG channels and one EMG channel, while the LightGBM-1EEG is designed for data with one EEG channel and one EMG channel.</li>
	<li>Before running the sleep stage prediction, include the additional SHAP calculation that helps explain the sleep stage prediction results. To process the SHAP calculation, check the Run/Plot SHAP checkbox. The SHAP calculation requires around 5-10 min to process.</li>
	<li>Click Score All Files. The model automatically scores all the EDF/EDF+ files and calculates the global and epoch SHAP values to interpret the scoring decisions in the list if that is chosen to do so. 
	NOTE: During the scoring process, the model generates the following files and saves them to the same folder where the EDF/EDF+ files are located. The model uses these files to plot the global SHAP values and epoch SHAP values. 
	 
	&#34;EDF/EDF+ file name}_{model_name}_features.csv&#34;; this file stores all the extracted feature values. 
	&#34;EDF/EDF+ file name}_{model_name}_scores.csv&#34;; this file stores the predicted sleep stages. 
	&#34;EDF/EDF+ file name}_{model_name}_rs_100hz.npy&#34;; this file stores a copy of the resampled/downsampled signals (100hz). To improve the visualization speed, the model uses the downsampled signal instead of the original signal when plotting the signal. 
	&#34;&#8203;EDF/EDF+ file name}_{model_name}explainer. pickle&#34;; &#34;{EDF/EDF+ file name}{model_name}shap_500samples.pickle&#34;; &#34;{EDF/EDF+ file name}{model_name}_indicies_500samples.npy&#34;;</li>
	<li>After finishing the sleep scoring process, click on the Visualize the Selected File option to visualize the EEG/EMG signals and a hypnogram time-aligned with the signals.
	<ol>
		<li>Score the selected file again before visualization if the epoch length is changed.</li>
	</ol>
	</li>
</ol>
4. Navigating the scored results
<ol>
	<li>Click the provided Navigation buttons to move forward and backward to see different epoch data.</li>
	<li>If SHAP calculation is chosen to be performed, view both the global and epoch-level SHAP values. Right-click on an epoch to plot the epoch-level SHAP values. 
	NOTE: It will take a few seconds to update the epoch-level SHAP plot. Figure 1 shows the GUI page overview after running the prediction for Example-1 EDF/EDF+ file with 1_LightBGM-2EEG model.</li>
</ol>
5. Interpretation of the scored sleep stages hypnogram
NOTE: There are 4 rows in the hypnogram (Figure 2). The top row is the predicted results. The bottom 3 rows are raw data of 2 EEG and 1 EMG channels, respectively. On the top row, orange suggests the Wake stage, blue suggests the NREM stage, and red suggests the REM stage in each epoch.
<ol>
	<li>To change the number of the epochs to display, click on the menu box on the right of Select Number of Epochs to Display and choose a desired value. In Figure 2, 100 epochs were chosen. Therefore, only 100 epochs are displayed in the sleep stage prediction plot. Select a smaller number in the dropdown menu to zoom into the plot.</li>
	<li>The pink transparent bar on the left of Figure 2 indicates the current location of the epoch. Left-click on any place on the hypnogram to switch to another epoch, or simply click on Go to Epoch and enter a specific number of epochs to be observed. Right-click the selected epoch to generate its epoch SHAP plot if the user has enabled the SHAP function.</li>
</ol>
6. Manual correction of the predicted sleep stages on GUI (Optional)
NOTE: if no anomaly is observed or extremely high accuracy is not required for REM stage prediction, manual verification is not needed.
<ol>
	<li>Left-click on an epoch in the sleep stage prediction plot (top plot) to select a specific epoch. The model predicted Stage of Selected Epoch is shown on the right of the text. To manually change the predicted stage on that epoch, click on the widget and select a new stage from the Wake, NREM, and REM options in the dropdown menu.</li>
	<li>The user-corrected stages are marked with dashed lines on top of the original plot (Figure 3). Close the GUI, and a new file with corrected prediction results will be automatically generated in the same folder.
	<ol>
		<li>To open a saved scored file on the GUI again, ensure that the epoch length setting and the selected model match the one used when the EDF file was initially processed to be able to reopen it. All the previously modified/scored information will be quickly loaded.</li>
	</ol>
	</li>
</ol>

