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996 results for “mouse brain”
ISS Mouse brain embryo - MIPPED images , all rounds all channels
<p>Repository containing the stitched, mipped and aligned images of all the cycles and channels used in the Mouse embryo ISS characterization from La Manno et al 2020 The repository contains:</p> <ul> <li>Stitched aligned and mipped images of all round and cycles for different samples (2A,2D, 6B,10B)</li> <li>A codebook with the code of every expected gene detecoded is included</li> <li>A preliminary decoding of the 4 samples included in the folder "decoded_spots"</li> <li>Information about channel order in a .txt</li> </ul>
BIDS Data for "A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation"
<p>Base data package for the “"A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation” article, formatted corresponding to the Brain Imaging Data Structure.</p>
An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain
<p>This archive contains the dataset produced by the Blue Brain Project (BBP) for improving the Common Coordinate Framework version 3 (CCFv3) mouse brain atlas from the Allen Institute for Brain Science (AIBS). The dataset nomenclature is aligned with AIBS standards, utilizing the Allen Reference Atlas (ARA) Nissl-stained volume sectioned in the coronal incidence (ARA NisslCOR) and the annotation file version 3 (ANNOTv3). Additional data were used, such as the AIBS Nissl-stained volume sectioned in the sagittal incidence (AIBS NisslSAG, Allen Mouse Brain Atlas ID 100042147) as well as a Waxholm (WAXH) Nissl-stained volume sectioned in the horizontal incidence (WAXH NisslHOR; https://www.nitrc.org/projects/incfwhsmouse).</p> <p>Here is a list of the files produced and shared below with their descriptions:</p> <p><strong>ara_bbp_nisslCOR_25 - 10</strong>: ARA Nissl-stained volume sectioned in the coronal incidence at 25 and 10 μm isotropic resolution accurately aligned in the CCFv3.</p> <p><strong>arav3a_bbp_nisslCOR_25 - 10</strong>: ARA Nissl-stained volume sectioned in the coronal incidence at 25 and 10 μm isotropic resolution accurately aligned in the CCFv3 and extended for covering the entire brain.</p> <p><strong>annotv3am_bbp_manual_25</strong>: Expert manual delineation of the extended tissue based on ARAv3aBBP NisslCOR at 25 isotropic resolution as well as of the granular and molecular layers in the cerebellum.</p> <p><strong>annotv3a_bbp_25 - 10</strong>: Extended CCFv3 annotation at 25 and 10 μm isotropic resolution covering the entire mouse brain plus including new granular and molecular layers in all cerebellar lobules, assessed using ANNOTv3am.</p> <p><strong>annotv3c_bbp</strong>: Extended CCFv3aBBP annotation covering the mouse central nervous system including spinal cord as well as barrel columns in the isocortex.</p> <p><strong>aibs_bbp_nisslSAG_25</strong>: AIBS sagittal Nissl-stained volume aligned in the CCFv3aBBP at 25 μm isotropic resolution.</p> <p><strong>waxh_bbp_nisslHOR_25</strong>: WAXH horizontal Nissl-stained volume aligned in the CCFv3aBBP at 25 μm isotropic resolution.</p> <p><strong>annotation_bbp_atlas_pipeline_25</strong>: Annotation file from the Blue Brain cell atlas pipeline including the extended version annotv3a_bbp_25, plus some additional sublayers such as layer 2 and layer 3, as well as the barrel columns in the isocortex.</p> <p><strong>hierarchy_bbp_atlas_pipeline</strong>: Hierarchy file attached to the annotation_bbp_atlas_pipeline_25 version.</p> <p><strong>average_nissl_init_25_v3a_CBcorrected</strong>: Average Nissl-stained template composed of the average of arav3a_bbp_nisslCOR_25, aibs_bbp_nisslSAG_25, and waxh_bbp_nisslHOR_25 and including some automated corrections of the artifacts in the cerebellum. This was used as a reference and initialization for building the average Nissl-stained template.</p> <p><strong>average_nissl_template</strong>: Symmetric (symmetric_full) and non symmetric (nissl_average_full) averaged Nissl-stained template in the CCFv3aBBP, as well as the number of occurences per voxel (frequency) in the averaging process.</p> <p><strong>QuickNII-CCFv3a-extended</strong>: Extended CCFv3a atlas file compatible with QuickNII software.</p> <p><strong>VisuAlign-v0.91</strong>: Extended CCFv3a atlas file compatible with VisuAlign software.</p> <p>An additional video (<strong>FullBrainAtlas_bbp</strong>) is provided in that archive, presenting the different mouse brain annotations from AIBS to BBP ones, as well as the BBP computed neuron distribution among the entire mouse brain colored by regions given AIBS standards. The code for creating the data in the video is accessible at https://github.com/favreau/BioExplorer/tree/master/bioexplorer%2Fpythonsdk%2Fnotebooks%2Fccfv3.