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5,448 results for “Neurons”
Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation
Open the record for dataset details and reuse information.
Dataset for training the Surrogate Model of microlaser neurons on the reduced MNIST classification task
<p>This dataset was used to train a surrogate multilayer perceptron surrogate model of microlaser neurons.</p> <p>It is in csv format. It was generated using the Yamada Model as found in </p> <p><span>Selmi F, Braive R, Beaudoin G, Sagnes I, Kuszelewicz R and Barbay S 2014 Relative Refractory Period in an Excitable Semiconductor Laser <em>Phys. Rev. Lett.</em> <strong>112</strong> 183902</span>.</p>
Phindr3D: Test Data Set 1 (primary mouse cortical neurons)
<p>3D confocal image stacks of primary cortical neurons under different treatment conditions to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP file.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p> <p> </p>
Neurothreads: development of supportive carriers for mature dopaminergic neuron differentiation and implantation
<p>Raw data for the publication:</p> <p><strong>Neurothreads: development of supportive carriers for mature dopaminergic neuron differentiation and implantation</strong></p>
Dataset from "Matthieu Delescluse and Christophe Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29."
<p>The dataset (in HDF5 format) used in Delescluse and Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29. arXiv:q-bio/0505053. See this reference for recording details. Data collected by Matthieu Delescluse. Briefly, 4 channels (data sets Channel_0,1,2,3, organized in a group called 'ExtracellularData'; extracellular recordings along the Purkinje cell layer of a young rat cerebellar cortex slice) of a linear 'Michigan' (now Neuronexus) probe and a loose cell-attached recording (data set Reference, in group 'CellAttached') from one of the Purkinje cells that is also extracellularly recorded: a 'ground truth' for spike sorting algorithms. Each group has three attributes: SamplingRate, HighPass and LowPass. The last two are the filter settings used prior to A/D conversion. These attributes have identical values for the 5 traces (2 groups): the data were sampled at 15 kHz, high-passed at 300 Hz and low-passed at 5 kHz.</p>
Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T>G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T>G) in NDD_05 proband</p>
Dataset of Social buffering switches fear to safety encoding by oxytocin recruitment of central amygdala buffer neurons
<p>Dataset of <span>Hegoburu et al., Social buffering switches fear to safety encoding by oxytocin recruitment of central amygdala buffer neurons, Nature Communications.</span></p> <p><span>ABSTRACT</span></p> <p><span>The presence of a companion can reduce fear, but the precise neural mechanisms underlying this social buffering of fear (SBF) are incompletely known. We studied SBF in male and female rats, and its encoding in the amygdala of males, that were fear-conditioned (FC) to auditory conditioned stimuli (CS). Pharmacological, opto,- and/or chemogenetic interventions showed that oxytocin (OT) signaling from hypothalamus-to-central amygdala (CeA) projections was required for acute fear reduction in the presence, and SBF retention 24h later without the companion. Single-unit recordings with optetrodes revealed fear-encoding CeA neurons (characterized by increased CS-responses after FC) were inhibited by SBF and blue light (BL) stimulation of OTergic projections. Other CeA neurons increased CS responses only after SBF exposure. Their baseline activity was enhanced by BL and exposure to the companion. SBF thus switches the CS from encoding "fear" to "safety" by OT-mediated recruitment of a distinct group of CeA "buffer neurons".</span></p> <p> </p>
Dataset Activation of Lactate Receptor HCAR1 Down-modulates Neuronal Activity in Rodent and Human Brain Tissue
<p>This dataset is related to the study: </p> <p>Briquet M, Rocher AB, Alessandri M, Rosenberg N, de Castro Abrantes H, Wellbourne-Wood J, Schmuziger C, Ginet V, Puyal J, Pralong E, Daniel RT, Offermanns S, Chatton JY. Activation of lactate receptor HCAR1 down-modulates neuronal activity in rodent and human brain tissue. J Cereb Blood Flow Metab. 2022 Mar 3:271678X221080324. doi: 10.1177/0271678X221080324. Epub ahead of print. PMID: 35240875.</p>
Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
Ultrafast imaging recordings from the axon initial segment of neocortical layer-5 pyramidal neurons.
