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387 results for “axon”
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>
Spatial characterization of the motor and non-motor somal and axonal transcriptome in adult healthy and mutant FUS mice
<table> <tbody> <tr> <td> <p>Here we investigated the transcriptome of motor and non-motor axons and cell bodies in the context of mutant FUS-related amyotrophic lateral sclerosis (ALS). We applied Nanostring GeoMX Digital Spatial Profiler platform to profile the transcriptome of subcellular compartments in the lower motor circuitry of a mouse model ricapitulating ALS motor symptoms. This work sheds light for the first time on the transcriptomic alterations in axons and in somas which may contribute to axonal degeneration and neuromuscular junction denervation, early features of ALS.</p> </td> </tr> </tbody> </table>
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>
Xiao et al, Oligodendrocyte Precursor Cells Sculpt the Visual System by Regulating Axonal Remodeling [Dataset]
<p>Raw data from the behavior and imaging experiments of Xiao et al., Nature Neuroscience 2022. For additional details about the acquisition of each part of the dataset, refer to the methods section of the paper. From this dataset, using the published code, all the figures relative to the imaging in the optic tectum can be generated and all the cumulative statistics for the behavioural assays run with Stytra recomputed.</p> <p> </p> <p><strong>Organisation of the dataset</strong></p> <p>This dataset is organised in the following subdirectories:<br> - <em>freely_swimming</em>: contains the data for the freely swimming experiments quantifying motor activity in the various ablated groups. It contains subfolders of groups, each of which contains the Stytra raw data directories for all fish of that group. Refer to `Stytra` and `bouter` documentation for further details about the files.<br> - <em>OMR</em>: contains the data for the quantification of OMR reflex across different spatial frequencies. It contains subfolders for the control and ablated group, each of which contains the Stytra raw data directories for all fish of that group.<br> - <em>receptive_field_imaging</em>: contains the imaging data for the receptive field estimation. Subfolders contains, for each individual fish (both ablated and controls are pooled in the same directory):<br> - stytra raw output from the experiment<br> - data_from_suite2p_unfiltered.h5: `flammkuchen`-loadable `.h5` file that contains the raw fluorescent trace<br> - anatomy.mask: `flammkuchen`-loadable mask file saved by the `pypra` tool that was used for segmenting the tectum, delimiting the region of the tectum<br> The folder contains an additional file, `manual_alignment_offsets.h5`, where the offsets of the manual morphing across fish were saved.</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>
Dataset In-vivo estimation of axonal morphology from MRI and EEG data
<p>This dataset includes the data underlying the conclusions made in the scientific article:<br> "In-vivo estimation of axonal morphology from MRI and EEG data"<br> Rita Oliveira, Andria Pelentritou, Giulia Di Domenicantonio, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://www.frontiersin.org/articles/10.3389/fnins.2022.874023/full">https://www.frontiersin.org/articles/10.3389/fnins.2022.874023</a></p> <p>The main objective is to use data collected in-vivo in humans to estimate microscopic morphologic features of the white matter tracts.</p> <p>The in-vivo data estimated along a white matter tract of interest includes:<br> • the MRI g-ratio sampled along the visual transcallosal white matter tract<br> • a measure of conduction velocity estimated from an EEG measure of interhemispheric transfer time (IHTT)</p> <p>The microscopic morphologic features of white matter we estimate are:<br> • the axonal radius distribution, P(r)<br> • the g-ratio dependence on the radius, g(r)</p> <p>-------------------------------------------------------------------------<br> CONTENT:</p> <p>This package includes data for all the 14 subjects used in the corresponding scientific article:<br> • G-ratio values sampled along the transcallosal visual tract <br> double vector (# MRI_gratio samples x 1): G_ratio_samples.mat<br> • Length of the transcallosal visual tract <br> double: Tract_length.mat<br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the left brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_left_brain_V1V2.mat <br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the right brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_right_brain_V1V2.mat <br> • Vector of the time sample of the EEG epochs<br> double vector (1 x #time points): time_vec.mat</p> <p>The codes used in the analysis of this data are available on our online repository: <a href="https://github.com/LREN-physics/AxonalMorphology">https://github.com/LREN-physics/AxonalMorphology</a>.</p> <p>-------------------------------------------------------------------------<br> AUTHORS:</p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p>