</p> <p>For accessing to the code related to that work, please go to the corresponding GitHub repository: https://github.com/BlueBrain/ccfv3a-extended-atlas.</p> <p>--</p> <p>Citation:</p> <p>Piluso, S., Verasztó, C., Carey, H., Delattre, É., L’Yvonnet, T., Colnot, É., Romani, A., Bjaalie, J. G., & Keller, D. (2024). An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain. Zenodo. <a href="https://doi.org/10.5281/zenodo.13640418" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.13640418</a></p> <p>--</p> <p>Reference paper:</p> <p>Sébastien Piluso, Csaba Verasztó, Harry Carey, Émilie Delattre, Thibaud L’Yvonnet, Éloïse Colnot, Armando Romani, Jan G. Bjaalie, Henry Markram, Daniel Keller; An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain. <em>Imaging Neuroscience</em> 2025; doi: <a href="https://doi.org/10.1162/imag_a_00565" target="_blank" rel="noopener">https://doi.org/10.1162/imag_a_00565</a></p> <p>--</p> <p>Funding:</p> <p><em>This study was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology. This project/research has received funding from the European Union’s Research and Innovation Program Horizon Europe under Grant Agreement no. 101147319 (EBRAINS 2.0).</em></p>
Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin
<p>This dataset and the associated Python notebooks are related to the publication "Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin".</p>
Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"
<p>Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat 15: 791015. https://doi.org/10.3389/fnana.2021.791015</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2022_Liu_FrontNeuroanat.pdf</strong>" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy.</p> <p>2. The file named "<strong>Liu_data_code.zip</strong>" (~1 GB) is a zipped version of a folder ‘<em>Liu_data_code</em>’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.</p> <p> </p>
Small-angle X-ray scattering datasets for imaging crossing fibers in mouse, pig, monkey, and human brain
<p>Small-angle X-ray scattering datasets for resolving crossing fibers (myelinated neuronal axon bundles), as described in</p> <p>"<strong><em>Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering</em></strong>"</p> <p>deposited in bioRxiv:</p> <p>https://doi.org/10.1101/2022.09.30.510198</p>
A deep learning-based dataset of WFA-positive perineuronal nets and parvalbumin neurons localizations in the adult mouse brain
<p><strong>Quality-controlled predictions of deep learning models for cell counting</strong></p> <p>This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper:</p> <p><em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain.</em></p> <p>The dataset integrates the raw data published on a <a href="https://zenodo.org/record/7419282">previous upload</a> on Zenodo.</p> <p>Cell locations were obtained using two deep-learning models for cell counting (publicly available on <a href="http://github.com/ciampluca/counting_perineuronal_nets">GitHub</a>, details in the paper by <a href="https://www.sciencedirect.com/science/article/pii/S1361841522001475">Ciampi et al., 2022</a>). The output of the deep-learning pipeline was filtered based on the <em>score</em> assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. </p> <p><strong>Content</strong></p> <p>The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper.</p> <p><strong>Folder Structure and file naming conventions</strong></p> <p>There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells).</p> <p> </p>
MRI Brain Template and Atlas of the Mouse Lemur Primate Microcebus murinus
<p>MRI template and 120-region atlas for the mouse lemur primate Microcebus murinus.<br> <br> Generated from 34 animals aged 15-58 months old scanned at 7T using a T2-weighted sequence, resolution 115 × 115 × 230 µm. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; <a href="https://sammba-mri.github.io/">https://sammba-mri.github.io/</a>). The template was up-sampled to 91 µm isotropic for hand-segmentation of structures, and also used to create probability maps of grey matter, white matter and cerebro-spinal fluid.</p> <p>if used for publication please cite: </p> <p><strong>A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy</strong><br> Nachiket A Nadkarni, Salma Bougacha, Clément Garin, Marc Dhenain, Jean-Luc Picq<br> Jan 2019<br> <strong>NeuroImage</strong> 185, 85-95<br> DOI: 10.1016/J.NEUROIMAGE.2018.10.010<br> <a href="https://www.sciencedirect.com/science/article/pii/S1053811918319694">https://www.sciencedirect.com/science/article/pii/S1053811918319694</a></p>
CAMRI Mouse Brain MRI Data
Open the record for dataset details and reuse information.