<p>This dataset contains imaging and whole-cell electrophysiological recordings from neocortical layer-5 pyramidal neuron from brain slices of the mouse.</p> <p>Electrophysiological recordings (at 20 kHz) are from the soma. Imaging data (10 kHz) are from lines along the axon initial segment (distal>proximal) with 500 nm pixel resolution. These correspond to:</p> <ul> <li>Sodium imaging (Figures 1 and S6).</li> <li>Voltage imaging (Figures 2,4,5,S4,S7)</li> <li>Calcium imaging (Figures 3,S3,S8).</li> </ul> <p>This dataset is used in the paper available online:</p> <p>Filipis L, Blömer LA, Montnach J, De Waard M, Canepari M. Nav1.2 and BK channels interaction shapes the action potential in the axon initial segment. bioRxiv, 2022. doi: 10.1101/2022.04.12.488116.</p>
Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes
<p>This dataset contains some of the raw and preprocessed data presented in the manuscript "Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes" submitted to Nature Communications. Detailed information on each individual file is as follows: </p> <ul> <li><strong>256ch_device2_impedance_spectroscopy.csv:</strong> Impedance magnitudes presented in Fig. 2b.</li> <li><strong>rat1_impedances.csv:</strong> Impedance magnitudes belonging to Rat #1 (presented in Fig. 4c).</li> <li><strong>rat2_impedances.csv:</strong> Impedance magnitudes belonging to Rat #2 (presented in Fig. 4c).</li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_neuron.tif:</strong> Max. intensity projection image of Nissl staining shown in Fig. 4g. </li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_IBA.tif:</strong> Max. intensity projection image of IBA staining shown in Fig. 4g. </li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_GFAP.tif:</strong> Max. intensity projection image of GFAP staining shown in Fig. 4g. </li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_neuron_4x4bins.tif: </strong>z-stack-averaged and binned image of Nissl staining used in histology analysis shown in Fig. 4g.</li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_IBA_4x4bins.tif:</strong> z-stack-averaged and binned image of IBA staining used in histology analysis shown in Fig. 4g.</li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_GFAP_4x4bins.tif:</strong> z-stack-averaged and binned image of GFAP staining used in histology analysis shown in Fig. 4g.</li> <li><strong>neuron_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for Nissl staining (Fig. 4g). </li> <li><strong>gfap_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for GFAP staining (Fig. 4g). </li> <li><strong>iba_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for IBA staining (Fig. 4g). </li> <li><strong>256ch_device2_phase_spectroscopy.csv: </strong>Impedance phases presented in Supplementary Fig. 7a.</li> <li><strong>rat1_impedance_phases.csv: </strong>Impedance phases belonging to Rat #1 (presented in Supplementary Fig. 7b).</li> <li><strong>rat2_impedance_phases.csv:</strong> Impedance phases belonging to Rat #2 (presented in Supplementary Fig. 7b).</li> <li><strong>mouseLL2_impedances_magnitudes.csv:</strong> Impedance magnitudes presented in Supplementary Fig. 9a.</li> <li><strong>mouseLL2_impedances_phases.csv: </strong>Impedance phases presented in Supplementary Fig. 9b. </li> <li><strong>mouseLL2_single_unit_SNRs.csv: </strong>Single unit SNRs presented in Supplementary Fig. 9c.</li> <li><strong>mouseLL2_single_unit_lifetimes.csv: </strong>Single unit lifetimes presented in Supplementary Fig. 9d. </li> <li>Figure_5_data.mat: Data used in Figure 5 (can be imported into the corresponding Matlab script in the GitHub repository).</li> </ul> <p>The rest of the data supporting the figures is provided in the Source File and Supplementary Data files, which are available through the online version of the article. Any additional requests for information can be directed to, and will be fulfilled by, the corresponding author. </p>
DATA to support Dyrk1a function in glutamatergic neurons in mouse models of Mental Retardation Disease 7 (MRD7) and Down syndrome (or trisomy 21)
<p>Four datasets are provided here to support the function of Dyrk1a in glutamatergic neurons in mouse models of Mental Retardation Disease 7 (MRD7) and Down syndrome (or trisomy 21):</p> <p>- RNAseq data to compare hippocampal expressed genes at postnatal day 30, in the complete inactivation of Dyrk1a in glutamatergic neurons using a Dyrk1a floxed-allele and the Camk2:Cre transgene</p> <p>- data from all the figures</p> <p>-data from all the supplementary figures </p> <p>-data from the quantitative proteomic analysis made from hippocampal extract of wt, Dyrk1a heterozygote, Dp(16)1Yey and Dp(16)1Yey with only two functional copies of Dyrk1a</p> <p>Detailed information are available in the article by Brault et al 2021, deposited in Biorachiv https://doi.org/10.1101/2021.05.01.442242 </p>
Electrophysiological characterization and functionality of neurons in murine cerebral organoids.
<p>Dataset includes patch-clamp recordings aim to characterize functionality of neurons belonging to murine cerebral organoids.</p> <p>Patch-clamp recordings were performed by University of Modena and Reggio Emilia unit (PI Prof. Curia Giulia).</p> <p>Organoids were generated by University of Verona unit (PI Prof. Decimo Ilaria) and transferred to Modena for electrophysiology experiments.</p>
Two specific populations of GABAergic neurons originating from the medial and the caudal ganglionic eminences aid in proper navigation of callosal axons.
