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ShareScore release 0.9.0
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12 results for “perineuronal nets”
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>
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>
Perineuronal nets in HVC and plasticity in male canary song
<p>Songbirds learn their vocalizations during developmental sensitive periods of song memorization and sensorimotor learning. Some seasonal songbirds, called open-ended learners, recapitulate transitions from sensorimotor learning and song crystallization on a seasonal basis during adulthood. In adult male canaries, sensorimotor learning occurs each year in autumn and leads to modifications of the syllable repertoire during successive breeding seasons. We previously showed that perineuronal nets (PNN) expression in song control nuclei decreases during this sensorimotor learning period. Here we explored the causal link between PNN expression in adult canaries and song modification by enzymatically degrading PNN in HVC, a key song control system nucleus. Three independent experiments identified limited effects of the PNN degradation in HVC on the song structure of male canaries. They clearly establish that presence of PNN in HVC is not required to maintain general features of crystallized song. Some suggestion was collected that PNN are implicated in the stability of song repertoires but this evidence is too preliminary to draw firm conclusions and additional investigations should consider producing PNN degradations at specified time points of the seasonal cycle. It also remains possible that once song has been crystallized at the beginning of the first breeding season, PNN no longer play a key role in determining song structure; this could be tested by treatments with chondroitinase ABC at key steps in ontogeny. It would in this context be important to develop multiple stereotaxic procedures allowing the simultaneous bilateral degradation of PNN in several song control nuclei for extended periods.</p>
Perineuronal nets in HVC and plasticity in male canary song
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Data from: Timing of perineuronal nets development in the zebra finch song control system correlates with developmental song learning
The appearance of perineuronal nets (PNN) represents one of the mechanisms that contribute to the closing of sensitive periods for neural plasticity. This relationship has mostly been studied in the ocular dominance model in rodents. Previous studies also indicated that PNN might control neural plasticity in the song control system (SCS) of songbirds. To further elucidate this relationship, we quantified PNN expression and their localization around parvalbumin interneurons at key time-points during ontogeny in both male and female zebra finches and correlated these data with the well-described development of song in this species. We also extended these analyses to the auditory system. The development of PNN during ontogeny correlated with song crystallization although the timing of PNN appearance in the four main telencephalic song control nuclei slightly varied between nuclei in agreement with the established role these nuclei play during song learning. Our data also indicate that very few PNN develop in the secondary auditory forebrain areas even in adult birds, which may allow constant adaptation to a changing acoustic environment by allowing synaptic reorganization during adulthood.
Data from: Timing of perineuronal nets development in the zebra finch song control system correlates with developmental song learning
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Primary cilia are required for the persistence of memory and stabilization of perineuronal nets
GEO Series GSE174076. Mus musculus. 25 samples. Type: Expression profiling by high throughput sequencing.
Microglia shape AgRP neuron postnatal development via regulating perineuronal net plasticity
GEO Series GSE243697. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
ProNGF Drives Localized and Cell Selective Parvalbumin Interneuron and Perineuronal Net Depletion in the Dentate Gyrus of Transgenic Mice: a microarray study in proNGF-overexpressing transgenic mouse
GEO Series GSE70757. Mus musculus. 22 samples. Type: Expression profiling by array.
Degradation of perineuronal nets in hippocampal CA2 explains the loss of social cognition memory in Alzheimer's disease
GEO Series GSE317499. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
A Multi-Rater Benchmark for Perineuronal Nets Detection and Counting in Fluorescence Microscopy Images
<p>Dataset of fluorescence microscopy images of mice brain slices stained against perineuronal nets (PNNs). The dataset is composed of two subsets: a large single-rater subset (PNN-SR) and a smaller multi-rater subset (PNN-MR).</p> <ul> <li>PNN-SR consists of 25 images having different sizes ranging from 8184×6163 to 15120×9477 pixels. Among all the images, there are roughly 34k annotated PNNs, varying from a few dozens to some thousand per image, dot-annotated by a single human rater. <br> </li> <li>PNN-MS comprises 12 microscopic images of 2000×2000 pixels representing different portions of a mouse brain, with a total of 2,532 dot-annotated PNNs. The annotation procedure has been performed by seven different raters.</li> </ul>
CSF1R inhibitor-mediated microglial depletion prevents reductions in striatal volume, extracellular matrix alterations, and perineuronal net loss in a mouse model of Huntington’s disease
GEO Series GSE136158. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
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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.