Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
109
datasets available to search
ShareScore release 0.7.1
Dataset results
109 results for “neocortex”
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>
Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms
<p>RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders, yet the role of cell-type-specific splicing or transcript-isoform diversity during human brain development has not been systematically investigated. Here, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone (GZ) and cortical plate (CP) regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 unique isoforms, of which 72.6% are novel (unannotated in Gencode-v33), and uncovered a substantial contribution of transcript-isoform diversity, regulated by RNA binding proteins, in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to re-prioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.</p>
Enhanced atlases and flatmaps of rodent neocortex
<p><strong>Flatmaps of mouse isocortex and barrel column annotations in CCFv3 space (10 µm resolution)</strong></p> <ul> <li><strong>annotation_barrels.nrrd:</strong> Pre-generated annotations of barrel columns divided into layers, can be transplanted directly into the CCFv3 atlas.</li> <li><strong>barrel_positions.feather:</strong> 3D coordinates of segmented barrel voxels used to create annotations.</li> <li><strong>central-streamlines.feather:</strong> Central streamlines used to calculate barrel cortex metrics.</li> <li><strong>depth.nrrd:</strong> Streamline-derived absolute cortical depth in µm.</li> <li><strong>flatmap_authalic_shaped_256.nrrd: </strong>Discretized flatmap for use in flatmap comparison notebook (256x256 pixel resolution, shape-match border).<strong><br></strong></li> <li><strong>flatmap_authalic_square_256.nrrd:</strong> Discretized flatmap for use in flatmap comparison notebook (256x256 pixel resolution, square border).</li> <li><strong>flatmap_both_shaped.nrrd: </strong>Flatmap of mouse isocortex in both hemispheres (shape-match border).</li> <li><strong>flatmap_both_square.nrrd:</strong> Flatmap of mouse isocortex in both hemispheres (square border).</li> <li><strong>flatmap_shaped.nrrd:</strong> Flatmap of mouse isocortex in a single hemisphere (shape-match border).</li> <li><strong>flatmap_square.nrrd:</strong> Flatmap of mouse isocortex in a single hemisphere (square border).</li> <li><strong>hexgrid.nrrd:</strong> Decomposition of mouse isocortex into 1307 hexagonal columns of roughly equal size.</li> <li><strong>hierarchy.json:</strong> Region hierarchy in AIBS format extended with barrel annotations.</li> <li><strong>isocortex.nrrd:</strong> Annotations of mouse isocortex regions in a single hemisphere (1-based, consecutive) for plotting in ITK-SNAP.</li> <li><strong>isocortex.label:</strong> Accompanying label file for plotting <strong>isocortex.nrrd</strong> in ITK-SNAP.</li> <li><strong>thickness.nrrd:</strong> Streamline-derived cortical thickness in µm.</li> <li><strong>flatmap_input/Makefile:</strong> Pipeline to derive inputs for the flatmapping algorithm from CCFv3 datasets.</li> <li><strong>flatmap_input/border_points.txt</strong><strong>:</strong> Border points for the shape-match flatmap, a convex approximation to the shape of the CCFv3 flatmap.</li> <li><strong>flatmap_input/config_shaped.mk:</strong> Configuration file for the flatmapping algorithm (shape-match border).</li> <li><strong>flatmap_input/config_square.mk:</strong> Configuration file for the flatmapping algorithm (square border).</li> </ul> <p> </p> <p><strong>Enhanced atlas and flatmap of P14 rat somatosensory cortex</strong></p> <ul> <li><strong>brain_regions.nrrd:</strong> Annotated regions in rat somatosensory cortex, divided into layers.</li> <li><strong>flatmap.nrrd:</strong> Flatmap of rat somatosensory cortex in a single hemisphere (square border).</li> <li><strong>hexgrid.nrrd:</strong> Decomposition of rat somatosensory cortex into 263 hexagonal columns of roughly equal size.</li> <li><strong>hierarchy.json:</strong> Region hierarchy in AIBS format.</li> <li><strong>orientation.nrrd:</strong> Local orientation towards pia, stored as a quaternion.</li> <li><strong>relative_depth.nrrd:</strong> Relative cortical depth in the range [0,1].</li> <li><strong>sscx.nrrd:</strong> Annotations of rat somatosensory cortex regions in a single hemisphere (1-based, consecutive) for plotting in ITK-SNAP.</li> <li><strong>sscx.label: </strong>Accompanying label file for plotting <strong>sscx.nrrd</strong> in ITK-SNAP.</li> <li><strong>thickness.nrrd:</strong> Cortical thickness in µm.</li> <li><strong>flatmap_input/config.mk:</strong> Configuration file for the flatmapping algorithm.</li> <li><strong>flatmap_input/annotations.nrrd:</strong> Annotations of rat somatosensory cortex regions (0-based, consecutive).</li> <li><strong>flatmap_input/mask.nrrd:</strong> Labeling of source volume into interior, exterior, top/bottom/sides boundaries.</li> <li><strong>flatmap_input/orientation_{x,y,z}.nrrd:</strong> Components of the orientation vector, as input to the flatmapping algorithm.</li> <li><strong>flatmap_input/relative_depth.nrrd:</strong> Relative depth field, as input to the flatmapping algorithm.</li> </ul> <p> </p> <p>Code to generate flatmaps and barrel cortex annotations can be found in a <a href="https://github.com/BlueBrain/atlas-enhancement" target="_blank" rel="noopener">software repository</a> under a free software license.</p>
VERTEX 2.0 Rat Neocortex Simulation Results
<p>The results of simulations of electric field stimulation in rat neocortex.</p> <p>Three stimulation paradigms have been used, a single pulse, a paired pulse, and theta burst stimulation.</p> <p>The VERTEX simulator code that generated these results is available at https://github.com/haeste/Vertex_2.</p>
Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms
Open the record for dataset details and reuse information.
