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285 results for “cortical neurons”
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>
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>
Data set for "Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices"
<p>Data set for: Liu Y, Bech P, Tamura K, Délez LT, Crochet S, Petersen CCH (2024) Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices. eLife 13: RP97602. https://doi.org/10.7554/eLife.97602</p> <p>There are 3 files in this upload:</p> <p>1. The file named "2024_Liu_eLife.pdf" is the Open Access pdf of the online publication in eLife.</p> <p>2. The file named "Liu_anatomy_data_code.zip" (~35 GB) is a zipped version of a folder "Liu_anatomy_data_code" (~111 GB), which contains the anatomical data analysed in the study along with the Python codes used to generate the published figures 1-7 and their associated figure supplements. </p> <p>3. The file named "Liu_function_data_code.zip" (~10 GB) is a zipped version of a folder "Liu_function_data_code" (~35 GB), which contains the functional data analysed in the study along with the Python codes used to generate the published figure 8 and its associated figure supplement. </p> <p>After unzipping, the Python codes should run as a Jupyter notebook (anatomy .ipynb code) or Python code (function .py code) in Anaconda.</p>
Characterization of a loss-of-function NAPB mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected iPSC lines. Author names and affiliations
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from induced pluripoent stem cells (iPSC). There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz) and reverse read (R2_001.fastq.gz).</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01: Proband sample</p> <p>NDD_04: Proband sample</p> <p>NDD_05: Proband sample</p>
Computational analysis of cortical neuronal excitotoxicity in a large animal model of neonatal brain injury
<p>This is the dataset accompanying the manuscript:</p> <p><strong>"Computational Analysis of Cortical Neuronal Excitotoxicity in a Large Animal Model of Neonatal Brain Injury"</strong></p> <p>Panagiotis Kratimenos<sup>1,2,5 </sup>*, Abhya Vij<sup>5</sup>, Robinson Vidva<sup>6</sup>, Ioannis Koutroulis<sup>3,4,5</sup>, Maria Delivoria-Papadopoulos<sup>7</sup>**, Vittorio Gallo<sup>1,5</sup>, and Aaron Sathyanesan<sup>1,5</sup>*</p> <p><em><sup>1</sup></em><em>Center for Neuroscience Research, Children’s National Research Institute, Children’s National Hospital, Washington DC, USA</em></p> <p><em><sup>2</sup></em><em>Department of Pediatrics, Division of Neonatology, Children’s National Hospital, Washington DC, USA</em></p> <p><em><sup>3</sup></em><em>Department of Pediatrics, Division of Emergency Medicine, Children’s National Hospital, Washington, DC, USA</em></p> <p><em><sup>4</sup></em><em>Center for Genetic Medicine Research, Children’s National Research Institute and Department of Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA</em></p> <p><em><sup>5</sup></em><em>George Washington University School of Medicine and Health Sciences, Washington DC, USA</em></p> <p><em><sup>6</sup></em><em>Digirobi Solutions, Bengaluru, Karnataka, India</em></p> <p><em><sup>7</sup></em><em>Department of Pediatrics, Drexel University College of Medicine, Philadelphia, PA, USA</em></p> <p>*Corresponding Authors:</p> <p>Panagiotis Kratimenos, MD, PhD: <a href="mailto:panagiotis.kratimenos@childrensnational.org">panagiotis.kratimenos@childrensnational.org</a></p> <p>Aaron Sathyanesan, PhD: <a href="mailto:asathyanesan@childrensnational.org">asathyanesan@childrensnational.org</a></p> <p>111 Michigan Avenue, Washington, DC, 20010, USA</p>
Molecular and electrophysiological features of GABAergic neurons in the dentate gyrus reveal limited homology with cortical interneurons
<p>GABAergic interneurons tend to diversify into similar classes across telencephalic regions. However, it remains unclear whether the electrophysiological and molecular properties commonly used to define these classes are discriminant in the hilus of the dentate gyrus. Here, using patch-clamp combined with single cell RT-PCR, we compare the relevance of commonly used electrophysiological and molecular features for the clustering of GABAergic interneurons sampled from the mouse hilus and primary sensory cortex. While unsupervised clustering groups cortical interneurons into well-established classes, it fails to provide a convincing partition of hilar interneurons. Statistical analysis based on resampling indicates that hilar and cortical GABAergic interneurons share limited homology. While our results do not invalidate the use of classical molecular marker in the hilus, they indicate that classes of hilar interneurons defined by the expression of molecular markers do not exhibit strongly discriminating electrophysiological properties.</p>
