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5,448 results for “Neuron”
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 Sets ''Omeprazole and Proteinoids in Neuron Models''
<p>Data Sets ''Omeprazole and Proteinoids in Neuron Models''</p>
The joint representation of self-motion and touch in superior colliculus neurons supporting active sensation
<p><span><span>Localizing</span><span> objects using active </span><span>sensing</span><span> requires </span><span>the</span><span> brain </span><span>to</span> <span>integrate</span> <span>a </span><span>map </span><span>sensory</span><span> space against</span><span> an ongoing </span><span>estimate</span><span> of </span><span>body position</span><span>. </span><span>While </span><span>such computations </span><span>are</span><span> often</span> <span>ascribed to </span><span>the </span><span>cerebral </span><span>corte</span><span>x</span><span>, we examined the </span><span>midbrain</span><span> superior colliculus (SC), due to its </span><span>close relationship</span><span> with</span> <span>the sensory periphery </span><span>as well as</span><span> higher, motor-related brain regions. </span><span>Using high-density electrophysiology and movement tracking, w</span><span>e discovered tha</span><span>t</span> <span>the </span><span>on-going </span><span>kinematics of </span><span>whisker motion</span> <span>and locomotion </span><span>speed </span><span>accurately predict </span><span>the firing rate of</span><span> mouse</span><span> SC neurons</span><span>. </span><span>Neural activity was best predicted by m</span><span>ovement</span><span>s </span><span>occurring</span> <span>either </span><span>in the past, present, or future, </span><span>revealing</span><span> that the SC population continuously </span><span>estimates</span> <span>a</span> <span>trajector</span><span>y of self-motion</span><span>. </span><span>Neural </span><span>selectivity</span><span> for </span><span>combinations </span><span>of kinematic features built </span><span>an accurate</span><span> representation of</span><span> whisker </span><span>position</span> <span>with</span><span> high temporal resolution.</span> <span>Half of all self-motion encoding</span> <span>neurons displayed a </span><span>touch</span><span> response </span><span>as</span><span> an object entered the active whisking field. </span><span>Trial-to-trial v</span><span>ariation in the size of this response was explained by the </span><span>position</span><span> of </span><span>the whisker</span><span> upon touch.</span><span> Taken together, these data </span><span>indicate</span><span> that SC neurons linearly combine an internal estimate of </span><span>body </span><span>movements</span><span> with </span><span>external</span><span> stimulation to </span><span>enable active tactile localization</span><span>.</span></span><span> </span></p>
Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C
<p>Supplementary example data for the work presented in "<em>Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C</em>". </p> <p><strong>Abstract: </strong></p> <p>Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.</p> <p> </p>
Chondroitin Sulphate and Proteinoids in Neuron Models
<p>Data for the ''Chondroitin Sulphate and Proteinoids in Neuron Models'' paper</p>
Data and scripts used in: "Exploring Biological Neuronal Correlations with Quantum Generative Models"