Subset of nucleosomal DNA sequences from mouse brain nucleus accumbens tissue (GEO dataset GSE54263)
<p>This dataset contains a subset of nucleosomal DNA sequences of +1 nucleosomes from mouse brain nucleus accumbens cells (NAC) used to analyze nucleosome positioning sequence (NPS) patterns in <a href="https://doi.org/10.1371/journal.pcbi.1007365">Pranckeviciene, Erinija and Hosid, Sergey and Liang, Nathan and Ioshikhes, Ilya (2020). Nucleosome positioning sequence patterns as packing or regulatory. In PLoS computational biology, 16 (1), pp. e1007365.</a></p> <ul> <li>controlm.fa.gz contains sequences of <strong>control</strong> mice (GSE54263 subset Con_H3 GSM1311267)</li> <li> resilientm.fa.gz contains sequences of mice <strong>resilient to social stress</strong> (GSE54263 subset Res_H3 GSM1311268)</li> <li> susceptiblem.fa.gz contains sequences of<strong> </strong>mice <strong>susceptible to social stress</strong> (GSE54263 subset Sus_H3 GSM1311269)</li> </ul> <p>This dataset originates from the GEO accession GSE54263 data from <a href="https://www.nature.com/articles/nm.3939">Sun H, Damez-Werno DM, Scobie KN, Shao NY et al. ACF chromatin-remodeling complex mediates stress-induced depressive-like behavior. <em>Nat Med</em> 2015 Oct;21(10):1146-53.</a></p>
Serial Coronal Sections Of An Adult Mouse Brain - Sample Dataset
<p>Experimental data: 88 serial coronal sections of the full brain of an adult mouse.</p> <p>Preparation of the sample: details to come...</p> <p>Imaging of the sample: details to come...</p> <p>2 channels per section are present:</p> <ul> <li>DAPI</li> <li>autofluorescence</li> </ul> <p>This dataset is used in particular as a test dataset for the <a href="https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/image-to-atlas-registration/">Allen Brain Biop Aligner Fiji plugin</a>.</p> <p>Some sections are flipped (left / right), for workflow documentation purpose.</p> <p>Animal handling according to protocols approved by the Swiss animal license VD2808.1</p>
MyD88-TLR4-dependent choroid plexus activation precedes perilesional inflammation and secondary brain edema in a mouse model of intracerebral hemorrhage
<p>Supplementary data and code of the article "MyD88-TLR4-dependent choroid plexus activation precedes perilesional inflammation and secondary brain edema in a mouse model of intracerebral hemorrhage".</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 4. Mouse trajectory – images taken from http://iographica.com 4 hours in Photoshop (left) vs. 4 hours in Eclipse (right)
<p>Studies from different fields (Arroyo & Wei, 2006), (Mäkiaho & Poranen, 2012), (Lockton, Harrison, Cain, Stanton, & Jennings, 2013), (Seelye, et al., 2015), (Hehman, Stolier, & Freeman, 2014) have shown that mouse trajectory and clicks are environment (application, device) and user specific, especially if mapped over a long working session (Figure 4). </p>
Long-range axonal projections analyses of the mouse brain
<p>Accompanying data and analyses of the article <a href="https://doi.org/10.1101/2024.10.04.616605">"Generating brain-wide connectome using synthetic axonal morphologies"</a>. The code to reproduce the figures is available at <a href="https://github.com/Remy2506/axon_projection_figures">this repository</a>.</p> <p>Main contents:</p> <ul> <li><strong>SEU_morphs.zip, peng_2021_morphs.zip, ML_morphs.zip: </strong>input morphologies from various sources (novel morphologies collected by H. Peng, Southeast University, <a href="https://www.nature.com/articles/s41586-021-03941-1">Peng et al. 2021</a>, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754285/">Winnubst et al. 2019</a>) after <a href="https://github.com/BlueBrain/morphology-workflows"><em>morphology-workflows</em> <em>Repair</em></a> post-processing steps.