Dynamic structure of motor cortical neuron co-activity carries behaviorally relevant information
<p>(This is the dataset used in <a href="https://doi.org/10.1101/2022.05.18.492501" rel="noopener" title="Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information">Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information</a>, Abstract below)</p> <p><span>Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of co-activity between neurons. Using pairwise spike time statistics, co-activity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in non-human primates are behaviorally specific: low dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the instruction cue consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment.</span></p>
Molecular and electrophysiological features of GABAergic neurons in the dentate gyrus reveal limited homology with cortical interneurons
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Dynamic structure of motor cortical neuron co-activity carries behaviorally relevant information
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Cortical somatostatin long-range projection neurons and interneurons exhibit divergent developmental trajectories
<p>Spatial transcriptomic data with 94 gene panels on P5 mouse neocortex. Files here include raw tiff files, results/spot counts, DAPI files for ROI segmentation, ROI segmented, final measurement with annotated region and counts. </p>
Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans
<h3>Contact:</h3><h3>Ilya Verzhbinsky</h3><h3>ilya@health.ucsd.edu</h3><p> </p><p>This is the processed data used to generate the results in the manuscript:</p><p>Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans. <i>PNAS</i> (2023).</p><p>To analyze this data, please first access the code at the following repository: <a href="https://github.com/iverzh/coripple-prediction"><strong>https://github.com/iverzh/coripple-prediction</strong></a></p><p>All downloaded zip files should be uncompressed and placed in a directory named <i>out/ </i>in the <i>CoRipplePredictionPNAS/</i> folder.</p><p> </p><p> </p><p> </p>
Data from: Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture
<p>Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.</p>
Source data for Chen et al (2024) entitled "Motor Cortical Neuronal Hyperexcitability Associated with α-Synuclein Aggregation"
<div> </div> <div>--------------------</div> <div>GENERAL INFORMATION </div> <div>--------------------</div> <div>This readme file was generated on [2024-01-15] by [Liqiang Chen].</div> <div> </div> <div>Title of Dataset:</div> <div>Description of Dataset: </div> <div>Principal Investigator: Hong-Yuan Chu, hc948@georgetown.edu, ORCID: 0000-0003-0923-683X. </div> <div>Date of Data Collection: 2023-04-01 to 2024-11-10 </div> <div>Software Dependencies: Excel and Image J.</div> <div> </div> <div>-------------</div> <div>FILE OVERVIEW </div> <div>-------------</div> <div>Directory of Files: Source data, Electrophysiology trace data, and Microscopy images.</div> <div>Relationship Between Files: Source data is used to make figures in GraphPad. Electrophysiology trace data is used to plot electrophysiology traces. Microscopy images are used for representative images. </div> <div>File Formats: Microsoft Excel Worksheet (.xlsx) and confocal images (.nd2)</div> <div>File Naming Convention: Based on file formats.</div> <div> </div> <div>----------------------------------------</div> <div>DATA SPECIFIC INFORMATION FOR [Source data]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Source data is used to make figures in GraphPad.</div> <div>A. Missing data are represented n/a.</div> <div>B. Abbreviations (Primary cortex: M1; Secondary cortex: M2; α-Synulein: αSyn; intratelencephalic neurons: ITNs; corticospinal neurons: CSNs).</div> <div>C. Figure 5B data (ITN-Sholl analysis-Intersections-5 µm Radius) can not organized as tidy format because of too many data points in each group. </div> <div> </div> <div>DATA SPECIFIC INFORMATION FOR [Electrophysiology trace data]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Electrophysiology trace data is used to plot electrophysiology traces.