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125 results for “Brain atlas”
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
Open the record for dataset details and reuse information.
RESOLUTE atlas for brain PET/MR pseudo-CT generation
<p>Template and mask images for performing the <em>Region specific optimization of continuous linear attenuation coefficients based on UTE</em> (RESOLUTE) pseudo-CT generation approach. This dataset can be used in conjunction with an open-source C++ implementation of RESOLUTE (<a href="https://github.com/UCL/petmr-RESOLUTE">https://github.com/UCL/petmr-RESOLUTE</a>) for the Siemens mMR scanner.</p>
Brain Hierarchical Atlas 2 (BHA2)
<p>Elucidating the intricate relationship between the structure and function of the brain, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Magnetic Resonance Imaging (MRI) has helped in the understanding of this matter, with diffusion images providing information about structural connectivity (SC) and resting-state functional MRI revealing the functional connectivity (FC).</p> <p>Furthermore, the brain operates by discrete multiscale computations in both the time and spatial domains, in a way that is far from known (Churchland and Sejnowski, The MIT Press, 1994). To advance in the understanding of this puzzle, a dual structure-function hierarchical clustering strategy was proposed in (Diez et. al, SciRep, 2015), providing a common skeleton shared by structure and function. Here, we further extend this approach by:<br> 1. Fine-tuning the amount of matching between SC and FC via a free-parameter gamma. Specifically, when gamma is set to 0, SC is fully recovered, while when gamma is set to 1, FC is recovered. In between these extremes, a fusion scenario occurs, where both SC and FC contribute to the connectivity patterns. The raw data to generate the SC and FC matrices came from (Babayan et. al, Scientific Data, 2019), and can be downloaded from https://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html.<br> 2. Making use of brain-transcriptomic data to shed light on biological interpretability of brain-related diseases in the gamma-modulated multiscale structure-function correspondence.<br> 3. Providing to the scientific community open data of different scenarios of structure-function sharing and at different spatial scales, and open code to generate them in a MRI dataset.</p> <p>The dataset is organized in the following way:</p> <p>data<br> │ ├───iPA_nROIS [Different spatial scales 183, 391, 568, 729, 964, 1242, 1584, 1795 and 2165]<br> │ │ ├───iPA_nROIS.nii.gz [Brain parcellation image]<br> │ │ ├───iPA_nROIS.csv [MNI Coordinates and location of the brain parcellation ROIs]<br> │ │ ├───SC [Structural connectivity matrices]<br> │ │ ├───FC [Functional connectivity matrices]<br> │ │ ├───ts [Resting-state functional connectivity timeseries]<br> │ │ | ├───confounds [Confounds used to filter the timeseries]<br> │ │ ├───gamma-trees [Gamma-trees of nROIs levels]<br> │ │ ├───transcriptomics.csv [Transcriptomics' of each ROI]</p> <p>If you want to use this dataset, please cite:</p> <p><em>Antonio Jimenez-Marin, Ibai Diez, Asier Erramuzpe, Sebastiano Stramaglia, Paolo Bonifazi, Jesus M Cortes</em>. <strong>Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain</strong>. biorxiv. 2023. <a href="https://doi.org/10.1101/2023.08.04.551953">https://doi.org/10.1101/2023.08.04.551953</a></p>
Human Brain MRI Template and Myelin Atlas
<p>The structural template, quantitative myelin water imaging atlases, tissue segmentations, and regions of interest (ROIs) generated and analyzed for <em>An atlas for human brain myelin content throughout the adult life span</em></p> <p><a href="https://www.nature.com/articles/s41598-020-79540-3">https://www.nature.com/articles/s41598-020-79540-3</a></p>
An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain
<p>This archive contains the dataset produced by the Blue Brain Project (BBP) for improving the Common Coordinate Framework version 3 (CCFv3) mouse brain atlas from the Allen Institute for Brain Science (AIBS). The dataset nomenclature is aligned with AIBS standards, utilizing the Allen Reference Atlas (ARA) Nissl-stained volume sectioned in the coronal incidence (ARA NisslCOR) and the annotation file version 3 (ANNOTv3). Additional data were used, such as the AIBS Nissl-stained volume sectioned in the sagittal incidence (AIBS NisslSAG, Allen Mouse Brain Atlas ID 100042147) as well as a Waxholm (WAXH) Nissl-stained volume sectioned in the horizontal incidence (WAXH NisslHOR; https://www.nitrc.org/projects/incfwhsmouse).</p> <p>Here is a list of the files produced and shared below with their descriptions:</p> <p><strong>ara_bbp_nisslCOR_25 - 10</strong>: ARA Nissl-stained volume sectioned in the coronal incidence at 25 and 10 μm isotropic resolution accurately aligned in the CCFv3.</p> <p><strong>arav3a_bbp_nisslCOR_25 - 10</strong>: ARA Nissl-stained volume sectioned in the coronal incidence at 25 and 10 μm isotropic resolution accurately aligned in the CCFv3 and extended for covering the entire brain.</p> <p><strong>annotv3am_bbp_manual_25</strong>: Expert manual delineation of the extended tissue based on ARAv3aBBP NisslCOR at 25 isotropic resolution as well as of the granular and molecular layers in the cerebellum.</p> <p><strong>annotv3a_bbp_25 - 10</strong>: Extended CCFv3 annotation at 25 and 10 μm isotropic resolution covering the entire mouse brain plus including new granular and molecular layers in all cerebellar lobules, assessed using ANNOTv3am.</p> <p><strong>annotv3c_bbp</strong>: Extended CCFv3aBBP annotation covering the mouse central nervous system including spinal cord as well as barrel columns in the isocortex.</p> <p><strong>aibs_bbp_nisslSAG_25</strong>: AIBS sagittal Nissl-stained volume aligned in the CCFv3aBBP at 25 μm isotropic resolution.</p> <p><strong>waxh_bbp_nisslHOR_25</strong>: WAXH horizontal Nissl-stained volume aligned in the CCFv3aBBP at 25 μm isotropic resolution.</p> <p><strong>annotation_bbp_atlas_pipeline_25</strong>: Annotation file from the Blue Brain cell atlas pipeline including the extended version annotv3a_bbp_25, plus some additional sublayers such as layer 2 and layer 3, as well as the barrel columns in the isocortex.</p> <p><strong>hierarchy_bbp_atlas_pipeline</strong>: Hierarchy file attached to the annotation_bbp_atlas_pipeline_25 version.</p> <p><strong>average_nissl_init_25_v3a_CBcorrected</strong>: Average Nissl-stained template composed of the average of arav3a_bbp_nisslCOR_25, aibs_bbp_nisslSAG_25, and waxh_bbp_nisslHOR_25 and including some automated corrections of the artifacts in the cerebellum. This was used as a reference and initialization for building the average Nissl-stained template.</p> <p><strong>average_nissl_template</strong>: Symmetric (symmetric_full) and non symmetric (nissl_average_full) averaged Nissl-stained template in the CCFv3aBBP, as well as the number of occurences per voxel (frequency) in the averaging process.</p> <p><strong>QuickNII-CCFv3a-extended</strong>: Extended CCFv3a atlas file compatible with QuickNII software.</p> <p><strong>VisuAlign-v0.91</strong>: Extended CCFv3a atlas file compatible with VisuAlign software.</p> <p>An additional video (<strong>FullBrainAtlas_bbp</strong>) is provided in that archive, presenting the different mouse brain annotations from AIBS to BBP ones, as well as the BBP computed neuron distribution among the entire mouse brain colored by regions given AIBS standards. The code for creating the data in the video is accessible at https://github.com/favreau/BioExplorer/tree/master/bioexplorer%2Fpythonsdk%2Fnotebooks%2Fccfv3.</p> <p>For accessing to the code related to that work, please go to the corresponding GitHub repository: https://github.com/BlueBrain/ccfv3a-extended-atlas.