Data set for "Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex"
<p>Data set for: Yamashita T, Vavladeli A, Pala A, Galan K, Crochet S, Petersen SSA, Petersen CCH (2018) Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex. Front Neuroanat 12: 33. https://doi.org/10.3389/fnana.2018.00033</p> <p>There are 25 files in this data upload:</p> <p>1. '2018_Yamashita_FrontNeuroanat.pdf' - this a pdf version of the online publication.</p> <p>2. 'Yamashita_Figure2_Quantification.xlsx' - this is a Microsoft Excel file giving the locations of high density axonal projections from layer 2/3 pyramidal neurons in the mouse C2 barrel column in the coordinate frame of Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. The data are plotted in Figure 2 of Yamashita et al., 2018.</p> <p>3. 'Yamashita_Figure7_Quantification.xlsx' - this is a Microsoft Excel file giving the dendritic length, number of dendrites, number of dendritic nodes and total axonal length, as well as the axonal length in the different projection zones for each reconstructed neuron. The data are plotted in Figure 7 of Yamashita et al., 2018.</p> <p>4. 'Yamashita_SupMov1_S2P_AP049.mov' - this is a QuickTime video file, showing the 3D structure of neuron AP049 featured in Figure 3 of Yamashita et al., 2018.</p> <p>5. 'Yamashita_SupMov2_M1P_TY308.mov' - this is a QuickTime video file, showing the 3D structure of neuron TY308 featured in Figure 5 of Yamashita et al., 2018.</p> <p>6. 'AV198.zip' - this zipped folder contains data relating to mouse AV198: a) 'AV198_stack.tif' the z-stack of whole-brain fluorescence images from expression of tdTomato in layer 2/3 neurons of the C2 barrel column of mouse AV198. b) 'AV198_ROI_Box.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a box. c) 'AV198_ROI_Point.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a point. d) 'AV198_Paxinos' is a folder showing the coronal fluorescent brain sections in pdf format overlaid on the equivalent drawing from Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press.</p> <p>7. 'AV199.zip' - same as 'AV198.zip' but for mouse AV199.</p> <p>8. 'AV201.zip' - same as 'AV198.zip' but for mouse AV201.</p> <p>9. 'AV202.zip' - same as 'AV198.zip' but for mouse AV202.</p> <p>10. 'AV203.zip' - same as 'AV198.zip' but for mouse AV203.</p> <p>11. 'AP042.ASC' - Neurolucida (http://www.mbfbioscience.com/neurolucida) data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP042. Brain contours are also traced.</p> <p>12. 'AP044.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP044. Brain contours are also traced.</p> <p>13. 'AP046.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP046. Brain contours are also traced.</p> <p>14. 'AP047.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP047. Brain contours are also traced.</p> <p>15. 'AP049.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP049. Brain contours are also traced.</p> <p>16. 'TY220.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY220. Brain contours are also traced.</p> <p>17. 'TY288.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY288. Brain contours are also traced.</p> <p>18. 'TY300.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY300. Brain contours are also traced.</p> <p>19. 'TY302.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY302. Brain contours are also traced.</p> <p>20. 'TY308.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY308. Brain contours are also traced.</p> <p>21. 'TY310.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY310. Brain contours are also traced.</p> <p>22. 'TY337.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY337. Brain contours are also traced.</p> <p>23. 'TY345.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY345. Brain contours are also traced.</p> <p>24. 'TY367.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY367. Brain contours are also traced.</p> <p>25. 'TY369.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY369. Brain contours are also traced.</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>
Dataset Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology
<p>This dataset is a subset of the data presented in the article Single‐subject electroencephalography measurement of interhemispheric transfer time for the in‐vivo estimation of axonal morphology Rita Oliveira, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420">https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420</a></p> <p><br> For a complete description of our approach for axonal morphology estimation in-vivo, see: <em>Oliveira, R., Pelentritou, A., Di Domenicantonio, G., De Lucia, M., and Lutti, A. (2022). In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front. Neurosci. 16, 1–18. doi: 10.3389/fnins.2022.874023.</em></p> <p>This dataset contains the following Matlab files:</p> <ul> <li>CD_CondNameVisualField_LeftBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the left brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>CD_CondNameVisualField_RightBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the right brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>Stats_Source_CondNameVF_Occipital_LeftBrainOccipital.mat - Result of the cluster permutation for the left brain cortex for the CondNameVF, CondName being Left or Right visual stimulation (Fieldtrip stat structure)</li> <li>Stats_Source_CondNameVF_Occipital_RightBrainOccipital.mat - Result of the cluster permutation for the right brain cortex for the CondNameVF, CondName being Left or - Right visual stimulation (Fieldtrip stat structure)</li> <li>time_vec.mat - Time vector associated with the timecourses [1 x #timepoints]</li> <li>Occipital_vertices.mat - Structure containing the vertices of the brain mesh of the region of interest. Occipital_vertices.Vertices [1 x #vertices]</li> <li>G_ratio_samples.mat - MRI g-ratio sampled along the occipital transcallosal tract [#samples x 1]</li> <li>Tract_length.mat - Length of the occipital transcallosal tract (double)</li> </ul> <p>The analysis scripts that allow the users to replicate the results of the original publication can be found here: <a href="https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation">https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation</a><br> <br> Funding: Swiss National Science Foundation (grant no 320030 184784 and 32003B 212981), ROGER DE SPOELBERCH Foundation and Bertarelli Catalyst Foundation.</p> <p> </p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p> <p> </p>
Analysis of AaH-II effect in the axon initial of neocortical pyramidal neurons
<p>This dataset contains data in HEK293 neurons expressing either human Na<sub>v</sub>1.2 or human Na<sub>v</sub>1.6 and whole-cell electrophysiological recordings and imaging data from neocortical layer-5 pyramidal neuron in brain slices of the mouse.</p> <p> </p> <p>This dataset is used in the paper:</p> <p>Abbas F, Blömer LA, Millet H, Montnach J, De Waard M, Canepari M. Analysis of the effect of the scorpion toxin AaH-II on action potential generation in the axon initial segment. bioRxiv, 2023. https://www.biorxiv.org/content/10.1101/2023.10.06.561226v1</p>
CA3 axonal calcium imaging
<p>Memorizing locations that are harmful or dangerous is a key capability of all organisms, and requires an integration of affective and spatial information. In mammals, the dorsal hippocampus mainly processes spatial information, while the intermediate to ventral hippocampal divisions receive affective information via the amygdala. However, how spatial and aversive information is integrated is currently unknown.</p> <p>To address this question, we recorded the activity of hippocampal long-range CA3 axons at single axon resolution in mice forming an aversive spatial memory. We show that intermediate CA3 to dorsal CA3 (i-dCA3) projections rapidly overrepresent areas preceding the location of an aversive stimulus, due to a spatially selective addition of newly place-coding axons, followed by a spatially nonspecific stabilization. This sequence significantly improves the encoding of location by the i-dCA3 axon population.</p> <p>These results suggest that i-dCA3 axons transmit a precise, denoised and stable signal indicating imminent danger to dorsal hippocampus.</p>
Computational generation of long-range axonal morphologies
<p>This repository contains both the data and scripts to reproduce all figures of the related article.</p> <p>Usually, one should download all the files in a directory and read the README.md file.</p>
AP-4-mediated axonal transport controls endocannabinoid production in neurons - Imaging data 3