</div> <div>A. Electrophysiology traces can be plotted using Excel.</div> <div>B. Traces are plotted in Electrophysiology trace data file.</div> <div> </div> <div>DATA SPECIFIC INFORMATION FOR [Microscopy images]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Microscopy images are used to make representative images. </div> <div>A. Microscopy images can be opened using Image J. </div> <div>B. Microscopy images are named based on experimental group, animal ID, and figure number in the manuscript. </div> <div> </div> <div> </div> <div>-----------</div> <div>METHODOLOGY</div> <div>-----------</div> <div>Description of methods used for data collection: Electrophysiology data is collected using MultiClamp 700B amplifier and Digidata 1550B. pClamp 11 software is used. Microscopy images are collected using an confocal microscope. </div> <div> </div> <div>Description of methods used for data processing: </div> <div>A. Electrophysiology data is processed using clampfit software, including measure the peak of EPSC, count the number of action potentials, measure the width/rise time/decay time of action potential.</div> <div>B. Microscopy images are processed using Image J software, including measure the α-Synulein pathologic area in motor cortex, quantify the TH staining, and verify the co-localization of pS129 and biocytin. </div> <div>C. All data after processed through clampfit and Image J is put into GraphPad to make figures.</div> <div> </div> <div>-----------------------</div> <div>DATA ACCESS AND SHARING</div> <div>-----------------------</div> <div>This research was funded in part by </div> <div>1. Aligning Science Across Parkinson’s (ASAP-020572) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF).</div> <div>2. National Institute of Neurological Disorders and Stroke (R01NS121374).</div> <div>3. Congressionally Directed Medical Research Programs (W81XWH-21-1-0943).</div> <p> </p>
Parkinson's disease-associated, sex-specific changes in DNA methylation at PARK7 (DJ-1), ATXN1, SLC17A6, NR4A2, and PTPRN2 in cortical neurons
<p>Evidence for epigenetic regulation playing a role in Parkinson's disease (PD) is growing, particularly for DNA methylation. Approximately 90% of PD cases are due to a complex interaction between age, genes, and environmental factors, and epigenetic marks are thought to mediate the relationship between aging, genetics, the environment, and disease risk. To date, there are a small number of published genome-wide studies of DNA methylation in PD, but none accounted for cell-type or sex in their analyses. Given the heterogeneity of bulk brain tissue samples and known sex differences in PD risk, progression, and severity, these are critical variables to account for. In this first genome-wide analysis of DNA methylation in an enriched neuronal population from PD post-mortem parietal cortex, we report sex-specific PD-associated methylation changes in <em>PARK7</em> (DJ-1), <em>SLC17A6</em> (VGLUT2), <em>PTPRN2</em> (IA-2β), <em>NR4A2</em> (NURR1), and other genes involved in developmental pathways, neurotransmitter packaging and release, and axon and neuron projection guidance.</p>
Responses to axonal current injection in cortical layer 5 pyramidal neurons
<p>Patch-clamp recordings in cortical layer 5 pyramidal neurons performed by Wenqin Hu. A current pulse is either injected in the soma or in an axonal bleb and recorded simultaneously in the axonal bleb or the soma, respectively.</p> <p>The data are related to figure 7 of the paper: Hu, W., & Bean, B. P. (2018). Differential control of axonal and somatic resting potential by voltage-dependent conductances in cortical layer 5 pyramidal neurons. <em>Neuron</em>, <em>97</em>(6), 1315-1326.</p> <p> </p>
Data and code for: Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons
<p>Data and accompanying analysis code to generate main figures from "Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons" in PNAS.</p> <p> </p> <p> </p>
Chronic Ca2+ imaging of cortical neurons with long-term expression of GCaMP-X