</p> <p>--</p> <p>Citation:</p> <p>Piluso, S., Verasztó, C., Carey, H., Delattre, É., L’Yvonnet, T., Colnot, É., Romani, A., Bjaalie, J. G., & Keller, D. (2024). An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain. Zenodo. <a href="https://doi.org/10.5281/zenodo.13640418" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.13640418</a></p> <p>--</p> <p>Reference paper:</p> <p>Sébastien Piluso, Csaba Verasztó, Harry Carey, Émilie Delattre, Thibaud L’Yvonnet, Éloïse Colnot, Armando Romani, Jan G. Bjaalie, Henry Markram, Daniel Keller; An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain. <em>Imaging Neuroscience</em> 2025; doi: <a href="https://doi.org/10.1162/imag_a_00565" target="_blank" rel="noopener">https://doi.org/10.1162/imag_a_00565</a></p> <p>--</p> <p>Funding:</p> <p><em>This study 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. This project/research has received funding from the European Union’s Research and Innovation Program Horizon Europe under Grant Agreement no. 101147319 (EBRAINS 2.0).</em></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>
A Spatio-temporal Atlas of the Developing Fetal Brain with Spina Bifida Aperta
<p>This version contains two zipped folders.</p> <ol> <li><a href="https://zenodo.org/api/files/c84cc018-9d2d-4adb-9558-9ab96649c922/spina_bifida_atlas.zip">spina_bifida_atlas.zip</a> contains a copy of our spina bifida aperta fetal brain atlas.<br> This folder is available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication), as indicated in the LICENSE file in this folder.<br> This is the same version of the atlas as the one available on synapse (<a href="https://www.synapse.org/#!Synapse:syn25887675/wiki/611424">https://www.synapse.org/#!Synapse:syn25887675/wiki/611424</a>, DOI: 10.7303/syn25887675).</li> <li><a href="https://zenodo.org/api/files/c84cc018-9d2d-4adb-9558-9ab96649c922/LucasFidon/spina-bifida-MRI-atlas-0.1.0.zip?versionId=31751dfa-7b17-4c84-89d5-2769d648eed8">LucasFidon/spina-bifida-MRI-atlas-0.1.0.zip</a> is a copy of the code that was used to compute the fetal brain atlas for spina bifida aperta in this repository.<br> This folder is available under BSD-3-Clause license, archived from GitHub, as indicated in the LICENSE file in this folder.</li> </ol> <p><strong>How to cite:</strong><br> If you find the data in this folder useful for your research please cite:</p> <p>L. Fidon, E. Viola, N. Mufti, A. L. David, A. Melbourne, P. Demaerel, S. Ourselin, T. Vercauteren, J. Deprest, M. Aertsen. A Spatio-temporal Atlas of the Developing Fetal Brain with Spina Bifida Aperta, 2021.</p>
Quail (Coturnix japonica) brain MRI template and whole-brain atlas
<p>A population average MRI brain template computed from 20 male Japanese Quails and a manually segmented atlas containing 194 regions. </p> <p>In this Version 2:</p> <ul> <li>the nomenclature in the file <em>siwiaszczyk_LUT-ITK-SNAP_v2.txt</em> was updated</li> <li>one slice of one region was completed in the file <em>siwiaszczyk_atlas_v2.nii.gz.</em></li> </ul>
High-Resolution Heterogeneous Digital PET [18F]FDG Brain Phantom based on the BigBrain Atlas
<p>We present the design of a digital phantom that tries to overcome the problems of the current PET digital brain phantoms, particularly for the simulation of simultaneous PET-MRI data sets. We propose a new brain digital brain phantom based on the BigBrain atlas, a free, publicly available tool that provides considerable neuroanatomical insight into the human brain with an ultrahigh-resolution 3D model of a human brain at nearly cellular resolution of 20 micrometers. We used the histology maps, the classified tissue maps and the MRI image of the BigBrain atlas, as well as the Hammersmith atlas and a PET [18F]FDG template as inputs to create an instance of this ultra high-resolution heterogeneous PET-MRI phantom.</p> <p>Full details of this phantom in Medical Physics: "Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas", <a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218.</a></p> <p>You can find codes examples for reading the data at https://github.com/mabelzunce/PETBrainPhantoms </p> <p>Please cite this paper if you use this phantom in your work:</p> <p>Belzunce, M.A. and Reader, A.J. (2020), Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas. Med. Phys., 47: 3356-3362. doi:<a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218</a></p>