<div>Data and script for the manuscript "Exploring Biological Neuronal Correlations with Quantum Generative Models", by Vinicius Hernandes and Eliska Greplova.</div> <h3>main scripts</h3> <div> <p><strong><em>generate_activity_dataset.py</em></strong></p> <p>reshape data in <em>neuronData.npy</em> to 50k samples of (neurons, timesteps) shape, saved in <em>activity_data.npy</em></p> <p><strong><em>create_target_distributions.py</em></strong></p> <p>based on the dataset, makes dicionary with the the target distribution for each (neurons, timesteps) pair, saved in <em>distribution_target_dictionary.pkl</em></p> <p><strong><em>create_hyperparameters_file.py</em></strong></p> <p>generates <em>hyperparameters.csv</em>, containing:</p> </div> <ul> <li>number of neurons</li> <li>number of timesteps</li> <li>number of auxiliary_qubits</li> <li>batch_size</li> <li>learning rate of generator</li> <li>learning rate of critic</li> <li>number parametrized layers</li> <li>number of training iterations</li> <li>loss type</li> </ul> <p>for each run</p> <p><strong><em>train_qgan.py</em></strong></p> <div> <p>trains models defined <em>models.py</em> using <em>activity_data.npy</em> dataset, and for the hyperparameters defined in <em>hyperparameters.csv</em></p> </div> <div>saves loss functions, and the trained models for each 10 iterations, in specific folders indexed by the run specified in the hyperparameters file</div> <div> </div> <div><strong><em>generate_fake_activity.py</em></strong></div> <div> </div> <div>uses trained models saved in <em>output/models/run{run}/i{training_step}.pth</em> for a specific <em>training_step</em> and <em>run</em> to generate fake data, and save them in <em>output/generated_data/run{run}/i{training_step}.npy</em> files</div> <div> </div> <div><strong><em>analyze_error.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate two statistical quantities (k-probs and firing rate), using the function in <em>metrics.py</em>, and compare the errors in those quantities between the models using k-loss and standard-loss</div> <div> </div> <div><strong><em>analyze_stats.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate:</div> <ul> <li>js diverge for each training step, and final distribution of generated states, stored in <em>distribution_target_dictionary.pkl</em></li> <li>other statistical quantities, using the function in <em>metrics.py</em> file</li> </ul> <h3>auxiliary scripts</h3> <div><strong><em>metrics.py</em></strong></div> <div> </div> <div>functions to calculate neuronal statistics</div> <div> </div> <div><strong><em>aux.py</em></strong></div> <div> </div> <div>auxiliary functions:</div> <ul> <li>to generate states distribution given a dataset</li> <li>custom js divergence</li> </ul> <h3>Data</h3> <p><strong><em>neuronData.npy</em></strong></p> <p>neuronal data from Marre et al., Multi-electrode array recording from salamander retinal ganglion cells (2017)</p> <p><strong><em>activity_data.npy</em></strong></p> <p>dataset obtained from <em>neuronData.npy</em>, taking 50 thousand samples of shape (neurons, timesteps)</p> <p><strong><em>output</em></strong></p> <p>results obtained from <em>train_qgan.py</em> and <em>generate_fake_activity.py</em> </p> <p>contains:</p> <ul> <li><strong><em>losses</em></strong></li> </ul> <p>generator and critic loss for all training runs and steps</p> <ul> <li><strong><em>models</em></strong></li> </ul> <p>saved torch models every 10 training steps, for all training runs</p> <ul> <li><strong><em>generated_data</em></strong></li> </ul> <p>generated data for all models saved in <em>models</em></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>
Characterizing and targeting glioblastoma neuron-tumor networks with retrograde tracing
<h2>Dataset</h2> <p>Space ranger output (Visium platform) of two human slice culture samples (S1 & S2) injected with GBstarter cells (<span><span>Tetzlaff et al., 2024</span></span>). </p>
MERFISH Dataset of Mouse Dopamine Neurons_03Jul2024
<p>Cell_by_gene and Cell_metadata files from MERFISH runs of mouse Midbrain spatial transcriptomics. Datasets are setup to be analyzed using Seurat v5.</p>
Key Resource Table associated with "Endo-IP and Lyso-IP Toolkit for Endolysosomal Profiling of Human Induced Neurons"
<p>This entry contains a Key Resource table associated with: https://doi.org/10.1101/2024.09.24.614704</p>
ROSMAP meQTL Results for Neurons with regionalpcs and averages