</li> <li><strong>atlas</strong>: atlas of the mouse brain used (enhanced version of Allen Brain CCFv3)</li> <li><strong>out_a_p</strong>: output of the <a href="https://github.com/BlueBrain/axon-projection">axon projection</a> anaylsis and clustering on the 3601 morphologies of the dataset. <ul> <li>axon_lengths_12.csv, axon_terminals_12.csv: the lengths of axons in each subregion where they terminate, and terminals, at hierarchy level 12 from the brain hierarchy.</li> <li>clustering_output.csv: output of the GMM clustering.</li> <li>config_a_p.cfg: configuration file used to produce this analysis, with running the axonal projection code.</li> </ul> </li> <li><strong>circuit</strong>: contains files describing the circuit synthesized with Blue Brain's <em>circuit-build*.</em> <ul> <li>bioname: parameters used to synthesize the circuit (regions to synthesize, cell densities per region, location of axons to graft...).</li> <li>sonata: files that contain nodes and edges of the circuit.</li> <li>auxiliary: various cell collections from the synthesized cells, filtered by region. Cell collections are to be read with <a href="https://github.com/BlueBrain/voxcell/tree/main">Voxcell.</a></li> <li>conn_mat.h5: the connectivity matrices obtained for the case where MOp5 long-range axons were synthesized, computed with <a href="https://github.com/BlueBrain/ConnectomeUtilities/tree/main">ConnectomeUtilities.</a>Contains connectivity matrices for the synthesized LRAs, biological LRAs, and grafted local axons.</li> </ul> </li> <li><strong>local_axons</strong>: biological local axons that are grafted to the synthesized dendrites that do not have synthesized long-range axons.</li> <li><strong>synthesized_MOp5_LRAs</strong>: 1695 synthesized cells with long-range axons of the MOp5 region, in the atlas reference frame. </li> <li><strong>out_a_p_synth_MOp5</strong>: axonal projection analysis of the synthesized MOp5 axons.</li> <li><strong>synthesized_isocortex_cells</strong>: all synthesized cells of the isocortex region, except MOp5 cells. They are in h5 format, which takes less space than asc and swc. The h5 format can be read and converted for instance with <a href="https://github.com/BlueBrain/MorphIO">MorphIO</a>.</li> <li><strong>synthesized_isocortex_LRAs</strong>: 21680 synthesized cells with long-range axons of the isocortex regions for which a GMM cluster was created. </li> </ul> <p>Additional files:</p> <ul> <li>flatmap_both.nrrd: file used to generate a flat map visualization of the mouse isocortex, shown in the article.</li> <li>config_a_s.cfg: configuration file used to synthesize the long-range axons with the <a href="https://github.com/BlueBrain/axon-synthesis">axon-synthesis</a> code.</li> <li>target_pts: tufts common ancestors for the synthesized MOp5 axons.</li> <li>brain_atlas.zip: a lightweight version of the mouse brain atlas for the synthesis code example.</li> </ul> <p>*<em>These softwares might not be open-source at the time of publication of this data, but a public link will be provided as soon as they are.</em></p>
Impact of prenatal THC exposure on mouse brain development; a lifespan approach with MRI
<p>Prenatal cannabis exposure has been demonstrated to impact neurodevelopment in offspring at different ages. To date, to our knowledge, no study has longitudinally examined the effects from embryos to adulthood. Here we collected and analyzed data to explore how prenatal exposure to delta-9-tetrahydrocannabinol (5 mg/kg subcutaneous injections, gestational dat [GD] 3-10) in mice impacts trajectories of brain development with structural magnetic resonance imaging. We supplement these findings with behavioural analyses and electron microscopy as described below.