Multilevel atlas comparisons reveal divergent evolution of the primate brain
<p>Nifti files of 20 mammalian atlases modified into a Common Multilevel Segmentation.</p> <p>(See Figure 1 in Multilevel atlas comparisons reveal divergent evolution of the primate brain; https://www.pnas.org/doi/full/10.1073/pnas.2202491119#sec-3)</p> <p>These nifti files are based on the brain atlases from 18 mammalian species, that were published between the years 2013 and 2021 (see list).</p> <p>The Python script to re-segment the "original" atlases into the modified version (that is shared here) is also available:</p> <p>see Modify_atlases.py</p> <p>Each species folder contains 5 nifti files: 1 for each level of segmentation and 1 for the brain segmentation.</p> <p>The other txt files are the volumetric output extracted using AFNI on each nifti file.</p> <p>Please read the Readme.txt file to credit and cite accordingly all the authors.</p> <p> </p> <p> </p> <p> </p> <p> </p>
MRI Brain Template and Atlas of the Mouse Lemur Primate Microcebus murinus
<p>MRI template and 120-region atlas for the mouse lemur primate Microcebus murinus.<br> <br> Generated from 34 animals aged 15-58 months old scanned at 7T using a T2-weighted sequence, resolution 115 × 115 × 230 µm. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; <a href="https://sammba-mri.github.io/">https://sammba-mri.github.io/</a>). The template was up-sampled to 91 µm isotropic for hand-segmentation of structures, and also used to create probability maps of grey matter, white matter and cerebro-spinal fluid.</p> <p>if used for publication please cite: </p> <p><strong>A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy</strong><br> Nachiket A Nadkarni, Salma Bougacha, Clément Garin, Marc Dhenain, Jean-Luc Picq<br> Jan 2019<br> <strong>NeuroImage</strong> 185, 85-95<br> DOI: 10.1016/J.NEUROIMAGE.2018.10.010<br> <a href="https://www.sciencedirect.com/science/article/pii/S1053811918319694">https://www.sciencedirect.com/science/article/pii/S1053811918319694</a></p>
A magnetic resonance multi-atlas for the neonatal rabbit brain - Dataset
<p>Dataset related to the Neuroimage paper https://doi.org/10.1016/j.neuroimage.2018.06.029. Download links, documentation and code for the manipulation are available from the software repository https://github.com/gift-surg/SPOT-A-NeonatalRabbit</p>
An MRI-Derived Neuroanatomical Atlas of the Fischer 344 Rat Brain for Automated Anatomical Segmentation
<p>This version of the dataset described in: <a href="https://www.biorxiv.org/content/10.1101/743583v2">https://www.biorxiv.org/content/10.1101/743583v2</a>, is outdated. Please refer to https://doi.org/10.5281/zenodo.3555556 for the current version and all future editions of the Fischer 344 neuroanatomical atlas.</p>
Ginkgo Chauvel's left and right superficial white matter atlas of the chimpanzee brain
<p><strong>Superficial Chauvel's chimpanzee white matter atlas.</strong></p> <p>The left and right superficial white matter atlas of the chimpanzee brain was built upon a cohort of 39 in vivo chimpanzees magnetic resonance imaging (MRI) scans shared by the Pr. William D. Hopkins, registered on a template space (Juna.chimp template from Vickery et al. 2020). The construction of this atlas is based on the analysis of the anatomical and diffusion MRI dataset using the tractography and fiber clustering tools available from the Ginkgo toolbox (CEA, NeuroSpin, BAOBAB, GAIA, Ginkgo Team, <a href="https://framagit.org/cpoupon/gkg">https://framagit.org/cpoupon/gkg</a>). The atlas can be visualized using the BrainVISA/Anatomist viewer available at <a href="https://brainvisa.info/web/download.html">https://brainvisa.info/web/download.html</a>.