<p>This dataset contains methylation quantitative trait loci (meQTL) results for the following study:</p> <p><strong><em>"regionalpcs improve discovery of DNA methylation associations with complex traits"</em></strong></p> <p>Tiffany Eulalio*<sup>1</sup>, Min Woo Sun<sup>1</sup>, Olivier Gevaert<sup>1</sup>, Michael D. Greicius<sup>2</sup>, Thomas J. Montine<sup>3</sup>, Daniel Nachun*‡<sup>3</sup>, Stephen B. Montgomery*‡<sup>1,3</sup></p> <p>‡ These authors contributed equally as senior authors</p> <p>* Corresponding authors: Tiffany Eulalio (<a href="mailto:eulalio@alumn.stanford.edu">eulalio@alumn.stanford.edu</a>), Daniel Nachun (<a href="mailto:dnachun@stanford.edu">dnachun@stanford.edu</a>), Stephen B. Montgomery (<a href="mailto:smontgom@stanford.edu">smontgom@stanford.edu</a>)</p> <p> Author affiliations:</p> <p>1. Department of Biomedical Data Science, Stanford University, Stanford, CA</p> <p>2. Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA</p> <p>3. Department of Pathology, Stanford University, Stanford, CA</p> <p> </p> <p><strong>Dataset description</strong>:</p> <p>This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).</p> <p><strong>Contents:</strong></p> <ul> <li><strong>Parquet tar files</strong>: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. <code>parquet1</code> includes chromosomes 1-10, and <code>parquet2</code> includes chromosomes 11-22.</li> <li><strong>cis_qtl_summary_stats.csv</strong>: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.</li> <li><strong>cis_qtl.signif_pairs.csv</strong>: Contains the significant QTL results identified by FastQTL.</li> <li><strong>cis_qtls.time_to_run.txt</strong>: Reports the running time for FastQTL analysis.</li> <li><strong>cis_qtls.cis_qtl.txt.gz</strong>: Contains comprehensive results for all cis QTLs.</li> </ul> <p>This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.</p>
Data from: Structural and functional brain connectome in motor neuron diseases: a multicenter MRI study
Objective. To investigate structural and functional neural organization in amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS) and progressive muscular atrophy (PMA) patients. Methods. 173 ALS, 38 PLS, 28 PMA sporadic patients and 79 healthy controls were recruited from three Italian centers. Subjects underwent clinical, neuropsychological and brain MRI evaluations. Using graph analysis and connectomics, global and lobar topological network properties and regional structural and functional brain connectivity were assessed. The association between structural and functional network organization and clinical/cognitive data was investigated. Results. Compared to healthy controls, ALS and PLS patients showed altered structural global network properties, as well as local topological alterations and decreased structural connectivity in sensorimotor, basal ganglia, frontal and parietal areas. PMA patients showed preserved global structure. Patient groups did not show significant alterations of functional network topological properties relative to controls. Increased local functional connectivity was observed in ALS patients in the precentral, middle and superior frontal areas, and in PLS patients in the sensorimotor, basal ganglia and temporal networks. In both ALS and PLS patients, structural connectivity alterations correlated with motor impairment, while functional connectivity disruption was closely related to executive dysfunctions and behavioral disturbances. Conclusions. This multicenter study showed widespread motor/extra-motor network degeneration in ALS and PLS, suggesting that graph analysis and connectomics might represent a powerful approach to detect upper motor neuron degeneration, extra-motor brain changes and network reorganization associated with the disease. Network-based advanced MRI provides an objective in vivo assessment of motor neuron diseases, delivering potential prognostic markers.