Automated Sleep Stage Scoring Tool for Rodents Using LightGBM

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J., Brown, R. E., Basheer, R., Uygun, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep-deep-learner+is+taught+sleep-wake+scoring+by+the+end-user+to+complete+each+record+in+their+style.">Sleep-deep-learner is taught sleep-wake scoring by the end-user to complete each record in their style.</a> Sleep Adv. 5 (1), zpae022 (2024).</li><li>Allocca, G., 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=Validation+of+'somnivore',+a+machine+learning+algorithm+for+automated+scoring+and+analysis+of+polysomnography+data.">Validation of 'somnivore', a machine learning algorithm for automated scoring and analysis of polysomnography data.</a> Front Neurosci. 13, 207 (2019).</li><li>Jha, P. K., Valekunja, U. K., Reddy, A. 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The authors declare no conflict of interest.

This paper presents how to use the IntelliSleepScorer (v1.2) graphic user interface to automatically score the sleep stages of mice and how to leverage SHAP values/plots to better understand the sleep stage scores generated by the model.
An important consideration when using the software is data compatibility. The in-house data used in this study was limited to electrodes placed in the frontal and parietal regions. In the independent dataset from Miladinovic and colleagues11<...

Sleep stage scoring in rodents is the procedure to identify the three stages: non-rapid eye movement sleep (NREM), rapid eye movement sleep (REM), and wake2. In rodents, NREM is characterized by reduced muscle activity, slow and regular breathing, decreased heart rate, and low-frequency oscillations of the brain waves. REM in rodents, similar to humans, shows muscle atonia, EEG activation, and rapid eye movements, although the occurrence of vivid dreaming is less clear in rodents compared to humans2,3. The &#34;wake&#34; state in rodents is marked by desynchronized brain activity with high-frequency, low-amplitude waves, increased muscle tone, and active behavior, such as grooming and exploration4. These three stages can be identified by inspecting electroencephalogram (EEG) and electromyogram (EMG) signals5.
The automatic sleep stage scoring models in rodents are in great need. First, manual sleep stage scoring by human experts is labor-intensive and time-consuming. Secondly, sleep patterns in rodents differ from those in humans and have more fragmented episodes of NREM and REM interspaced by waking, around 10 min, in contrast to 60-120 min in humans6. Therefore, identifying these brief periods during manual scoring is challenging. There have been many attempts since the 60s to develop an automatic scoring system of rodent sleep data7. Although many automated rodent sleep scoring methods exist, their performances vary8,9,10,11,12,13,14,15,16,17,18. Importantly, most high-performing models with high generalizability are not publicly available (some need special requests from developers) or are not cost-free for sleep researchers.
Therefore, to fill the current technology gap, we developed a machine learning-based model using a large dataset of 5776 h of EEG and EMG signals from 519 recordings across 124 mice with the LightGBM algorithm1. The lightGBM uses a gradient-boosting approach to construct decision trees19. In Wang et al., 2023, the LightGBM model (consisting of over 8000 decision trees) achieved an overall accuracy of 95.2% and a Cohen&#39;s kappa of 0.91, which outperformed two widely used baseline models such as the logistic regression model (accuracy = 93.3%) and the random forest model (accuracy = 94.3%, kappa = 0.89). The overall performance of the model also displayed a similar performance to that of human experts. Most importantly, the model has been proved to have generalizability and not overfitted to the original training data1: 1) It performed well (accuracy &#62; 89%) on two other publicly available independent datasets, from Miladinovic and colleagues11, with different sampling frequencies and epoch lengths; 2) The performance of the model is not impacted by the light/dark cycle of mice; 3) A modified LightGBM model performed well on data containing only one EEG and one EMG electrode with kappa &#8805; 0.89; 4) Both wildtype and mutant mice were used for the testing and the performances of the model were both accurate. This suggests the model can score sleep stages for mice with different genetic backgrounds.
In order to make this model accessible to sleep researchers who may not have coding expertise, we developed IntelliSleepScorer, a user-friendly software tool with a visually intuitive interface. The software can fully automate the sleep-scoring procedure in mice. It produces interactive visualizations of the signals, hypnogram, and Shapley Additive exPlanations (SHAP) values from an European data format (EDF)/EDF+ file input. The SHAP value approach, based on cooperative game theory, enhances the interpretability of machine learning models20. The model offers both global and epoch-level SHAP values, revealing how different feature values contribute to the scoring decision of the model overall and for each epoch. This advanced program significantly reduces the time and effort required for sleep stage scoring in mice while ensuring that downstream analysis can rely on highly accurate results. In this manuscript, we present step-by-step usage of IntelliSleepScorer (v1.2) with several updates upon version 1.0, including an option to run SHAP analysis separately from sleep pattern prediction, an user adjustable epoch length for sleep stage scoring, and a sleep stage manual correction feature integrated within the GUI.