</p> <p><br> The left hemisphere atlas is composed of 422 superficial white matter bundles and the right hemisphere atlas of 400 superficial white matter bundles.<br> <br> The 38 cortical regions considered for the left and right hemispheres were : the anterior/middle/posterior superior frontal gyrus (aSFG/mSFG/pSFG) ; the anterior and posterior middle frontal gyrus (aMFG/pMFG) ; the anterior/middle/posterior inferior frontal gyrus (aIFG/mIFG/pIFG) ; the medial lateral orbitofrontal cortex (mOFC/ lOFC) ; the superior, middle, inferior precentral gyrus (sPrCG / mPrCG / iPrCG) ; the Paracentral Lobule (PCL) ; the anterior and posterior insula (alns / plns) ; the anterior, posterior superior temporal gyrus (aSTG/pSTG) ; the anterior, posterior middle temporal gyrus (aMTG/pMTG) ; the anterior and posterior inferior temporal gyrus (aITG, pITG) ; the anterior and posterior fusiform gyrus (aFFG/pFFG) ; the superior, middle and inferior postcentral gyrus (sPoCG/mPoCG/iPoCG) ; the superior parietal lobule (SPL) ; the supramarginal gyrus (SMG) ; the angular gyrus (AnG) ; the Precuneus (PCun) ; the cuneus (Cun) ; the Lingual Gyrus (LG) ; the superior, middle, inferior occipital gyrus (sOG / mOG/ iOG) ; the entorhinal Cortex (EnC) ; the parahippocampal gyrus (PHC).<br> <br> The atlas is provided using the Anatomist *.bundles/*.bundlesdata format for which metainformation can be found in the *.bundles file among which:<br> - the labels of the different white matter bundles ('labels' entry) following the syntactic rule "<right/left>_<roi1>_<roi2>_<clusterId>",<br> - the number of streamlines populating each white matter bundle ('curve3d_counts' entry), in the same order as the 'labels' key,<br> - the total number of white matter bundles ('item_count' entry),<br> - the total number of streamlines ('curves_count' entry)</p> <p> </p>
Ginkgo Chauvel's deep white matter atlas of the human brain
<p><strong>Deep Chauvel's human white matter atlas.</strong></p> <p>The deep white matter atlas of the human brain was built upon a cohort of 39 in vivo human magnetic resonance imaging (MRI) scans shared by the Human Connectome Project (HCP), registered on a template space (the MNI ICBM152 2009c non-linear asymmetric template). The construction of this atlas is based on the analysis of the anatomical and diffusion MRI dataset using the tractography and fiber clustering tools available from the Ginkgo toolbox (CEA, NeuroSpin, BAOBAB, GAIA, Ginkgo Team, <a href="https://framagit.org/cpoupon/gkg">https://framagit.org/cpoupon/gkg</a>). The atlas can be visualized using the BrainVISA/Anatomist viewer available at <a href="https://brainvisa.info/web/download.html">https://brainvisa.info/web/download.html</a>.</p> <p><br> This atlas is composed of 39 white matter bundles including :<br> - symmetrically on both hemispheres, the anterior, superior and posterior thalamic radiations (optic radiations), the arcuate, dorsal and ventral cingulum, the cortico-spinal tract, the fornix, the frontal aslants, the inferior fronto-occipital fascicle, the inferior longitudinal fasciculus, the middle longitudinal fascicle, the optic radiations, the uncinate fascicle and the visual occipito-temporal fibers (also called ventral visual stream),<br> - interhemispheric bundles such as the anterior commissure and the Witelson's subdivisions of the corpus callosum (I, II, III, IV, V, VI, VII),<br> - cerebellar bundles, such as the hypothamic-subthalamic fibers and the cortico-ponto-cerebellar fibers,<br> The atlas is provided using the Anatomist *.bundles/*.bundlesdata format for which meta-information can be found in the *.bundles file among which:<br> - the labels of the different white matter bundles ('labels' entry),<br> - the number of streamlines populating each white matter bundle ('curve3d_counts' entry), in the same order as the 'labels' key,<br> - the total number of white matter bundles ('item_count' entry),<br> - the total number of streamlines ('curves_count' entry)</p>