</p> <p>In the first cohort (embryos) embryos were extracted on GD 17 and scanned with MRI postnatally, as described in the methods of the accompanying paper. Electron microscopy was used to investigate dark neural and glial cells, apoptotic cells, and dividing cells in the hippocampus. In the second cohort (neonates) pups were born and scanned postnatally with manganese enhanced MRI on postnatal day (PND) 3, 5, 7, and 10. Separation-induced ultrasonic vocalizations were acquired on PND 12 and pups were perfused on PND 13. EM analyses were repeated in the neonatal hippocampi. In the third cohort (adults) pups were scanned on PND 25, 35, 60, and 90. Behavioral assessments for anxiety-like behavior with open-field test and sensorimotor gating with prepulse inhibition were performed on PND 35 and 37 respectively. </p> <p>Findings showed altered prenatal body volumes and weight-trajectories, altered brain volumes (especially sustained in females until adulthood), and indications of changes to behavior, including anxiety-like phenotypes in neonates and adolescents. Evidence from electron microscopy suggests increased cell division in the embryo hippocampus. Together these data suggest a profound and sustained impact of early gestation prenatal THC exposure on brain development. For further details on the methods, approach, and results, please see the forthcoming publication.</p> <p>In this dataset you will find the following data:</p> <p>Pregnancy/dam-level outcomes can be found in maternal_outcomes.zip</p> <ul> <li><a href="../api/records/13820978/draft/files/zenodo_pregnancy_README.txt/content" target="_blank" rel="noopener noreferrer">zenodo_pregnancy_README.txt</a>: includes description of the data and fields available in each csv.</li> <li>dam_weights.csv: A spreadsheet including the information related to each dam pooled across the studies</li> <li>nest_quality.csv: A spreadsheet including the manually-rated nest quality from a pilot and the full experiment</li> <li>master_maternal_observations_old_thc.csv: A spreadsheet including data for time spent on and off nest extracted automatically and manually from Ethovision</li> </ul> <p>Embryo outcomes:</p> <ul> <li>zenodo_embryos_README.txt: includes description of the data and fields available in each csv.</li> <li>demographics_for_analysis.csv: A spreadsheet with relevant information for each embryo sample.</li> <li>raw_embryo_mincs.zip: includes 84 embryo scans, full body</li> <li>embryo_heads.zip: includes 57 embryo scans that all passed qc, head only niftis </li> <li>squish_qc.csv: QC of whether the embryos were squished or not</li> <li>em_embryo_hc_mm2csv.csv: cells per mm^2 from electron microscopy</li> </ul> <p>Neonate outcomes:</p> <ul> <li>zenodo_neonates_README.txt: includes description of data and fields available in each csv</li> <li>demographics.csv: A spreadsheet with the demographic information for each pup and timepoint in the study</li> <li>raw_neonate_niftis.zip: 172 scans from neonates in nifti format</li> <li>agreement_qc.csv: quality control file with assessments of raw images</li> <li>milestones_999_as_NA.csv: Record of which milestones were tested and whether they were obtained</li> <li>master_usv.csv: Spreadsheet including data for ultrasonic vocalizations from all tested pups</li> <li>neo_cell_counts_mm2.csv: cells per mm^2 from electron microscopy for the neonates</li> </ul> <p>Adult outcomes: </p> <ul> <li>zenodo_adult_README.txt: includes description of the data and fields available in each csv.</li> <li>demographics.csv: A spreadsheet with the demographic information for each mouse and timepoint in the study</li> <li>adult_raw_niftis.zip: The raw data (before preprocessing) in nifti format</li> <li>master_qc.csv: Quality control assessment of the raw images</li> <li>master_oft.csv: Values for open field test extracted from Ethovision</li> <li>avg_trials_ppi.csv: Data from prepulse inhibition trials, average startle of 100 ms following pulse</li> <li>max_trials_ppi.csv Dat afrom prepulse inhibition trials, maximum startle of 100 ms following pulse</li> </ul>
Dataset related to article "Fluoxetine rescues rotarod motor deficits in Mecp2 heterozygous mouse model of Rett syndrome via brain serotonin"
