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79 results for “mouse behavior”
Neurons for infant social behaviors in the mouse zona incerta
<p><strong>Neurons for infant social behaviors in the mouse zona incerta</strong></p> <p>Repository containing datasets supporting the study.</p> <p>Github link to related analysis code: https://github.com/yxl95/zona_incerta_infant_social_behavior</p>
Behavior-relevant top-down cross-modal predictions in mouse neocortex
<p>Simultaneously recorded S1 and PPC neuronal population activity from awake mice during a texture discrimination task. Data acquired with two-photon calcium imaging.</p>
Data set for "Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior"
<p>Data set for: Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S (2024) Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior. Cell Reports 43: 113618. https://doi.org/10.1016/j.celrep.2023.113618</p> <p> </p> <p>There are 2 files in this upload:</p> <p> </p> <p>1. The file named "2024_Oryshchuk_CellReports.pdf" is the Open Access pdf of the online publication in Cell Reports.</p> <p> </p> <p>2. The file named " Oryshchuk _data_code.zip" (~1.8 GB) is a zipped version of a folder "Oryshchuk _data_code" (~2.3 GB), which contains the preprocessed data analyzed in the study along with the Matlab and Python codes used to generate the published figures. To access the data and codes, first unzip the file.</p> <p>· The subfolder “Atlas” contains templates from the Allen Mouse Brain Reference Altas of anatomical brain sections used to map the location of the silicon probes (Supplementary Figure S1).</p> <p>· The subfolder “Clustering-master” contains the Matlab codes used for the clustering on neuronal activity (Figure 1). The output is the data structure ‘Data_Clustering.mat’ file already provided in the folder ‘Data’.</p> <p>· The subfolder “Code” contains the main Matlab codes used to analyze the data and plot the figures. The ouput from the clustering and decoding analyses are provided in the ‘Data’ folder, thus the Matlab codes can be run independently, without running the ‘clustering’ or ‘decoding’ codes first.</p> <p>· The subfolder “Data” contains the Matlab data structures containing the electrophysiological and behavioral data from whisker rewarded (‘DataWR.mat’) and non-rewarded (‘DataWnonR.mat’) mice, the behavioral data for optogenetic inactivation in rewarded mice, the clustering results (‘Data_Clustering.mat’) and a subfolder containing the results from the decoding analyses (“Decoding”).</p> <p>· The subfolder “decoding” contains the Python codes used for the decoding analyses. The required configuration can be found in the file ‘requirements.txt’. To run the codes, follow instructions from the ‘README.md’ file.</p> <p>· The subfolder “Figures” will be populated with figures saved in .png and .eps formats as well as a ‘Methods.txt’ files when running the main Matlab codes.</p> <p>· The subfolder “Functions” contains subfunctions used by the main Matlab codes to analyze the data and plot the figures.</p> <p>· The subfolder “Results” will be populated with Matlab data structures as well as a ‘.xlsx’ files when running the main Matlab codes.</p> <p>When running the code, you need to set the Matlab file path to be "Oryshchuk _data_code". In addition, you should add the folder "Oryshchuk_data_code" with subfolders to the Matlab path. Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication. Please note that some of the code can take several hours to execute.</p>
Data from: Offspring behavioral outcomes following maternal allergic asthma in the IL-4-deficient mouse
<p>Background: Maternal allergies and asthma during pregnancy have been associated with increased risk of ASD and ADHD to the child. Previous rodent studies have demonstrated that inducing a T helper-2 (Th2)-mediated allergic response during pregnancy leads to an offspring behavioral phenotype characterized by decreased social interaction and increased stereotypies. Interleukin-4 is a key signal in the Th2 immune cascade, but its role in fetal brain development and subsequent impacts of maternal allergic asthma (MAA) on offspring behavioral deficits have yet to be determined.</p> <p>Objective: In this study, we investigated whether the absence of IL-4 signaling would mitigate MAA-induced behavioral changes.</p> <p>Methods: C57BL/6J and Interleukin-4 knockout (IL-4 KO) mice were sensitized to ovalbumin and exposed to repeated allergic asthma aerosol inductions throughout pregnancy. Offspring were assessed on Juvenile Reciprocal Social Interaction, Elevated Plus Maze, Open Field Exploration, Novel Object Recognition, Forced Swim, Marble-burying, and Grooming tasks.</p> <p>Results: MAA during pregnancy resulted in decreased social interactions in male C57 offspring and impaired memory performance in both male and female C57 mice. These deficits were not observed in IL-4 KO mice exposed to MAA. However, we observed genotype effects in IL-4 KO mice including altered motor performance and anxiety-associated responses.</p> <p>Conclusion: MAA-induced social and cognitive behavioral alterations are IL-4 dependent. IL-4KO offspring display genotype-specific differences suggesting IL-4 signaling is important for typical developmental processes.</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - Mouse feeding behavior dataset
