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47 results for “cortical networks”
Data for conductance-based simulations of "Cortical oscillations support sampling-based computations in spiking neural networks"
<p>This repository contains the full data generated by the conductance-based simulations described in: <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009753">Cortical oscillations support sampling-based computations in spiking neural networks</a>. The code is accessible via <a href="https://doi.org/10.5281/zenodo.5512526.">this repository</a>.</p>
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
<p>Dataset linked to the article with the same title</p> <p>The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to <em>O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. </em>Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit.</p> <p><strong>Analyzing the model</strong></p> <p>The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook)</p> <p><strong>Simulating the model</strong></p> <p>To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch <em>nbS1-2023</em>, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a <em>"special"</em> executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where <em>mod-files-from-released-circuit </em>is replaced by the location of <em>O1/mods</em> on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (<em>simulation_config.zip</em>).</p> <p><strong>Analyzing simulation results</strong></p> <p>Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations.</p> <p><strong>Version 2</strong></p> <p>Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1.</p> <p>--</p> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
Data from "Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques"
<p>DATA FILES from the study below:</p> <p><strong><a href="https://www.biorxiv.org/content/10.1101/603530v1">Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques</a></strong></p> <p>Jérôme Sallet, MaryAnn P Noonan, Adam Thomas, Jill X O’Reilly, Jesper Anderson, Georgios KPapageorgiou, Franz X Neubert, Bashir Ahmed, Jackson Smith, Andrew H Bell, Mark J Buckley, LéaRoumazeilles, Steven Cuell, Mark E Walton, Kristine Krug, Rogier B Mars, Matthew FS Rushworth</p> <p>bioRxiv 603530; doi: <a href="https://doi.org/10.1101/603530">https://doi.org/10.1101/603530</a></p> <p>*.nii.gz files could be opened with FSLeyes -<a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)</a></p> <p>Dara are also available from : https://www.jeromesallet.org/data-ofc-reversal-learning</p>
MICrONS: Machine Intelligence from Cortical Networks
<p>A ground truth dataset for 3D neuron segmentation from electron microscopy (EM) images of mouse visual cortex, created for <a href="https://www.iarpa.gov/research-programs/microns">IARPA's Machine Intelligence from Cortical Networks (MICrONS) program</a>. For more information, please visit <a href="https://www.microns-explorer.org/">our MICrONS explorer website</a>.</p> <p> </p> <p><strong>Citation</strong></p> <p><em>Functional connectomics spanning multiple areas of mouse visual cortex</em><br> MICrONs Consortium et al.<br> bioRxiv 2021.07.28.454025; doi: https://doi.org/10.1101/2021.07.28.454025</p> <p> </p> <p><strong>Data Format Specification</strong></p> <p>Volumes and annotations are stored in a single HDF5 file with the following datasets:</p> <ul> <li><strong>volumes</strong>: are stored in row-major format, i.e., with dimensions (z, y, x). Every volume has an attribute <strong>resolution</strong> that specifies the voxel size in <em>nm</em>. <ul> <li><strong>image</strong></li> <li><strong>segmentation</strong></li> <li><strong>mitochondria</strong> (optional)</li> <li><strong>synapse</strong> (optional): annotation for postsynaptic densities (PSDs)</li> <li><strong>fold</strong> (optional): annotation for folds (a kind of image defect) in images</li> <li><strong>mask</strong>: valid/invalid voxels are represented as 1s/0s respectively.</li> </ul> </li> <li><strong>annotations</strong>: are incomplete, meaning that each volume may or may not contain proper annotations. <ul> <li><strong>object_ids</strong> (optional): small unidentified objects or intracellular organelles that are oversegmented, which may require special handling.</li> <li><strong>myelin_ids</strong> (optional)</li> <li><strong>blood_vessel_ids</strong> (optional)</li> <li><strong>soma_ids</strong> (optional)</li> <li><strong>nucleus_ids</strong> (optional)</li> </ul> </li> </ul> <p> </p>
Cortical oscillations support sampling-based computations in spiking neural networks
<p>This archive contains the scripts for generating the data and figures and the data of the publication: "Cortical oscillations support sampling-based computations in spiking neural networks".</p> <p>The different parts of the material are grouped into separate archives to enable modular usage and can be individually downloaded as required:</p> <ul> <li>The archive spike-based-tempering_scripts.tgz contains all the scripts to reproduce the simulation data and figures.</li> <li>The file software.img contains the third-party software needed to execute the simulations of the current-based experiments.</li> <li>The archive current-based_experiments_data includes the scripts and the simulation data for the current-based experiments.</li> </ul>
Network-level encoding of local neurotransmitters in cortical astrocytes
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Data related to "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al.