<p><em>The file contains raw data related to the article "Fluoxetine rescues rotarod motor deficits in Mecp2 heterozygous mouse model of Rett syndrome via brain serotonin", available from </em><a href="https://doi.org/10.1016/j.neuropharm.2020.108221">https://doi.org/10.1016/j.neuropharm.2020.108221</a><em>.</em></p> <p> </p> <p><strong>Abstract of the manuscript</strong></p> <p> Motor skill is a specific area of disability of Rett syndrome (RTT), a rare disorder occurring almost exclusively in girls, caused by loss-of-function mutations of the X-linked methyl-CpG-binding protein2 (MECP2) gene, encoding the MECP2 protein, a member of the methyl-CpG-binding domain nuclear proteins family. Brain 5-HT, which is defective in RTT patients and Mecp2 mutant mice, regulates motor circuits and SSRIs enhance motor skill learning and plasticity. In the present study, we used heterozygous (Het) Mecp2 female and Mecp2-null male mice to investigate whether fluoxetine, a SSRI with pleiotropic effects on neuronal circuits, rescues motor coordination deficits. Repeated administration of 10 mg/kg fluoxetine fully rescued rotarod deficit in Mecp2 Het mice regardless of age, route of administration or pre-training to rotarod. The motor improvement was confirmed in the beam walking test while no effect was observed in the hanging-wire test, suggesting a preferential action of fluoxetine on motor coordination. Citalopram mimicked the effects of fluoxetine, while the inhibition of 5-HT synthesis abolished the fluoxetine-induced improvement of motor coordination. Mecp2 null mice, which responded poorly to fluoxetine in the rotarod, showed reduced 5-HT synthesis in the prefrontal cortex, hippocampus and striatum, and reduced efficacy of fluoxetine in raising extracellular 5-HT as compared to female mutants. No sex differences were observed in the ability of fluoxetine to desensitize 5-HT<sub>1A</sub> autoreceptors upon repeated administration. These findings indicate that fluoxetine rescues motor coordination in Mecp2 Het mice through its ability to enhance brain 5-HT and suggest that drugs enhancing 5-HT neurotransmission may have beneficial effects on motor symptoms of RTT.</p>
Simultaneous dynamic glucose-enhanced (DGE) MRI and fiber photometry measurements of glucose in the healthy mouse brain
<p>This dataset was acquired for the DGE and fiber photometry study published in NeuroImage ( <a href="https://doi.org/10.1016/j.neuroimage.2022.119762">https://doi.org/10.1016/j.neuroimage.2022.119762</a>).<br> Comprises of three datasets: DGE MRI, fiber photometry and two-photon microscopy.</p>
A brain-wide, annotated dataset of WFA-positive perineuronal nets and parvalbumin neurons in the adult mouse brain
<p><strong>Microscopy dataset for perineuronal nets and parvalbumin-positive interneurons in the adult mouse brain</strong></p> <p>This dataset contains the data used in the paper titled:</p> <p><em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain</em></p> <p><strong>Content</strong></p> <p>The dataset contains microscopy images of coronal brain slices of 7 adult mice and several kinds of biological annotations.</p> <p>For each mouse, the annotations contain information about:</p> <ul> <li>Several files related to the alignment of each brain slice to the Allen Brain Institute CCFv3 atlas (for a more detailed description see <a href="https://github.com/LeonardoLupori/brainAlignment">here</a>)</li> <li>Location of individual PNNs and PV cells in each slice</li> </ul> <p><strong>Folder Structure</strong></p> <p>There are separate folders for each mouse. Each folder is named with the ID of that mouse.