<p>Dataset contains feeding and drinking behavioral recordings of C57BL6/J male mice. Mice were distributed into 2 groups (9 control mice and 8 high-fat diet mice) and tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a></p> <p>The data set consist in:</p> <p>- a "mouse_recordings" folder containing a CSV file containing mouse recordings and the files.</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "phases" folder containing a CSV file containing experimental phases.</p> <p>- a "chromHMM_files" folder containing a cellmarkfiletable table used by chromHMM to learn a HMM model</p>
Reciprocal F1 hybrids of two inbred mouse strains reveal parent-of-origin and perinatal diet effects on behavior and expression
<p>Raw data and statistical analyses from an experiment to study parent-of-origin and diet-by-parent-of-origin effects on expression and behavior. </p> <p>In this experiment, female NOD/ShiLtJ x C57Bl/6J and C57Bl/6J x NOD/ShiLtJ mice were exposed in utero to one of four diets. After weaning, their whole-brain gene expression, as well as a set of behaviors that model psychiatric disease, were recorded and analyzed.</p> <p>File_S1_README contains detailed descriptions of all included files.</p>
Figs 2-6. Agonistic behavior between a in The hard task of a short-tailed mouse opossum (Monodelphis) to prey a harvestman (Arachnida: Opiliones)
Figs 2-6. Agonistic behavior between a harvestman of the family Gonyleptidae and the mouse opossum Monodelphis dimidiata (Wagner, 1847). The interaction starts with the mouse opossum in an attack position, facing the harvestman (Fig. 2), then the marsupial staggers side to side (Fig. 3) and is knocked out (Fig. 4). This sequence of events is repeated two times, until the mouse opossum assumes its third attack position and attacks the harvestman (Fig. 5). The mouse opossum removes the harvestman's legs one by one to then feed on its body (Fig. 6). Image edited in the Inkscape software.
Data from: Offspring behavioral outcomes following maternal allergic asthma in the IL-4-deficient mouse
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DEPRECATED Mouse feeding behavior dataset
<p>Mouse feeding behavior dataset.</p>
Additional raw video and pose estimation data of top view open field mouse behavior recordings after diazepam injections
<p>This repository contains raw data for 32 different open field recordings of mice. These include top view raw video .mp4 files (Videos.zip and Videos_2.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from a single diazepam injection experiment at Roche, where animals were injected with saline, 1 mg/kg, 2 mg/kg or 3 mg/kg of diazepam. The METADATA_ROCHE.csv or METADATA_ROCHE.xlsx files contain all grouping variables and help linking the pose estimation files (located in data/) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how we have used this data.</p>
Additional raw video and pose estimation data of top view mouse behavior recordings (marble burying test, light-dark box, fear conditioning box) of acute and chronic stress models
<p>This repository contains raw data for 296 different behavioral recordings of mice (marble burying test, light-dark box, fear conditioning box). These include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
Data from: Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy
<p>Mice are the most commonly used model animals for itch research and for the development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best-trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and the chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening. This dataset includes all videos for the study to establish a novel deep learning-based system for automatic mouse scratching behavior quantification.</p>
An exploratory study (with and without time pressure) using mouse dynamics to detect faking-good behavior in the MMPI-2 and PPI-R validity scales
<p>Please find here the dataset generated and analyzed during the study entitled "Can mouse dynamics detect faking-good behavior in personality questionnaires? An exploratory study (with and without time pressure) using the MMPI-2 and PPI-R validity scales". Moreover, here you can find the source code of the experiment to execute the task using MouseTracker software, the code of the statistical analysis and a file containing the instructions to replicate ML model results reported in the original paper.</p>
Raw video and pose estimation data of top view open field mouse behavior recordings of acute and chronic stress models
<p>This repository contains raw data for 411 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
Raw video and pose estimation data of top view open field mouse behavior recordings after yohimbine injections
<p>This repository contains raw data for 32 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from a single yohimbine injection experiment at Roche, where animals were injected with saline, 1mg/kg,3mg/kg or 6mg/kg of yohimbine. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in data/Yohimbine_Roche) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how we have used this data</p>