<table><tbody><tr><td>Canonical Unbuntu 18.04</td><td>Canonical</td><td>https://releases.ubuntu.com/18.04/</td><td>Supporting Operating System for the software IntelliSleep Scorer: Windows, Mac, or Linux</td></tr><tr><td>Intel Core i7-8550U CPU @ 1.80 GHz 1.99 GHz; RAM: 24 GB&amp;nbsp;</td><td>Intel Corp</td><td>https://www.intel.com/content/www/us/en/products/details/processors/core-ultra.html</td><td>Hardware requirment for the software: Both Inte Core listed here have been used to process the data. It takes around 10 min to process 12 h of recording sampled at 1000 Hz for both hardwares. Any similar or superior hardware would yield comparable or better performance.&amp;nbsp;&amp;nbsp;</td></tr><tr><td>Intel Core i7-10610U CPU @1.80 GHz 2.30 GHz; RAM: 16 GB</td><td>Intel Corp</td><td>https://www.intel.com/content/www/us/en/products/details/processors/core-ultra.html</td><td>Hardware requirment for the software: Both Inte Core listed here have been used to process the data. It takes around 10 min to process 12 h of recording sampled at 1000 Hz for both hardwares. Any similar or superior hardware would yield comparable or better performance.&amp;nbsp;&amp;nbsp;</td></tr><tr><td>LightGBM</td><td>Microsoft</td><td>https://lightgbm.readthedocs.io/en/latest/index.html</td><td>Machine learning-based algorithm that was used to train the software.&amp;nbsp;</td></tr><tr><td>MacBook Pro</td><td>Apple</td><td>https://www.apple.com/in/macbook-pro/</td><td>Supporting Operating System for the software IntelliSleep Scorer: Windows, Mac, or Linux</td></tr><tr><td>Windows</td><td>Microsoft</td><td>https://www.microsoft.com/en-in/windows/?r=1</td><td>Supporting Operating System for the software IntelliSleep Scorer: Windows, Mac, or Linux</td></tr></tbody></table>

intellisleepscorer, a software package with a graphic user interface for mice automated sleep stage scoring

Sleep stage scoring in rodents is the process of identifying the three stages: nonrapid eye movement sleep (NREM), rapid eye movement sleep (REM), and wake. Sleep stage scoring is crucial for studying sleep stage-specific measures and effects. 
Sleep patterns in rodents differ from those in humans, characterized by shorter episodes of NREM and REM interspaced by waking, and traditional manual sleep stage scoring by human experts is time-consuming. To address this issue, previous studies have used machine learning-based approaches to develop algorithms to automatically categorize sleep stages, but high-performing models with great generalizability are often not publicly available/cost-free nor user-friendly for non-trained sleep researchers. 
Therefore, we developed a machine learning-based LightGBM algorithm trained with a large dataset. To make the model available to sleep researchers without coding experience, a software tool named IntelliSleepScorer (v1.2- newest version) was developed based on the model, which features an easy-to-use graphic user interface. In this manuscript, we present step-by-step instructions for using the software to demonstrate a convenient and effective automatic sleep stage scoring tool in mice for sleep researchers.

There are three plots (only the top plot if SHAP values were not run) generated in the GUI after sleep stage scoring: the top plot presents EEG and EMG channels with a hypnogram of sleep stage prediction. The middle plot presents epoch SHAP values. The bottom plot presents Global SHAP values (Figure 1).
There are 4 types of data presented in the sleep stage prediction hypnogram plot (Figure 2). The top row is the predicted results. Th...

Author Spotlight: IntelliSleepScorer &#8212; A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

We present a software package with a graphic user interface for researchers without coding experience to score sleep stages in mice with a simple download and operation.