Data from: The complex synaptic pathways onto a looming-detector neuron revealed using serial block-face scanning electron microscopy (SBEM)
<p>The locust's lobula giant movement detector 1 (LGMD1) looming detector pathway is part of the compound eye visual system and enables the animals to reliably detect collisions. Trans-medullary afferent neurons are considered key players in this pathway. Thousands of these neurons connect the second visual neuropile region, or medulla, with the third neuropile region, or lobula complex. In the lobula complex they are in synaptic contact with the LGMD1, which forms a dendritic tree in the outer region of the lobula complex neuropile. In order to describe their anatomy and connectivity patterns with other upstream neurons of the LGMD1, we used serial block-face scanning electron microscopy. We thus produced serial electron micrographs spanning from the dendritic tree in the outer lobula complex to the origin of the trans-medullary afferent neurons in the medulla. Starting from the LGMD1, we segmented and 3D-reconstructed entire trans-medullary afferents, as well as connecting neurons and other trans-medullary neurons nearby. This study was based on two datasets from different locusts of fourth instar. Here we provide the raw data for this study as .tiff stacks.</p>
Behavioral control by depolarized and hyperpolarized states of an integrating neuron
<p><span>Coordinated transitions between mutually exclusive motor states are central to behavioral decisions. During locomotion, the nematode <i>Caenorhabditis elegans</i> spontaneously cycles between forward runs, reversals, and turns with complex but predictable dynamics. Here we provide insight into these dynamics by demonstrating how<i> </i>RIM interneurons, which are active during reversals, </span>act in two modes to stabilize both forward runs and reversals. <span>By systematically quantifying the roles of RIM outputs during spontaneous behavior, we </span>show that RIM lengthens reversals when depolarized through glutamate and tyramine neurotransmitters and lengthens forward runs when hyperpolarized through its gap junctions. RIM is not merely silent upon hyperpolarization: RIM <span>gap junctions actively reinforce a hyperpolarized state of the reversal circuit. Additionally, the combined outputs of chemical synapses and gap junctions from RIM regulate forward-to-reversal transitions. Our results indicate that multiple classes of RIM synapses create behavioral inertia during spontaneous locomotion.</span></p>
Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans
<p>To survive elevated temperatures, ectotherms adjust the fluidity of membranes by fine-tuning lipid desaturation levels in a process previously described to be cell-autonomous. We have discovered that, in <em>Caenorhabditis elegans,</em> neuronal Heat shock Factor 1 (HSF-1), the conserved master regulator of the heat shock response (HSR)- causes extensive fat remodelling in peripheral tissues. These changes include a decrease in fat desaturase and acid lipase expression in the intestine, and a global shift in the saturation levels of plasma membrane’s phospholipids. The observed remodelling of plasma membrane is in line with ectothermic adaptive responses and gives worms a cumulative advantage to warm temperatures. We have determined that at least six TAX-2/TAX-4 cGMP gated channel expressing sensory neurons and TGF-β/BMP are required for signalling across tissues to modulate fat desaturation. We also find neuronal <em>hsf-1</em> is not only sufficient but also partially necessary to control the fat remodelling response and for survival at warm temperatures. This is the first study to show that a thermostat-based mechanism can cell non-autonomously coordinate membrane saturation and composition across tissues in a multicellular animal.</p>
Labeled motor neurons following single and double nerve reinnervation of the long head of the biceps
<p>We developed a novel rat model to investigate the neurophysiological effects of peripheral double nerve transfers to a single target muscle. In 62 male Sprague-Dawley rats the ulnar nerve of the antebrachium alone (n=30) or together with the anterior interosseus nerve (n=32) was transferred to reinnervate the long head of the biceps brachii. To further assess reinnervation, retrograde labeling was performed. Twelve weeks following surgery retrograde tracer dyes were injected in the long head of the biceps muscle to stain the reinnervating motoneurons. In 7 control animals, the number of innervating motorneurons was assessed by injecting retrograde tracer in the untreated long head of the biceps.</p> <p>The aim of this dataset was to compare the effects of single nerve transfer and double nerve transfer on motor neuron count.</p>
Whole Brain Drosophila Larval Neurons
<p>Example intact whole brains from female and male Drosophila larva expressing functional neuron markers associated with manuscript -</p> <p><strong>Intact Drosophila Whole Brain Cellular Quantitation reveals Sexual Dimorphism</strong>. Wei Jiao, Gard Spreemann, Evelyne Ruchti, Soumya Banerjee, Ying Shi, R. Steven Stowers, Kathryn Hess and Brian D. McCabe</p> <p>Brain Mind Institute, EPFL - Swiss Federal Institute of Technology Lausanne, Switzerland <a href="https://mccabelab.org">https://mccabelab.org</a></p> <p>See ReadMe file for detailed genotypes and methods.</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>
Functional interactions among neurons within single columns of macaque V1
<p>Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.</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>
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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.