<p>This is Data for "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al., 2022</p> <p>This repository contains:</p> <p>Supplementary Dataset Tables 1-4 (Supplementary_Dataset_Tables_1-4.xlsx). They contain DE genes and GO terms from snRNA-seq and visium analysis.</p> <p>Single nuclei and Visium spatial transcriptomics sequencing data (snRNA-seq_and_spatial_transcriptomics.zip) containing raw count matrices of snRNA-seq samples; Conos object with aligned snRNA-seq samples; snRNA-seq single nuclei cell subtype annotations; raw count matrices of Visium spatial transcriptomics samples; Visium spatial transcriptomics manual histological cortical layer annotations; and 10x Genomics spaceranger count pipeline output for Visium spatial transcriptomics data.</p> <p>Histological images of H&E stained Visium spatial transcriptomics samples mounted on visium slide capture area (visium_sample_images.zip)</p>
Cooperative cortical network for categorical processing of Chinese lexical tone
<p><strong>This dataset contains the ECoG, CT and MRI data for the six subjects associated with the manuscript, "Cooperative cortical network for categorical processing of Chinese lexical tone", as well as stimulus sound files used in the study.</strong></p> <p>data_MRI.rar - MRI scan before the ECoG electrode implantation</p> <p>data_CT.rar - CT images with ECoG electrode implanted</p> <p>data_ECoG.rar - ECoG recording of six patients reported in the manuscript</p> <p>stimuli_BehaviorContinumm.rar - Chinese tone continuum stimuli (Token 1-13 as in Fig 1 of the manuscript)</p> <p>stimuli_ECoG_MMN.rar - Chinese tone stimuli used for oddball paradigm in ECoG experiment (Token 2, 5 and 8, see Fig 1 of the manuscript)</p> <p>Upon email request (hongbo@tsinghua.edu.cn), the authors may provide analysis code. </p> <p> </p> <p><strong>Original manuscript:</strong></p> <p>Si, Zhou and Hong. <em>PNAS</em>, 2017</p> <p><strong>Cooperative cortical network for categorical processing of Chinese lexical tone</strong></p> <p><strong>Abstract:</strong> In tonal languages such as Chinese, lexical tone with varying pitch contours serves as a key feature to provide contrast in word meaning. Similar to phoneme processing, behavioral studies have suggested that Chinese tone is categorically perceived. However, its underlying neural mechanism remains poorly understood. By conducting cortical surface recordings in surgical patients, we revealed a cooperative cortical network along with its dynamics responsible for this categorical perception. Based on an oddball paradigm, we found amplified neural dissimilarity between cross-category tone pairs, rather than between within-category tone pairs, over cortical sites covering both the ventral and dorsal streams of speech processing. The bilateral superior temporal gyrus (STG) and the middle temporal gyrus (MTG) exhibited increased response latencies and enlarged neural dissimilarity, suggesting a ventral hierarchy that gradually differentiates the acoustic features of lexical tones. In addition, the bilateral motor cortices were also found to be involved in categorical processing, interacting with both the STG and the MTG and exhibiting a response latency in between. Moreover, the motor cortex received enhanced Granger causal influence from the semantic hub, the anterior temporal lobe, in the right hemisphere. These unique data suggest that there exists a distributed cooperative cortical network supporting the categorical processing of lexical tone in tonal language speakers, not only encompassing a bilateral temporal hierarchy that is shared by categorical processing of phonemes but also involving intensive speech-motor interactions over the right hemisphere, which might be the unique machinery responsible for the reliable discrimination of tone identities.</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>
Data from: The fat body cortical actin network regulates Drosophila inter-organ nutrient trafficking, signaling, and adipocyte cell size
<p>Defective nutrient storage and adipocyte enlargement (hypertrophy) are emerging features of metabolic syndrome and type 2 diabetes. How the cytoskeletal network contributes to nutrient uptake, fat storage, and adipocyte size remains poorly understood. Utilizing the <em>Drosophila</em> larval fat body (FB) as a model adipose tissue, we show that a specific actin isoform—Act5C—forms the cortical actin network necessary for inter-organ lipid trafficking. Act5C also promotes FB tissue expansion during larval development so larvae can store sufficient biomass for metamorphosis. We find FB-specific loss of Act5C, but not other <em>Drosophila</em> actins, perturbs FB triglyceride (TG) storage in lipid droplets (LDs), resulting in developmentally delayed larvae that fail to develop into flies. Act5C localizes to the FB cell surface where it intimately contacts peripheral LDs (pLDs), forming a cortical actin network together with spectrins for cell architectural support. While both the cortical actin and spectrin cytoskeletons maintain FB cell surface architecture, we find