<p><span>Dynamic Ca<sup>2+</sup> signals reflect acute changes in membrane excitability and also mediate signaling cascades in chronic processes. In both cases, chronic </span><span>Ca<sup>2+</sup></span><span> imaging is often desired but challenged by the cytotoxicity intrinsic to calmodulin (CaM)-based GCaMP, a series of genetically-encoded </span><span>Ca<sup>2+</sup></span><span> indicators that have been widely applied. Here, we demonstrate the performance of GCaMP-X in chronic </span><span>Ca<sup>2+</sup></span><span> imaging of cortical neurons, where GCaMP-X by design is to eliminate the unwanted interactions between the conventional GCaMP and endogenous (apo)CaM-binding proteins. By expressing in adult mice at high levels over an extended time frame, GCaMP-X showed less damage and improved performance in two-photon imaging of sensory (whisker-deflection) responses or spontaneous </span><span>Ca<sup>2+</sup></span><span> fluctuations, in comparison with GCaMP. Chronic </span><span>Ca<sup>2+</sup></span><span> imaging of one month or longer was conducted for cultured cortical neurons expressing GCaMP-X, unveiling that spontaneous/local </span><span>Ca<sup>2+</sup></span><span> transients progressively developed into autonomous/global </span><span>Ca<sup>2+</sup></span><span> oscillations. Along with the morphological indices of neurite length and soma size, the major metrics of oscillatory </span><span>Ca<sup>2+</sup></span><span>, including rate, amplitude and synchrony were also examined. Dysregulations of both neuritogenesis and </span><span>Ca<sup>2+</sup></span><span> oscillations became discernible around 2</span><span>–</span><span>3 weeks after virus injection or drug induction to express GCaMP in newborn or mature neurons, which were exacerbated by stronger or prolonged expression of GCaMP. In contrast, neurons expressing GCaMP-X were significantly less damaged or perturbed, altogether highlighting the unique importance of oscillatory </span><span>Ca<sup>2+</sup></span><span> to neural development and neuronal health. In summary, GCaMP-X provides a viable solution for </span><span>Ca<sup>2+</sup></span><span> imaging applications involving long-time and/or high-level expression of </span><span>Ca<sup>2+</sup></span><span> probes. </span></p>
Test dataset for "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks"
<p>Data corresponding to test dataset used in Aberra AS, Lopez A, Grill WM, Peterchev AV. (2022). "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks". bioRxiv. Dataset includes:</p> <ul> <li><em>simnibs/ -</em> SimNIBS mesh and E-field solution file used in test dataset (posterior-anterior TMS of M1 in <em>ernie</em> example mesh, meshed with mri2mesh pipeline)</li> <li><em>layer_data/ - </em>surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li> <li><em>nrn_sim_data/ - </em>Thresholds from NEURON simulations for all 25 model neurons included in the study, each at 4,999-5,000 positions and 12 azimuthal orientations ("ground truth" for CNN) </li> <li><em>cell_data/</em> - Coordinates and morphology information for all model neurons</li> <li><em>weights/</em> - Trained 3D convolutional neural networks for estimating neuron model-specific TMS thresholds given input E-field distributions on a 3D grid (see code/manuscript for dimensions)</li> <li><em>est_data/ </em>- Output of trained CNNs on all E-field data for test dataset <em> </em></li> </ul> <p> </p>
SUMOylation of NaV1.2 channels regulates the velocity of backpropagating action potentials in cortical pyramidal neurons
<p>Voltage-gated sodium channels located in axon initial segments (AIS) trigger action potentials (AP) and play pivotal roles in the excitability of cortical pyramidal neurons. The differential electrophysiological properties and distributions of Na<sub>V</sub>1.2 and Na<sub>V</sub>1.6 channels lead to distinct contributions to AP initiation and backpropagation. While Na<sub>V</sub>1.6 at the distal AIS promotes AP initiation and forward propagation, Na<sub>V</sub>1.2 at the proximal AIS promotes backpropagation of APs to the soma. Here, we show the Small Ubiquitin-like Modifier (SUMO) pathway modulates persistent sodium current (I<sub>NaP</sub>) generation at the AIS to increase neuronal gain and the speed of backpropagation. Since SUMO does not affect Na<sub>V</sub>1.6, these effects were attributed to SUMOylation of Na<sub>V</sub>1.2. Moreover, SUMO effects were absent in a mouse engineered to express Na<sub>V</sub>1.2-Lys38Gln channels that lack the site for SUMO linkage. Thus, SUMOylation of Na<sub>V</sub>1.2 exclusively controls I<sub>NaP</sub> generation and AP backpropagation, thereby playing a prominent role in synaptic integration and plasticity.</p>
All-trans retinoic acid induces synaptic plasticity in human cortical neurons
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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)
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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.