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring

Sleep stage scoring in rodents is the process of identifying the three stages: nonrapid eye movement sleep (NREM), rapid eye movement sleep (REM), and ...

intellisleepscorer-software-package-with-graphic-user-interface-for

Research

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Neuroscience

401 Views.  Broad Institute of MIT and Harvard. We present a software package with a graphic user interface for researchers without coding experience to score sleep stages in mice with a simple download and operation. 

Video: Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia

We describe a protocol for the rapid and sensitive quantification of disease severity in mouse models of cerebella ataxia. It is derived from previously published phenotype assessments in several disease models, including spinocerebellar ataxias, Huntington s disease and spinobulbar muscular atrophy. Measures include hind limb clasping, ledge test, gait and kyphosis. Each measure is recorded on a scale of 0-3, with a combined total of 0-12 for all four measures. The results effectively discriminate between affected and non-affected individuals, while also quantifying the temporal progression of neurodegenerative disease phenotypes. Measures may be analyzed individually or combined into a composite phenotype score for greater statistical power. The ideal combination of the four described measures will depend upon the disorder in question. We present an example of the protocol used to assess disease severity in a transgenic mouse model of spinocerebellar ataxia type 7 (SCA7).
Albert R. La Spada and Gwenn A. Garden contributed to this manuscript equally.

We describe a protocol for the rapid and sensitive quantification of disease severity in mouse models of cerebellar ataxia. Measures include hind limb clasping, ledge test, gait and kyphosis. This protocol effectively discriminates between affected and non-affected individuals, and detects the progression of affected individuals over time.

We describe a protocol for the rapid and sensitive quantification of disease severity in mouse models of cerebella ataxia. It is derived from previously ...

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the &#34;cloud&#34; and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in&#160;cabinet; two such cabinets (16 environments) may be controlled by one computer.

Fully automated system for measuring physiologically meaningful properties of the mechanisms mediating spatial localization, temporal localization, duration, rate&#160;and probability estimation, risk assessment, impulsivity, and the accuracy and precision of memory, in order to assess the effects of genetic and pharmacological manipulations on foundational mechanisms of cognition in mice.

We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and ...

Behavior

Automated Interactive Video Playback for Studies of Animal Communication

Video playback is a widely-used technique for the controlled manipulation and presentation of visual signals in animal communication. In particular, parameter-based computer animation offers the opportunity to independently manipulate any number of behavioral, morphological, or spectral characteristics in the context of realistic, moving images of animals on screen. A major limitation of conventional playback, however, is that the visual stimulus lacks the ability to interact with the live animal. Borrowing from video-game technology, we have created an automated, interactive system for video playback that controls animations in response to real-time signals from a video tracking system. We demonstrated this method by conducting mate-choice trials on female swordtail fish, Xiphophorus birchmanni. Females were given a simultaneous choice between a courting male conspecific and a courting male heterospecific (X. malinche) on opposite sides of an aquarium. The virtual male stimulus was programmed to track the horizontal position of the female, as courting males do in the wild. Mate-choice trials on wild-caught X. birchmanni females were used to validate the prototype's ability to effectively generate a realistic visual stimulus.

Video playback is a widely used technique in animal behavior. We created and evaluated a program that applies rules-based, interactive playback of 3-D computer animations in response to real-time, automated data on subject behavior.

Video playback is a widely-used technique for the controlled manipulation and presentation of visual signals in animal communication. In particular, ...

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Traditionally, sleep is monitored by an electroencephalogram (EEG). EEG studies in rodents require surgical implantation of the electrodes followed by a long recovery period. To perform an EEG recording, the animal is connected to a receiver, creating an unnatural tether to the head-mount. EEG monitoring is time consuming, carries risk to the animal, and is not a completely natural setting for the measurement of sleep. Alternative methods to detect sleep, particularly in a high-throughput fashion, would greatly advance the field of sleep research. Here, we describe a validated method for detecting sleep via activity-based home-cage monitoring. Previous studies have shown that sleep assessed via this method has a high degree of agreement with sleep defined by traditional EEG-based measures. Whereas this method is validated for total sleep time, it is important to note that sleep bout duration should be assessed by an EEG which has better temporal resolution. The EEG can also differentiate rapid eye movement (REM) and non-REM sleep, giving more detail about the exact nature of sleep. Nevertheless, activity-based sleep determination can be used to analyze multiple days of undisturbed sleep and to assess sleep as a response to an acute event (like stress). Here, we show the power of this system to detect the response of mice to daily intraperitoneal injections.