Ginkgo Chauvel's left and right superficial white matter atlas of the human brain
<p><strong>Superficial Chauvel's human white matter atlas.</strong></p> <p>The left and right superficial white matter atlases of the human brain were built upon a cohort of 39 in vivo human magnetic resonance imaging (MRI) scans from the Human Connectome Project (HCP), registered on a template space (the MNI ICBM152 2009c non-linear<br> asymmetric template). The construction of this atlas is based on the analysis of the anatomical and diffusion MRI dataset using the tractography and fiber clustering tools available from the Ginkgo toolbox (CEA, NeuroSpin, BAOBAB, GAIA, Ginkgo Team, <a href="https://framagit.org/cpoupon/gkg">https://framagit.org/cpoupon/gkg</a>). The atlas can be visualized using the BrainVISA/Anatomist viewer available at <a href="https://brainvisa.info/web/download.html">https://brainvisa.info/web/download.html</a>.<br> The left hemisphere atlas is composed of 733 superficial white matter bundles and the right hemisphere atlas of 632 superficial white matter bundles.</p> <p><br> <br> The 38 cortical regions considered for the left and right hemispheres were : the anterior/middle/posterior superior frontal gyrus (aSFG/mSFG/pSFG) ; the anterior and posterior middle frontal gyrus (aMFG/pMFG) ; the anterior/middle/posterior inferior frontal gyrus (aIFG/mIFG/pIFG) ; the medial lateral orbitofrontal cortex (mOFC/ lOFC) ; the superior, middle, inferior precentral gyrus (sPrCG / mPrCG / iPrCG) ; the Paracentral Lobule (PCL) ; the anterior and posterior insula (alns / plns) ; the anterior, posterior superior temporal gyrus (aSTG/pSTG) ; the anterior, posterior middle temporal gyrus (aMTG/pMTG) ; the anterior and posterior inferior temporal gyrus (aITG, pITG) ; the anterior and posterior fusiform gyrus (aFFG/pFFG) ; the superior, middle and inferior postcentral gyrus (sPoCG/mPoCG/iPoCG) ; the superior parietal lobule (SPL) ; the supramarginal gyrus (SMG) ; the angular gyrus (AnG) ; the Precuneus (PCun) ; the cuneus (Cun) ; the Lingual Gyrus (LG) ; the superior, middle, inferior occipital gyrus (sOG / mOG/ iOG) ; the entorhinal Cortex (EnC) ; the parahippocampal gyrus (PHC).<br> <br> The atlas is provided using the Anatomist *.bundles/*.bundlesdata format for which metainformation can be found in the *.bundles file among which:<br> - the labels of the different white matter bundles ('labels' entry) following the syntactic rule "<right/left>_<roi1>_<roi2>_<clusterId>",<br> - the number of streamlines populating each white matter bundle ('curve3d_counts' entry), in the same order as the 'labels' key,<br> - the total number of white matter bundles ('item_count' entry),<br> - the total number of streamlines ('curves_count' entry)</p> <p> </p> <p> </p>
A spatial atlas of the complement system uncovers unique expression patterns in postnatal brain development in mice
Open the record for dataset details and reuse information.
Brain Transcriptome Single-cell (BTS) Atlas: Anndata, Seurat Object, CellTypist model, and Disorder Risk Geneplot
<p>Brain Transcriptome Single-cell Atlas (BTS) Anndata, Seurat object, and Celltypist model for further use of the atlas. The Celltypist model can be utilized to accurately annotate cell types in new datasets based on the atlas. Plots illustrating the expression profile for 3,380 neurological disorder risk genes across the atlas are also uploaded. Further availability for the data can be requested by the corresponding author.<br><br>This dataset is published in Kim, S., Lee, J., Koh, I.G. <em>et al.</em> An integrative single-cell atlas for exploring the cellular and temporal specificity of genes related to neurological disorders during human brain development. <em>Exp Mol Med</em> <strong>56</strong>, 2271–2282 (2024). https://doi.org/10.1038/s12276-024-01328-6</p>
Ultra-high-resolution diffusion MRI atlas of CD1 embryonic mouse brains at E10.5-E15.5.