<p>Imaging data associated with Fig. 6c,e,f, 7e-h, S5a,b, S6b-d and S7 from the article "AP-4-mediated axonal transport controls endocannabinoid production in neurons", published in Nature Communications by Davies et al.</p> <p>DAGLB_Axon_iPSC_derived_neurons: High-throughput confocal imaging was used to assay the density of DAGLB puncta in axons of iPSC neurons from a patient with AP-4 deficiency syndrome (patient 1) and their matched control, labelled with anti-DAGLB, the axonal marker antibody cocktail SMI312 and DAPI.</p> <p>DAGLB_Soma_iPSC_derived_neurons: High-throughput confocal imaging was used to assay the distribution of DAGLB in iPSC-derived neurons from two patients with AP-4 deficiency syndrome (LoF/LoF) and their matched unaffected controls (WT/LoF). Neurons in 96-well plates were labelled with antibodies against DAGLB, GOLGA1 (a TGN marker) and TUJ1, and DAPI. The ratio between the area of high intensity (HI; overlaps with TGN) and low intensity (LI) DAGLB labelling was quantified from three differentiations per cell line.</p> <p>iPSC_derived_neurons_neurite_outgrowth: Neurite outgrowth was assayed in iPSC-derived cortical neurons from two patients with AP4B1-associated AP-4 deficiency syndrome (SPG47) and their unaffected same sex heterozygous parents (control), using automated live cell imaging. Neurons were cultured in the presence of DMSO (vehicle control) or the MGLL inhibitor ABX-1431 at 10, 50, 100 or 500 nM (the highest two doses were administered only to the patient neurons). Neurons were monitored from 4 h post-plating, with images captured every 3 h until 25 h post-plating.</p> <p>See article for methods and further detail.</p>
AP-4-mediated axonal transport controls endocannabinoid production in neurons - Imaging data 2
<p>Imaging data from the article "AP-4-mediated axonal transport controls endocannabinoid production in neurons", published in Nature Communications by Davies et al., from Fig. 5 and S4.</p> <p>Super resolution structured illumination microscopy (SR-SIM) was used to image HeLa cells expressing endogenously-tagged SERINC1-Clover or SERINC3-Clover, treated with siRNA to knock down AP-4 or non-targeting control siRNA, and labelled with anti-GFP and either anti-ATG9A or anti-DAGLB.</p> <p>SR-SIM was performed on a Zeiss Elyra PS.1 microscope equipped with a 63x/1.46 oil objective (alpha Plan-Apochromat 63x/1.46 Oil Korr M27) and a PCO pco.edge 4.2 sCMOS Camera, and controlled with Zeiss ZEN 2 software with the SR-SIM module. Imaging was performed sequentially, with SERINC1 or SERINC3 (labelled with Alexa Fluor Plus 488) imaged first and DAGLB or ATG9A (labelled with Alexa Fluor 568) imaged second, using 3 rotations of the grid pattern and 5 phases for each rotation. Experiments were performed in biological duplicate, from separate dishes of cells, and with immunofluorescence and microscopy performed independently for each replicate. 20 cells were imaged per condition per replicate, and cell selection and manual focus were performed on the first acquired channel only, without viewing the second channel. For channel alignment, slides with multi-coloured fluorescent beads were imaged before and after each experiment using the same acquisition settings. The data were processed using the SIM module in Zen 2 software in manual mode, using default settings and a theoretical PSF model, except for the following settings: Noise filter -1; SR frequency weighting 2; Max.Isotrop on; Baseline shifted on; Raw scale on. Channel alignment fit parameters were calculated in Zen 2 software using a multi-coloured bead image as input, in affine mode. The resulting parameters were tested on further bead images and then applied to the experimental images. The channel-aligned images were used for colocalisation analysis (see methods of article for details).</p> <p>This dataset includes the raw unprocessed data and the processed and channel aligned input data used for the colocalisation analysis.</p> <p>See article for methods and further detail.</p>
AP-4-mediated axonal transport controls endocannabinoid production in neurons - Imaging data 1
<p>Imaging data from the article "AP-4-mediated axonal transport controls endocannabinoid production in neurons", published in Nature Communications by Davies et al., from Fig. 2, 3, 4, 6 and S2.</p> <p>Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X).</p> <p>HeLa_HADAGL_RUSC2_Fig_4: Widefield imaging of wild-type HeLa and HeLa cells stably expressing GFP-RUSC2, transiently expressing HA-DAGLB or HA-DAGLA, and labelled with anti-HA (Alexa Fluor 680), anti-ATG9A (Alexa Fluor 568) and DAPI.</p> <p>HeLa_KD_DAGLB_TGN_Fig_S2a,b: Widefield imaging of immunofluorescence double labelling of DAGLB (Alexa Fluor 568) and TGN46 (Alexa Fluor 680) in HeLa cells transfected with a non-targeting siRNA (control) or with siRNA to knock down AP-4. DAPI labelling of the nucleus is also shown. Note, the antibody used to label AP4E1 (Alexa Fluor 488) in replicate 1 had high non-specific background and so was not used in the analysis.