We describe a high-throughput method of measuring sleep by means of activity-based home-cage monitoring. This method offers advantages over traditional EEG-based methods. It is well validated for the determination of total sleep duration and can be a powerful tool to monitor sleep in rodent models of human disease.

Traditionally, sleep is monitored by an electroencephalogram (EEG). EEG studies in rodents require surgical implantation of the electrodes followed by a ...

A High-performance Liquid Chromatography Measurement of Kynurenine and Kynurenic Acid: Relating Biochemistry to Cognition and Sleep in Rats

The kynurenine pathway (KP) of tryptophan degradation has been implicated in psychiatric disorders. Specifically, the astrocyte-derived metabolite kynurenic acid (KYNA), an antagonist at both N-methyl-d-aspartate (NMDA) and &#945;7 nicotinic acetylcholine (&#945;7nACh) receptors, has been implicated in cognitive processes in health and disease. As KYNA levels are elevated in the brains of patients with schizophrenia, a malfunction at the glutamatergic and cholinergic receptors is believed to be causally related to cognitive dysfunction, a core domain of the psychopathology of the illness. KYNA may play a pathophysiologically significant role in individuals with schizophrenia. It is possible to elevate endogenous KYNA in the rodent brain by treating animals with the direct bioprecursor kynurenine, and preclinical studies in rats have demonstrated that acute elevations in KYNA may impact their learning and memory processes. The current protocol describes this experimental approach in detail and combines a) a biochemical analysis of blood kynurenine levels and brain KYNA formation (using high-performance liquid chromatography), b) behavioral testing to probe the hippocampal-dependent contextual memory (passive avoidance paradigm), and c) an assessment of sleep-wake behavior [telemetric recordings combining electroencephalogram (EEG) and electromyogram (EMG) signals] in rats. Taken together, a relationship between elevated KYNA, sleep, and cognition is studied, and this protocol describes in detail an experimental approach to understanding function outcomes of kynurenine elevation and KYNA formation in vivo in rats. Results obtained through variations of this protocol will test the hypothesis that the KP and KYNA serve pivotal roles in modulating sleep and cognition in health and disease states.

Alterations in the kynurenine pathway (KP) neuroactive metabolites are implicated in psychiatric illnesses. Investigating the functional outcomes of an altered kynurenine pathway metabolism in vivo in rodents may help elucidate novel therapeutic approaches. The current protocol combines biochemical and behavioral approaches to investigate the impact of an acute kynurenine challenge in rats.

The kynurenine pathway (KP) of tryptophan degradation has been implicated in psychiatric disorders. Specifically, the astrocyte-derived metabolite ...

Polygraphic Recording Procedure for Measuring Sleep in Mice

Recording of the epidural electroencephalogram (EEG) and electromyogram (EMG) in small animals, like mice and rats, has been pivotal to study the homeodynamics and circuitry of sleep-wake regulation. In many laboratories, a cable-based sleep recording system is used to monitor the EEG and EMG in freely behaving mice in combination with computer software for automatic scoring of the vigilance states on the basis of power spectrum analysis of EEG data. A description of this system is detailed herein. Steel screws are implanted over the frontal cortical area and the parietal area of 1 hemisphere for monitoring EEG signals. In addition, EMG activity is monitored by the bilateral placement of wires in both neck muscles. Non-rapid eye movement (Non-REM; NREM) sleep is characterized by large, slow brain waves with delta activity below 4 Hz in the EEG, whereas a shift from low-frequency delta activity to a rapid low-voltage EEG in the theta range between 6 and 10 Hz can be observed at the transition from NREM to REM sleep. By contrast, wakefulness is identified by low- to moderate-voltage brain waves in the EEG trace and significant EMG activity.

The recording of electroencephalogram (EEG) and electromyogram (EMG) in freely behaving mice is a critical step to correlate behavior and physiology with sleep and wakefulness. The experimental protocol described herein provides a cable-based system for acquiring EEG and EMG recordings in mice.

Recording of the epidural electroencephalogram (EEG) and electromyogram (EMG) in small animals, like mice and rats, has been pivotal to study the ...