that only the actin network is required for fat storage. Mechanistically, we show that FBs lacking the Act5C cortical cytoskeleton exhibit a block in lipoprotein (Lpp) secretion from FB cells, and a subsequent disruption of gut:FB inter-organ lipid transport, resulting in mid-gut fat accumulation. Utilizing temporal RNAi-depletion approaches, we also reveal that Act5C is indispensable post-embryogenesis during larval feeding to promote FB cell expansion. Act5C-deficient FBs fail to expand cell sizes, leading to lipodystrophic larvae unable to accrue sufficient biomass for metamorphosis. Collectively, we propose that the Act5C-mediated cortical actin network of <em>Drosophila</em> adipose tissue plays an essential role in post-embryonic inter-organ nutrient transport and FB cell size determination for organismal energy homeostasis and development.</p>
Data for: Prediction in cultured cortical neural networks
<p>Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing, and is believed to be involved in motor and cognitive control and decision making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus. However, there is no proof that the ability to predict is a generic feature of neural networks. We investigated whether random in vitro neuronal networks can predict stimulation, and how prediction is related to short and long-term memory. To answer these questions we applied two different stimulation modalities. Focal electrical stimulation has been shown to induce long term memory traces, whereas global optogenetic stimulation did not. We used mutual information to quantify how much activity recorded from these networks reduces the uncertainty of upcoming stimuli (prediction) or recent past stimuli (short-term memory). <br> <br>Cortical neural networks did predict future stimuli, with the majority of all predictive information provided by the immediate network response to the stimulus. Interestingly, prediction strongly depended on short-term memory of recent sensory inputs during focal as well as global stimulation. However, prediction required less short-term memory during focal stimulation. Furthermore, the dependency on short-term memory decreased during 20h of focal stimulation, when long-term connectivity changes were induced. These changes are fundamental for long-term memory formation, suggesting that besides short-term memory the formation of long-term memory traces may play a role in efficient prediction. </p>
Data for: Prediction in cultured cortical neural networks
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Data from: The fat body cortical actin network regulates Drosophila inter-organ nutrient trafficking, signaling, and adipocyte cell size
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Multimodal Investigation of Cortico-Basal Ganglia-Thalamo-Cortical Network Dynamics in Dystonic Patients with Deep Brain Stimulation
ClinicalTrials.gov study NCT06716983. IPD Sharing: Not stated. Countries: 1. Publications: 13.
rTMS for the Treatment of Chronic Tinnitus: Optimization by Simulation of the Cortical Tinnitus Network
ClinicalTrials.gov study NCT01663324. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Metformin reverses early cortical network dysfunction and behavior changes in Huntington's disease
Catching primal functional changes in early, "very far from disease onset" (VFDO) stages of Huntington's disease is likely to be the key to a successful therapy. Focusing on VFDO stages, we assessed neuronal microcircuits in premanifest Hdh150 knock-in mice. Employing in vivo two-photon Ca2+ imaging, we revealed an early pattern of circuit dysregulation in the visual cortex- one of the first regions affected in premanifest Huntington's disease - characterized by an increase in activity, an enhanced synchronicity and hyperactive neurons. These findings are accompanied by aberrations in animal behavior. We furthermore show that the anti-diabetic drug metformin diminishes aberrant Huntingtin protein load and fully restores both, early network activity patterns and behavioral aberrations. This network-centered approach reveals a critical window of vulnerability far before clinical manifestation and establishes metformin as a promising candidate for a chronic therapy starting early in premanifest Huntington's disease pathogenesis long before the onset of clinical symptoms.
PhD Thesis - Emergence of cortical network motifs
<p>Annotated representative video of mice performing sensorimotor task.</p>
Data from: Metformin reverses early cortical network dysfunction and behavior changes in Huntington’s disease
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Study of gene networks in basal progenitors during cortical neurogenesis
GEO Series GSE45450. Mus musculus. 11 samples. Type: Expression profiling by array.
Pharmacological reversal of synaptic and network pathology in human MECP2-KO neurons and cortical organoids
GEO Series GSE160146. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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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.