<p>This dataset is assciated with the paper "A Spatiotemporal Continuum of Embryonic Mouse Brain Development Built on Diffusion MR Microscopy for Probing Dynamic Gene-Neuroanatomy" on PNAS. For each stage, average FA (fractional anisotropy) and DEC (directionally encoded colormap) images (n=5) are provided. <br> <br> </p>
Dataset of ""Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution"
<p>Dataset of <em>Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution</em> by Sara Arganda, Ignacio Arganda-Carreras, Darcy G. Gordon, Andrew P. Hoadley, Alfonso Pérez-Escudero, Martin Giurfa and James F. A. Traniello.</p> <p>In this dataset, we are presenting:</p> <ul> <li>10 confocal brain images from <em>Pheidole spadonia </em>minors (in the original confocal TIFF format and in the open NRRD format), with manually segmented labels of 8 subregions (Optic Lobes, OL; Antennal Lobes, AL; Mushroom Body Medial Calyx, MB-MC; Mushroom Body Lateral Calyx, MB-LC; Mushroom Body Peduncle, MB-P; Central Complex, CX; Subesophageal zone, SEZ; and Rest of Central Brain, ROCB – in NRRD format) from one expert annotator.</li> <li>12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from one expert annotator.</li> <li>5 confocal brain images from <em>Pheidole spadonia </em>minors (“test brains”), with five sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from three expert annotators (one set from annotator 1, one set from annotator 2 and three sets from annotator 3, to evaluate inter and intra person differences).</li> <li>1 group-wise template generated from the 10 confocal brain images from <em>Pheidole spadonia </em>minors, with three sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>5 group-wise templates generated from the 9 confocal brain images from <em>Pheidole spadonia </em>minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>1 group-wise template generated from 12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>7 sets of automatic labels for the 5 “test brains”: 3 sets of “Direct Labels”, 3 sets of “Consensus Labels”, 1 set of “Multispecies Template Labels”.</li> </ul> <p>Brain of minor workers were dissected from the ant head capsule in ice cold HEPES-buffered saline and were fixed and immunohistochemically stained using SYNORF1 (a monoclonal <em>Drosophila</em> synapsin I antibody obtained from the Developmental Studies Hybridoma Bank, catalog 3C11) and secondarily stained using Alexa Fluor 488 for visualization of neuropil (slightly modified from Ott, 2008). Later, brains were mounted in methyl salicylate and imaged on an Olympus Fluoview BX50 laser scanning confocal microscope with a ×20 objective at a resolution of ~0.7 × 0.7 × 5µm/voxel. All brain tissue manipulation, staining and recording was performed by Darcy G. Gordon. Brain images were obtained in TIFF format by the confocal microscope and then opened and saved as Amira Mesh (.am) stack images in Amira (version 6.0). Manual segmentation of each brain was done using Amira (version 6.0 or 2019.2). Labels were traced on eight compartments in only one brain hemisphere, except for the CX, SEZ and ROCB, which lack a clear subdivision between hemispheres. Brain grey image stacks and labels were transformed to NRRD format for template construction using the Fiji plugin SaveAsGzipNrrd<a href="#_ftn1">[1]</a>. Volume and volume similarity of labels were calculated using the Fiji toolbox MorphoLibJ<a href="#_ftn2">[2]</a>.</p> <p><strong>Acknowledgements: </strong>We thank Ming Huang (from Dr. Diana Wheeler’s laboratory) who kindly provided access to colonies from four species of the hyperdiverse ant genus <em>Pheidole</em> (<em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana </em>and <em>P. obtusospinosa</em>). This research was supported by National Science Foundation grants IOS 1354291 and IOS 1953393 to JT, a Marie Skłodowska-Curie Individual Fellowship BrainiAnts-660976 and Ayudas destinadas a la atracción de talento investigador a la Comunidad de Madrid en centros de I+D. This work is supported in part by the University of the <a href="https://www.sciencedirect.com/topics/engineering/basque-country">Basque Country</a> UPV/EHU grant GIU19/027.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> https://github.com/iarganda/tefor</p> <p><a href="#_ftnref2">[2]</a> https://imagej.net/plugins/morpholibj</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.