Data for "Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas"
<p>Data for "Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas". Preprint: https://www.biorxiv.org/content/10.1101/507293v2.</p> <p>Raw sequencing data is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134378.</p>
Data for: Inhibition is a prevalent mode of activity in the neocortex around awake hippocampal ripples in mice
<p>Coordinated peri-ripple activity in the hippocampal-neocortical network is essential for mnemonic information processing in the brain. Hippocampal ripples likely serve different functions in sleep and awake states. Thus, the corresponding neocortical activity patterns may differ in important ways. We addressed this possibility by conducting voltage and glutamate wide-field imaging of the neocortex with concurrent hippocampal electrophysiology in awake mice. Contrary to our previously published sleep results, deactivation and activation were dominant in post-ripple neocortical voltage and glutamate activity, respectively, especially in the agranular retrosplenial cortex (aRSC). Additionally, the spiking activity of aRSC neurons, estimated by two-photon calcium imaging, revealed the existence of two subpopulations of excitatory neurons with opposite peri-ripple modulation patterns: one increases and the other decreases firing rate. These differences in peri-ripple spatiotemporal patterns of neocortical activity in sleep versus awake states might underlie the reported differences in the function of sleep versus awake ripples.</p>
Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex
<p>Rodent studies have demonstrated that synaptic dynamics from excitatory to inhibitory neuron types are often dependent on the target cell type. However, these target cell-specific properties have not been well investigated in human cortex, where there are major technical challenges in reliably obtaining healthy tissue, conducting multiple patch-clamp recordings on inhibitory cell types, and identifying those cell types. Here, we take advantage of newly developed methods for human neurosurgical tissue analysis with multiple patch-clamp recordings, <em>post-hoc</em> fluorescent <em>in situ</em> hybridization (FISH), machine learning-based cell type classification, and prospective GABAergic AAV-based labeling to investigate synaptic properties between pyramidal neurons and PVALB- vs. SST-positive interneurons. We find that there are robust molecular differences in synapse-associated genes between these neuron types, and that individual presynaptic pyramidal neurons evoke postsynaptic responses with heterogeneous synaptic dynamics in different postsynaptic cell types. Using molecular identification with FISH and classifiers based on transcriptomically identified PVALB neurons analyzed by Patch-seq, we find that PVALB neurons typically show depressing synaptic characteristics, whereas other interneuron types including SST-positive neurons show facilitating characteristics. Together, these data support the existence of target cell-specific synaptic properties in human cortex that are similar to rodents, thereby indicating evolutionary conservation of local circuit connectivity motifs from excitatory to inhibitory neurons and their synaptic dynamics.</p>
Data from: Molecular and cellular dynamics of the developing human neocortex
Open the record for dataset details and reuse information.
Data for: Inhibition is a prevalent mode of activity in the neocortex around awake hippocampal ripples in mice
Open the record for dataset details and reuse information.
Data from: Combinatorial expression of gamma-protocadherins regulates synaptic connectivity in the mouse neocortex
Open the record for dataset details and reuse information.
Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex
Open the record for dataset details and reuse information.
A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex
<p>This dataset contains all (>1000) cell pairs as the Blue Brain Projects <a href="https://github.com/BlueBrain/EModelRunner">EModelRunner</a> packages as well as analysis code and analysed data used for the figures of our <a href="https://www.biorxiv.org/content/biorxiv/early/2020/04/20/2020.04.19.043117.full.pdf">preprint</a>: <em><strong>"A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex"</strong></em>.</p> <p>More documentation will follow in the upcoming days.</p> <p><strong>Updates:</strong><br> v1.1 (31/12/2021): updated READMEs within cell_packages, added 2 extra authors for their contribution in EModelRunner.<br> v1.2 (31/12/2021): same as v1.1 but w/o MacOS junk<br> v1.3 (11/01/2022): added analysis notebooks<br> v2.0 (11/01/2022): same as v1.3 but w/o MacOS junk and proper version number<br> v2.1 (14/03/2022): fetching data from websites when possible instead of providing the downloaded csv files.</p>
Dataset for "DSCAM gene triplication causes excessive GABAergic synapses in the neocortex in Down syndrome mouse models"
<p>Dataset for manuscript entitled "DSCAM gene triplication causes excessive GABAergic synapses in the neocortex in Down syndrome mouse models"</p>
Data from: High bandwidth synaptic communication and frequency tracking in human neocortex
Open the record for dataset details and reuse information.
Knockdown of the schizophrenia susceptibility gene TCF4 alters gene expression and proliferation of progenitor cells from the developing human neocortex.
GEO Series GSE62085. Homo sapiens. 12 samples. Type: Expression profiling by array.
Single cell analysis of long non-coding RNAs in the developing human neocortex
GEO Series GSE71315. Homo sapiens. 242 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Human fetal neocortex polysome fractions over development
GEO Series GSE214272. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing.
Epigenome profiling and editing of neural progenitor cells in the developing mouse neocortex [RNA-seq]
GEO Series GSE90447. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
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.