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Three vigilance states dominate mammalian life: wakefulness, non-rapid eye movement (non-REM) sleep, and REM sleep. As more neural correlates of behavior are identified in freely moving animals, this three-fold subdivision becomes too simplistic. During wakefulness, ensembles of global and local cortical activities, together with peripheral parameters such as pupillary diameter and sympathovagal balance, define various degrees of arousal. It remains unclear the extent to which sleep also forms a continuum of brain states-within which the degree of resilience to sensory stimuli and arousability, and perhaps other sleep functions, vary gradually-and how peripheral physiological states co-vary. Research advancing the methods to monitor multiple parameters during sleep, as well as attributing to constellations of these functional attributes, is central to refining our understanding of sleep as a multifunctional process during which many beneficial effects must be executed. Identifying novel parameters characterizing sleep states will open opportunities for novel diagnostic avenues in sleep disorders.
We present a procedure to describe dynamic variations of mouse non-REM sleep states via the combined monitoring and analysis of electroencephalogram (EEG)/electrocorticogram (ECoG), electromyogram (EMG), and electrocardiogram (ECG) signals using standard polysomnographic recording techniques. Using this approach, we found that mouse non-REM sleep is organized into cycles of coordinated neural and cardiac oscillations that generate successive 25-s intervals of high and low fragility to external stimuli. Therefore, central and autonomic nervous systems are coordinated to form behaviorally distinct sleep states during consolidated non-REM sleep. We present surgical manipulations for polysomnographic (i.e., EEG/EMG combined with ECG) monitoring to track these cycles in the freely sleeping mouse, the analysis to quantify their dynamics, and the acoustic stimulation protocols to assess their role in the likelihood of waking up. Our approach has already been extended to human sleep and promises to unravel common organizing principles of non-REM sleep states in mammals.

Here, we present experimental and analytical procedures to describe the temporal dynamics of the neural and cardiac variables of non-REM sleep in mice, which modulate sleep responsiveness to acoustic stimuli.

Three vigilance states dominate mammalian life: wakefulness, non-rapid eye movement (non-REM) sleep, and REM sleep. As more neural correlates of behavior ...

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.

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. ...

Assessing Sleep Quality and Cognitive Function in Patients with Major Depressive Disorder

Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder

Cognitive symptoms and sleep disturbance (SD) are common non-mood-related symptoms of major depressive disorder (MDD). In clinical practice, both cognitive symptoms and SD are related to MDD progression. However, there are only a few studies investigating the connection between cognitive symptoms and SD in patients with MDD, and only preliminary evidence suggests a significant association between cognitive symptoms and SD in patients with mood disorders. This study investigates the relationship between cognitive symptoms and sleep quality in patients with major depressive disorder. Patients (n = 20) with MDD were enrolled; their mean Hamilton Depression Scale-17 score was 21.95 (&plusmn;2.76). Gold standard polysomnography (PSG) was used to assess sleep quality, and the validated THINC-integrated tool (the cognitive screening tool) was used to evaluate cognitive function in MDD patients. Overall, the results showed significant correlations between the cognitive screening tool's total score and sleep latency, wake-after-sleep onset, and sleep efficiency. These findings indicate that cognitive symptoms are associated with poor sleep quality among patients with MDD.

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression

Here, we present a protocol that investigates the relationship between cognitive symptoms and sleep quality in patients with major depressive disorder. The THINC-integrated tool and polysomnography were used to evaluate cognitive symptoms and sleep quality.

Cognitive symptoms and sleep disturbance (SD) are common non-mood-related symptoms of major depressive disorder (MDD). In clinical practice, both ...

Medicine

Analyzing Sleep Behavior in Mice through Electroencephalogram and Electromyogram Recordings

Source: Oishi, Y., et&#160; al. <a href="https://app.jove.com/v/53678">Polygraphic Recording Procedure for Measuring Sleep in Mice.</a> J. Vis. Exp. (2016).
This video demonstrates a protocol for implanting an electroencephalogram (EEG) and electromyogram (EMG) electrode assembly in an anesthetized mouse. The process involves securing the mouse on a stereotaxic frame, making a midline incision in the skull, drilling holes for the electrodes, and fixing the electrode unit for signal recording. After recovery, the mouse&#39;s sleep-wake behavior is analyzed using the recorded signals.

All procedures involving animal models have been reviewed by the local institutional animal care committee and the JoVE veterinary review board.
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