</p> <p>Each mouse folder contains:</p> <ol> <li>a <em>MOUSEID-info.xml</em> file - Contains general information for the mouse and images</li> <li>a <em>MOUSEID-quicknii.xml</em> file - Contains information for the alignment to the Allen Brain Atlas CCFv3</li> <li>a <em>MOUSEID-visualign.json</em> file - Contains information for the alignment to the Allen Brain Atlas CCFv3</li> <li>a <em>counts </em>folder - Contains annotations for PNNs and PV cell locations for each slice</li> <li>a <em>dispField </em>folder - Contains displacement fields for non-rigid alignment to the Allen Brain Atlas CCFv3</li> <li>a <em>hiRes </em>folder - Contains original, full-resolution, experimental images</li> <li>a <em>masks </em>folder - Contains binary masks for restricting the analysis</li> <li>a <em>thumbnails </em>folder - Contains low-resolution</li> </ol> <p><strong>Files Description</strong></p> <ul> <li><em>MOUSEID-info.xml</em> <ul> <li>XML file containing information about this mouse and details on each image</li> </ul> </li> <li><em>MOUSEID-quicknii.xml</em> <ul> <li>XML file used for global alignment of all the images to the CCFv3 using the software <a href="https://www.nitrc.org/projects/quicknii">QuickNII</a></li> </ul> </li> <li><em>MOUSEID-visualign.json</em> <ul> <li>JSON file used for the interactive local non-rigid alignment of brain slices to the CCFv3 using the software <a href="https://www.nitrc.org/projects/visualign">VisuAlign</a></li> </ul> </li> <li><em>counts </em>folder <ul> <li>Folder containing two .csv files for each high-resolution image. Each .csv file contains the (x,y) location of all PNNs (channel 1) and PV cells (channel 2) detected in that image</li> </ul> </li> <li><em>dispField </em>folder <ul> <li>This folder contains displacement fields in the X and Y direction for each image. These files are meant to be loaded in MATLAB and fed to the function <a href="https://it.mathworks.com/help/images/ref/imwarp.html">imwarp</a>. This function can be used to apply a non-rigid transformation to the reference volume slices in order for it to closely match experimental images.</li> </ul> </li> <li><em>hiRes </em>folder <ul> <li>Folder containing high-resolution experimental images split by channels</li> </ul> </li> <li><em>masks </em>folder <ul> <li>Folder containing binary masks. These files are used to restrict the analysis to portions of the image containing biological tissue and to exclude areas where the tissue was damaged or presented artifacts</li> </ul> </li> <li><em>thumbnails </em>folder <ul> <li>Folder containing a low-resolution RGB version of the experimental images </li> </ul> </li> </ul> <p> </p>
Mouse brain state classification dataset
<p>Data was collected from electrophysiological recordings of brain activity and behaviour in freely-moving mice. Brain electrical signals were recorded from micro-wire bundles implanted in the hippocampus. Behavioral data were collected from an accelerometer incorporated in the head-stage pre-amplifier of the electrophysiological setup. Since the head-stage was connected to the implant during recordings, the accelerometer captures the head movements (instantaneous accelerations).</p> <p>Each instance, representing a time point where the brain state has to be classified, contains two types of inputs:</p> <p>- A power spectrogram of the local field potential (LFP, representing local electrical actifity) around the time point to classify (+- 50 s).</p> <p>- A segment of the movement magnitude time series, extracted from an accelerometer on the mouse head.</p> <p>Each brain state is associated with a particular combination of patterns of LFP power and movement, allowing classification.</p>
Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
<p>A fundamental neuroscience topic is the link between the brain's molecular, cellular and cytoarchitectonic properties and structural connectivity (SC). Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the Matrix Inversion and Subset Selection (MISS) algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell type densities, demonstrating surprisingly strong prediction accuracy of cell types in general and particular cells like oligodendrocytes. We found evidence of a strong distance-dependency in the cell-connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia are salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.</p>
ScienceDex guides
Understand access before you commit
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.