<p>Reduced motility of CC GABAergic guidepost neurons after E16.5.<em>In vitro</em> time-lapse sequences over a period of around three hours (sequential pictures taken at regular intervals) of GAD67-GFP<sup>+</sup> neuron dynamics in coronal CC slices of E14.5 (mov 1) and E16.5 (mov 2) GAD67-GFP<sup>+</sup>transgenic mice. Open arrowheads indicate the progression of neurons between sequential pictures while arrowheads highlight immobilized neurons. (A1–A6) At E14.5, the majority of the GAD67-GFP<sup>+</sup> neurons exhibit rapid movements within the white matter of the CC (open arrowheads). (B1–B6) By contrast, at E16.5, nearly all the GAD67-GFP<sup>+</sup> neurons exhibit a reduced motility within the white matter of the CC (arrowheads). </p> <p>Branching and outgrowth defects in the callosal axons of Nkx2.1<sup>−/−</sup>:GAD67-GFP mice brains. (mov 3 and mov 4) A pCAG-Ires-Tomato plasmid was injected into the lateral ventricle and electroporated into the dorsal pallium, to label the callosal projecting neurons, of E14.5 GAD67-GFP<sup>+</sup> living embryos that were allowed to develop until E16.5. 6. High power views of <em>in vitro</em> time-lapse sequences over a period of 120 min (at 20 min intervals) of Tomato-labeled callosal axons and GAD67-GFP<sup>+</sup> neurons on coronal CC slices of E16.5 Nkx2.1<sup>+/+</sup>:GAD67-GFP<sup>+</sup> (mov 3) and Nkx2.1<sup>−/−</sup>:GAD67-GFP<sup>+</sup> (mov4) embryos. In the Nkx2.1<sup>−/−</sup> brains, though the callosal axons progressed along normal path, they displayed disoriented branch extensions. </p>
Super-resolution analysis of the origins of the elementary events of ER calcium release in dorsal root ganglion neurons
<p>This is the data supplement for the paper entitled, "Super-resolution analysis of the origins of the elementary events of ER calcium release in dorsal root ganglion neurons"<br><br>There are two principal subdirectories within the enclosed zip file:</p><ol><li>10xEExM_data: The directory contains two exemplar datasets each of 10x Enhanced expansion microscopy images of IP3R1 and RyR immunolabelling in DRG soma, at the sub-plasmalemmal regions.<br> </li><li>Correlative Analysis: The directory contains two sub-directories of worked examples of data and correlative analysis of calcium sparks and dSTORM images of RyR and IP3R. The instructions for the code, run in IDL, are included in the Readme.txt enclosed within.</li></ol>
BK Channels activation by N-type Ca2+ channels in the dendrites of neocortical pyramidal neurons
<p>This dataset contains imaging and whole-cell electrophysiological recordings from neocortical layer-5 pyramidal neuron dendrites in brain slices of the mouse.</p><p>Somatic electrophysiological and dendritic imaging recordings were done at 20 kHz. Imaging recordings were done with ~2.5 µm nm pixel resolution. These correspond to:</p><ul><li>Voltage imaging (Figures 1 and 7)</li><li>Calcium imaging (Figures 2,3 and 4).</li></ul><p>This dataset is used in the paper:</p><p>Blömer LA, Giacalone E, Abbas F, Filipis L, Migliore M, Canepari M. Kinetics and functional consequences of BK Channels activation by N-type Ca2+ channels in the dendrite of mouse neocortical layer-5 pyramidal neurons. bioRxiv, 2023 (https://www.biorxiv.org/content/10.1101/2023.10.26.564136v1).</p>
Meshing of Spiny Neuronal Morphologies using Union Operators
<p>Resulting datasets of the conference paper "<em>Meshing of Spiny Neuronal Morphologies using Union Operators</em>". The paper is published in 2022 in EG UK Computer Graphics & Visual Computing (2022).</p> <p><a href="https://doi.org/10.2312/cgvc.20221168">DOI: 10.2312/cgvc.20221168</a></p> <p>The dataset consists of the resulting meshes of a set of exemplar neurons created using the union-operator-based meshing algorithm that is described in the paper. </p>
PhasAGE Expert Seminar- Dissecting the contribution of motor-cargo adaptors to microtubule-based transport in neurons
<p>The PhasAGE Expert Seminars consist of a series of talks with speakers from PhasAGE partner’s institutions to promote a successful transfer of knowledge about PhasAGE topics – biomolecular phase separation, aging and age-related diseases.</p>
Dataset for the manuscript "ExSTED microscopy reveals contrasting functions of dopamine and somatostatin CSF-c neurons along the central canal"
<p>Description: Dataset that supports the expansion-STED and light sheet microscopy methods in spinal cord and support the findings in the manuscript: ExSTED microscopy reveals contrasting functions of dopamine and somatostatin CSF-c neurons along the central canal (Elham Jalalvand, Jonatan Alvelid, Giovanna Coceano, Steven Edwards, Brita Robertson, Sten Grillner, Ilaria Testa).</p> <p>The software used to open the files and perform the analysis: Imspector v0.10_rev8575 and ImageJ 1.52i.</p> <p>The preprint of the manuscript can be found here: https://doi.org/10.1101/2021.08.17.456595</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>
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.