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990 results for “Hippocampus”
Quantification of RNAseq and CUT&RUN from MeCP2 adult knockout hippocampus
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Increasing stimulus similarity drives nonmonotonic representational change in hippocampus
<p>Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.</p>
3d virtual histology of the human hippocampus based on phase-contrast computed-tomography
<p>This data package contains:<br> - exemplary data sets of multiscale imaging of the human hippocampus as raw-files (gray values, segmentation masks)<br> - object properties as xlsx-files (for all data sets)<br> - analysis scripts (based on matlab, R, python)</p>
Hippocampus barbouri decontaminated FSCR
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Hippocampus guttulatus decontaminated gx
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Hippocampus barbouri decontaminated gx
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FIGURE 38. Hippocampus taeniopterus Bleeker 1852 in Checklist of the marine and estuarine fishes of New Ireland Province, Papua New Guinea, western Pacific Ocean, with 810 new records
FIGURE 38. Hippocampus taeniopterus Bleeker 1852, Lissenung Island, Kavieng District, 16 May 2009 (underwater photograph: Dietmar Amon).
FIGURE 37. Hippocampus bargibanti Whitley 1970 in Checklist of the marine and estuarine fishes of New Ireland Province, Papua New Guinea, western Pacific Ocean, with 810 new records
FIGURE 37. Hippocampus bargibanti Whitley 1970, Lissenung Island, Kavieng District, 23 Apr. 2010 (underwater photograph: Dietmar Amon).
Data for Evaluation of altered cell-cell communication between glia and neurons in the hippocampus of 3xTg-AD mice at two time points
<p>processed_data.tar.gz contains all files from the data directory associated with the 230418_TS_AgingCCC GitHub project and includes the following:</p> <ul> <li> <p>CellRangerCounts/</p> </li> <ul> <li> <p>post_soupX/ : contains 12 directories for 12 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but the post_soupX directory contains one directory for each of the 12 samples:</p> </li> <ul> <li> <p>S01_6m_AD/</p> </li> <ul> <li> <p>barcodes.tsv</p> </li> <li> <p>genes.tsv</p> </li> <li> <p>matrix.mtx</p> </li> </ul> </ul> <li> <p>pre_soupX/ : contains 12 directories for 12 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this directory contains 1 directory for each individual sample:</p> </li> <ul> <li> <p>S01_6m_AD/outs/</p> </li> <ul> <li> <p>filtered_feature_ bc_matrix.h5</p> </li> <li> <p>raw_feature_bc_matrix.h5 </p> </li> </ul> </ul> </ul> <li> <p>PANDA_inputs/</p> </li> <ul> <li> <p>PANDA_exp_files_array.txt: Text files with files paths to expression inputs for PANDA gene regulatory networks.</p> </li> <li> <p>mm10_TFmotifs.txt: Mouse TF motif input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> <li> <p>mm10_ppi.txt: Mouse protein-protein interaction information from SringDB input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> </ul> <li> <p>ccc/</p> </li> <ul> <li> <p>nichenet_v2_prior/</p> </li> <ul> <li> <p>gr_network_mouse_21122021.rds : accessed in December 2023, gene regulation network – gene regulatory information from MultiNicheNet</p> </li> <li> <p>ligand_target_matrix_nsga2r_final_mouse.rds: accessed in December 2023, ligand target matrix for mouse from MultiNicheNet.</p> </li> <li> <p>ligand_tf_matrix_nsga2r_final_mouse.rds: accessed in December 2023, mouse ligand tf matrix for signaling path determination from MultiNicheNet</p> </li> <li> <p>lr_network_mouse_21122021.rds : accessed in December 2023, ligand-receptor matrix from MultiNicheNet</p> </li> <li> <p>signaling_network_mouse_21122021.rds : accessed in December 2023, signaling network – protein-protein interaction information from MultiNicheNet for mouse</p> </li> <li> <p>weighted_networks_nsga2r_final_mouse.rds : accessed in October 2023, networks weighted by literature evidence from MultiNicheNet for mouse</p> </li> </ul> <li> <p>multinichenet_output.rds : MultiNicheNet output for 3xTg-AD snRNA-seq data</p> </li> <li> <p>12m_signaling_igraph_objects.rds : list of igraph objects for 93 LRTs and their signaling mediators at 12 months</p> </li> <li> <p>6m_signaling_igraph_objects.rds :list of igraph objects for 2 LRTs and their signaling mediators at 6 months</p> </li> </ul> <li> <p>elisa/: CSV files of measured OD values for every ELISA.</p> </li> <ul> <li> <p>240319_ELISA_Ab40.csv: OD measurements for Ab40</p> </li> <li> <p>240319_ELISA_Ab42.csv: OD measurements for Ab42</p> </li> <li> <p>240319_ELISA_total_tau.csv: OD measurements for Total Tau</p> </li> </ul> <li> <p>panda/: PANDA gene regulatory networks for each time point and condition in excitatory and inhibitory neurons. Used for differential gene targeting.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>pseudobulk/: includes pseudo bulk matrices for every cell type which were used for downstream analyses. Each matrix also includes metadata information on condition and time point.</p> </li> <ul> <li> <p>all_counts_ls.rds: List of all the pseudo bulk matrices (below).</p> </li> <li> <p>astrocytes.rds: pseudobulk matrix for astrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>endothelial_cells.rds: pseudobulk matrix for endothelial cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>ependymal_cells.rds: pseudobulk matrix for ependymal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>excitatory_neurons.rds: pseudobulk matrix for excitatory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>fibroblasts.rds: pseudobulk matrix for fibroblasts. Include time point and condition information for downstream analyses.</p> </li> <li> <p>inhibitory_neurons.rds: pseudobulk matrix for inhibitory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>meningeal_cells.rds: pseudobulk matrix for meningeal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>microglia.rds: pseudobulk matrix for microglia. Include time point and condition information for downstream analyses.