</p> <p>HeLa_KO_DAGLB_TGN_Fig_2a,b: Widefield imaging of immunofluorescence double labelling of DAGLB (Alexa Fluor 568) and TGN46 (Alexa Fluor 680) in wild-type, AP4B1 knockout, and AP4B1 knockout HeLa cells stably expressing AP4B1 (functional rescue). DAPI labelling of the nucleus is also shown.</p> <p>HeLa_RUSC2_DAGLB_Fig_3: Widefield imaging of DAGLB (Alexa Fluor 568) labelling in wild-type and AP-4 knockout (AP4B1 KO or AP4E1 KO) HeLa cells, stably expressing GFP-tagged RUSC2. AP4B1 knockout HeLa expressing RUSC2-GFP were also transiently transfected with AP4B1 (rescue) or mock transfected without DNA as a negative control. DAPI labelling of the nucleus is also shown.</p> <p>iPSC_Neurons_DAGLB_TGN_Fig_6a,b: Widefield imaging of immunofluorescence triple labelling of DAGLB (Alexa Fluor 568), TGN46 (Alexa Fluor 680) and TUJ1 (Alexa Fluor 488; a marker to distinguish neurons from co-cultured astrocytes) in iPSC neurons from a patient with AP-4 deficiency syndrome (patient 1) and their matched control.</p> <p>SHSY5Y_DAGLB_TGN_Fig_2c,d_Fig_S2c,d: Widefield imaging of immunofluorescence double labelling of DAGLB (Alexa Fluor 568) and TGN46 (Alexa Fluor 680) in control (parental Cas9-expressing), AP4B1-depleted (BKO) and AP4E1-depleted (EKO) undifferentiated and neuronally-differentiated SH-SY5Y cells. DAPI labelling of the nucleus is also shown.</p> <p>See article for methods and further detail.</p>
An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected
<p>This is a collection of preprocessed data and standalone Matlab analysis code for the PLoS: Biology article of the same name, <a href="https://doi.org/10.1371/journal.pbio.3001575">https://doi.org/10.1371/journal.pbio.3001575</a>. The primary processed diffusion MRI connectivity data was reported and previous distributed in our eNeuro article <a href="http://doi.org/10.1523/ENEURO.0416-20.2020">https://doi.org/10.1523/ENEURO.0416-20.2020</a>. The replication dataset was reported and previous distributed by Arnatkeviciute et al. 2021, Nature Communications (<a href="http://doi.org/10.1038/s41467-021-24306-2">https://doi.org/10.1038/s41467-021-24306-2</a>). Please cite these publications when using relevant code or data. Files with the .mat extension are Matlab v7.3 data files. The raw imaging data from which these files were derived are available from <a href="https://db.humanconnectome.org/">https://db.humanconnectome.org</a>.</p>
Molecular and Anatomical Characterization of Parabrachial Neurons and Their Axonal Projections
<p>The parabrachial nucleus (PBN) predominantly relays threatening signals and learned sensory cues that predict threats to forebrain regions. Viral-mediated expression of Cre-dependent effector genes in Cre-driver lines of mice has demonstrated that specific populations of PBN neurons are necessary and sufficient for learning about threats. However, these neurons account for only a fraction of the total population. To further our understanding of the complexity of the PBN neuronal populations, we used single-cell RNA-sequencing technologies, which revealed 21 clusters of neurons (19 glutamatergic, 2 GABAergic) in the PBN and neighboring regions. RNAscope HiPlex <em>in situ</em> hybridization located 12 of these clusters within subregions of the PBN. Viral expression of fluorescent proteins in 21 Cre-driver lines of mice was used to map their axonal projections throughout the brain; they constitute two pathways innervating distinct brain regions. These results are a resource for further interrogation of PBN functions.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 2. Example of different axonal conduction delay between presynaptic neuron A0 and postsynaptic neuron A1 and A2
<p>Different axonal conduction delays between every two neurons are applied as follows:<br> For instance, consider presynaptic neuron A0 and postsynaptic neurons A1 and A2 in Figure 2. If neuron A0 fires spike S0 in t = 9 and S1 in t = 11 and with a time step equal to 0.5 ms, S0 arrives to A1 in t = 13 and to A2 in t = 14 where S1 arrives to A1 in t = 15 and to A2 in t = 16. This is implemented using counters assigned to each spike which increases with each time step in the time window. If counters arrive to axonal delay values, the effect of spikes from presynaptic neuron applies to the post synaptic neurons.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 4: Pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons
<p>In each time step, axonal conduction delays between presynaptic neurons and postsynaptic neurons are examined whether they are equal to the elements of array I_S, to apply the respective spikes.<br> The pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons are shown in Figure 4.</p>
Dataset: Axonics, Inc. (AXNX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.