Dataset of mouse EEG and behaviors under threat-and-escape paradigm (solitary threat condition)
<p>Original publication link: <a href="https://doi.org/10.1073/pnas.2308762120">https://doi.org/10.1073/pnas.2308762120</a></p> <p>A set of mouse EEG data (raw data files in [data_EEG-BIDS.zip]) and simultaneously recorded video (raw data files and processed behavioral data in [data_behavior.zip]). A mirror of this dataset is located here: <a href="https://gin.g-node.org/hiobeen/Mouse-threat-and-escape-Han-et-al-PNAS">https://gin.g-node.org/hiobeen/Mouse-threat-and-escape-Han-et-al-PNAS</a>.</p> <p>Check this <a href="https://gin.g-node.org/hiobeen/Mouse-threat-and-escape-Han-et-al-PNAS/src/master/README.md">how-to-start guide</a> to this dataset! <br> </p> <p>1. EEG dataset</p> <p>Most of all, the overall dataset structure adheres to the BIDS-EEG format introduced by Pernet et al. (2019). Within the top-level directory 'data_EEG-BIDS/', the EEG data is organized under paths starting with 'sub-##/'. These EEG recordings (mouse n = 8) were recorded under the Threat-and-escape paradigm experiment, which involves dynamic interactions with a spider robot. This experiment was done in two separate conditions: the solitary condition, where a mouse was exposed to the robot alone in the arena (referred to as the 'Single' condition), and the group condition, where mice encountered the robot alongside other conspecifics (referred to as the 'Group' condition). This dataset only includes the data from solitary condition. CBRAIN headstage (Kim et al., 2019) was employed to record this EEG data at a sampling rate of 1024 Hz. The recordings were taken from the medial prefrontal cortex (channel 1) and the basolateral amygdala (channel 2). For a comprehensive understanding of the experimental methods and procedures, please see our original publications: Han et al. (2023), Cho et al. (2023), and Kim et al. (2020).</p> <p> </p> <p>2. Position dataset</p> <p>Another top-level directory, 'data_behavior/', contains simultaneously recorded video (in avi format) ('data_behavior/raw/') and position extracted from the video in csv format ('data_behavior/processed/'). The positions are located in the 'stimuli/position/' directory.</p> <p>Position tracking is performed using the U-Net architecture of CNN (Ronnenberger, 2015; also see, Han et al. in press for detailed procedure). This method tracks the body area's location to extract its centroid.</p> <p> </p> <p>3. References</p> <p>Han, H. B., Shin, H. S., Jeong, Y., Kim, J., Choi, J. H., (2023) Dynamic switching of neural oscillations in the prefrontal–amygdala circuit for naturalistic freeze-or-flight, <em>Proceedings of the National Academy of Sciences,</em>, <em>120</em>(37), <a href="https://doi.org/10.1073/pnas.2308762120">https://doi.org/10.1073/pnas.2308762120</a></p> <p>Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., & Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. <em>Scientific data</em>, <em>6</em>(1), 103.</p> <p>Kim, J., Kim, C., Han, H. B., Cho, C. J., Yeom, W., Lee, S. Q., & Choi, J. H. (2020). A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC). <em>Science Advances</em>, <em>6</em>(49).</p> <p>Cho, S., & Choi, J. H. (2023). A guide towards optimal detection of transient oscillatory bursts with unknown parameters. <em>Journal of Neural Engineering</em>.</p> <p>Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In <em>Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18</em> (pp. 234-241). Springer International Publishing.</p> <p> </p> <p>written by Hio-Been Han, hiobeen.han@gmail.com, 2023-09-07.</p>
Microscopy images of the effects of PDZ-RhoGEF manipulation on dendritic spines and videos of effects on PDZ-RhoGEF on mouse behavioral phenotypes
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Data from: Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy
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Mouse behavior and neural activities of dopamine and D1- and D2- neurons in the tail of the striatum under threat-reward conflict and dopamine action onto the striatal neurons
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Data from: Are shy individuals less behaviorally variable? Insights from a captive population of mouse lemurs
Increasingly, individual variation in personality has become a focus of behavioral research in animal systems. Boldness and shyness, often quantified as the tendency to explore novel situations, are seen as personality traits important to the fitness landscape of individuals. Here we tested for individual differences within and across contexts in behavioral responses of captive mouse lemurs (Microcebus murinus) to novel objects, novel foods, and handling. We report consistent differences in behavioral responses for objects and handling. We also found that the responses to handling and novel objects were correlated and repeatable. Lastly, we show that shyer individuals may show less variability in their behavioral responses. This study provides new information on the potential for behavioral syndromes in this species and highlights differences in the degree to which behavioral types (e.g., shy/bold) vary in their behavioral responses.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.