</p> </li> <li> <p>oligodendrocytes.rds: pseudobulk matrix for oligodendrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>opcs.rds: pseudobulk matrix for oligodendrocyte progenitor cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>percicytes.rds: pseudobulk matrix for pericytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>rgcs.rds: pseudobulk matrix for retinal ganglion cells. Include time point and condition information for downstream analyses.</p> </li> </ul> <li> <p>pseudobulk_split/: Includes pseudo bulk count matrices split by time point and condition. Used for input to PANDA for gene regulatory network construction.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>seurat_preprocessing/</p> </li> <ul> <li> <p>filtered_seurat.rds : merged and filtered seurat object</p> </li> <li> <p>integrated_seurat.rds : seurat object integrated using harmony</p> </li> <li> <p>clustered_seurat.rds : clustered seurat object</p> </li> <li> <p>processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution</p> </li> </ul> </ul> <p> </p> <p>Raw data publicly available on GEO under series accession: GSE261596</p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - EE dataset
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that were housed in an enriched environment (EE). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were female. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - Irr+EE
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that were irradiated to ablate adult neurogenesis and housed in an enriched environment (Irr+EE). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were female. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - Irr+RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that werethat were irradiated to ablate adult neurogenesis and housed in a regular cage (Irr+RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were female. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - RetroAAV-EE
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from retro-AAV injected mice that were housed in an enriched environment (EE). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice in this dataset were male. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that were housed in a regular cage (RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>Refer to the original publication for additional information. The sex of individual mice is as follows:</p> <table> <tbody> <tr> <td><strong>Group</strong></td> <td><strong>Mouse #</strong></td> <td><strong>Sex</strong></td> </tr> <tr> <td>RC</td> <td>M1</td> <td>M</td> </tr> <tr> <td> </td> <td>M2</td> <td>M</td> </tr> <tr> <td> </td> <td>M3</td> <td>M</td> </tr> <tr> <td> </td> <td>M4</td> <td>F</td> </tr> <tr> <td> </td> <td>M5</td> <td>F</td> </tr> </tbody> </table>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - RetroAAV-RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from retro-AAV injected mice that were housed in a regular cage (RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were male. Refer to the original publication for additional information. </p>
Supplementary material 1 from: Short G, Smith R, Motomura H, Harasti D, Hamilton H (2018) Hippocampus japapigu, a new species of pygmy seahorse from Japan, with a redescription of H. pontohi (Teleostei, Syngnathidae). ZooKeys 779: 27-49. https://doi.org/10.3897/zookeys.779.24799
Genetic distance analysis (uncorrected p distances) of COI sequence data from 21 specimens of H.pontohi and those referred to H.severnsi :
Dataset of AD and normal aging in the hippocampus
<p>Alzheimer's disease (AD) is the most common type of dementia in the elderly, characterized by irreversible degenerative memory, language, learning, and other cognitive impairments. It is estimated that nearly 500,000 new cases of AD, which is the fifth-leading cause of death for those 65 and older in the United States in 2016. AD brings immense suffering and heavy financial burdens. However, few therapeutic strategies are available to prevent or cure AD due to complex pathogenesis. Due to the high morbidity and mortality of AD, revealing its causes and potential pathogenesis, identifying molecular biomarkers for the early diagnosis, prevention, and treatment are a vital event. However, existing studies have shown that Tau, amyloid-β (Aβ) and APOE play important roles in the pathogenesis of AD, but the above-mentioned aspects as a targeted treatment have not achieved good results. It is imperative in the search for hub gene and pathways in AD. Thus, we extracted mRNA expression data with AD and normal aging in the same tissue and used an integrative network-based method for combining transcriptomic and protein-protein interaction data to find <a>differential</a>ly <a>expressed</a> <a>genes</a> (DEGs) and pathways that may reflect key biological processes in AD.</p>
FIGURE 4 in Three new pygmy seahorse species from Indonesia (Teleostei: Syngnathidae: Hippocampus)
FIGURE 4. Live specimens of new species of pygmy seahorses from Indonesia. A) Hippocampus pontohi: Bunaken, Sulawesi, M. Boyer; Bunaken, Sulawesi, M. Aw; Raja Ampat, West Papua, L. Tackett. B) Hippocampus severnsi: Bunaken, Sulawesi, S. Wong & T. Uno; Bunaken, Sulawesi, M. Severns (type specimens); Raja Ampat, West Papua, L. Tackett. C) Hippocampus satomiae: Derawan Kalimantan, S. Wong & T. Uno; Derawan, Kalimantan, J–S. Chen; Derawan, Indonesia, S. Onishi (type specimen).
FIGURE 2 in Three new pygmy seahorse species from Indonesia (Teleostei: Syngnathidae: Hippocampus)
FIGURE 2. Radiographs of holotype specimens: A) Hippocampus pontohi (MZB 13593, 16.9 mm), B) Hippocampus severnsi (MZB 13594, 16.6 mm), and C) Hippocampus satomiae (NMV A25420–001, 13.8 mm). Scale bar = 2 mm in each case.
FIGURE 5. Distribution records for A in Three new pygmy seahorse species from Indonesia (Teleostei: Syngnathidae: Hippocampus)
FIGURE 5. Distribution records for A) Hippocampus pontohi, B) Hippocampus severnsi, C) Hippocampus satomiae.
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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.