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126 results for “brain atlas”
Supplementary Data Files from: A lamprey neural cell type atlas illuminates the origins of the vertebrate brain
<p><strong>Supplementary Data </strong><strong>1</strong></p> <p>Lamprey genome custom annotation files.</p> <p> </p> <p><strong>Supplementary Data 2</strong></p> <p><em>In situ</em> images produced in this study.</p>
BrainSTEM: A multi-resolution fetal brain atlas to assess the fidelity of human midbrain cultures
<p>This upload contains data objects associated with our paper "BrainSTEM: A multi-resolution fetal brain atlas to assess the fidelity of human midbrain cultures".</p> <p>In particular, this upload contains the in-house single-cell dataset (toh.inhouse) and 12 single-cell datasets from published literatures, totalling 13 query datasets, as R object files.</p> <p>Detailed information about the publicly available query datasets can be found in the Supplementary Table 6 of the paper abovementioned.</p> <p> </p> <p> </p>
Cell-Type Resolved Protein Atlas of Brain Lysosomes Identifies SLC45A1-Associated Disease as a Lysosomal Disorder: Untargeted Metabolomics and Lipidomics Data Deposition
<p>Raw data files used for untargeted metabolomics and lipidomics in the manuscript "Cell-Type Resolved Protein Atlas of Brain Lysosomes Identifies SLC45A1-Associated Disease as a Lysosomal Disorder".</p> <p>The PDF document <strong>(Data_Deposition_Naming_Info.pdf)</strong> contains information on the file naming system.</p>
Ginkgo Chauvel's deep white matter atlas of the chimpanzee brain
<p><strong>Deep Chauvel's chimpanzee white matter atlas.</strong></p> <p><br> The deep 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> This atlas is composed of 42 white matter bundles including :<br> - symmetrically on both hemispheres, the anterior, superior and posterior thalamic 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,<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, the cortico-ponto-cerebellar fibers, and the parallel fibers,<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),<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>
Genomic atlas of the human proteome from brain, CSF and plasma: Improvement with TOPMed imputed genomics
<p>Abstract</p><p>Comprehensive expression quantitative trait loci (eQTL) studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated GWAS loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein QTL (pQTL) in neurologically relevant tissues. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high-quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in CSF, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 pQTL in brain, 30 pQTL in CSF, and 22 pQTL in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These pQTL findings are assessable through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer (ONTIME) for use by the scientific community.</p>
Data from: Mapping and analysis of the connectome of sympathetic premotor neurons in the rostral ventrolateral medulla of the rat using a volumetric brain atlas
Spinally projecting neurons in the rostral ventrolateral medulla (RVLM) play a critical role in the generation of vasomotor sympathetic tone and are thought to receive convergent input from neurons at every level of the neuraxis; the factors that determine their ongoing activity remain unresolved. In this study we use a genetically restricted viral tracing strategy to definitively map their spatially diffuse connectome. We infected bulbospinal RVLM neurons with recombinant rabies variant that drives reporter expression in monosynaptically connected input neurons and mapped their distribution using a MRI-based volumetric atlas and a novel image alignment and visualization tool that efficiently translates the positions of neurons captured in conventional photomicrographs to Cartesian coordinates. We identified prominent inputs from well-established neurohumoral and viscero-sympathetic sensory actuators, medullary autonomic and respiratory subnuclei, and supramedullary autonomic nuclei. The majority of inputs lay within the brainstem (88 – 94%), and included putative respiratory neurons in the pre-Bötzinger Complex and post-inspiratory complex that are therefore likely to underlie respiratory-sympathetic coupling. We also discovered a substantial and previously unrecognized input from the region immediately ventral to nucleus prepositus hypoglossi. In contrast, RVLM sympathetic premotor neurons were only sparsely innervated by suprapontine structures including the paraventricular nucleus, lateral hypothalamus, periaqueductal grey and superior colliculus, and we found almost no evidence of direct inputs from the cortex or amygdala. Our approach can be used to quantify, standardize and share complete neuroanatomical datasets, and therefore provides researchers with a platform for presentation, analysis and independent analysis of connectomic data.
Enhanced and unified anatomical labeling for a common mouse brain atlas
<p>Anatomical atlases in standard coordinates are necessary for the interpretation and integration of research findings in a common spatial context. However, the two most-used mouse brain atlases, the Franklin and Paxinos (FP) and the common coordinate framework (CCF) from the Allen Institute for Brain Science, have accumulated inconsistencies in anatomical delineations and nomenclature, creating confusion among neuroscientists. To overcome these issues, here we adopt the FP labels into the CCF to merge two labels in the single atlas framework. We use cell type specific transgenic mice and an MRI atlas to adjust and further segment our labels. Moreover, detailed segmentations are added to the dorsal striatum using cortico-striatal connectivity data. Lastly, we digitize our anatomical labels based on the Allen ontology, create a web-interface for visualization, and provide tools for comprehensive comparisons between the CCF and FP labels. Our open-source labels signify a key step towards a unified mouse brain atlas.</p>
Source code and data for Osetrova et al. paper "Lipidome atlas of the adult human brain"
Open the record for dataset details and reuse information.
Rat head with brain atlas
SciDraw upload
Genomic atlas of the human proteome from brain, CSF and plasma: Improvement with TOPMed imputed genomics
<p>Abstract</p> <p>Comprehensive expression quantitative trait loci (eQTL) studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated GWAS loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein QTL (pQTL) in neurologically relevant tissues. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high-quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in CSF, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 pQTL in brain, 30 pQTL in CSF, and 22 pQTL in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These pQTL findings are assessable through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer (ONTIME) for use by the scientific community.</p>
Genomic atlas of the human proteome from brain, CSF and plasma: Improvement with TOPMed imputed genomics
<p>Abstract</p> <p>Comprehensive expression quantitative trait loci (eQTL) studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated GWAS loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein QTL (pQTL) in neurologically relevant tissues. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high-quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in CSF, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 pQTL in brain, 30 pQTL in CSF, and 22 pQTL in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These pQTL findings are assessable through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer (ONTIME) for use by the scientific community.</p>
Data from: Mapping and analysis of the connectome of sympathetic premotor neurons in the rostral ventrolateral medulla of the rat using a volumetric brain atlas
Open the record for dataset details and reuse information.
Enhanced and unified anatomical labeling for a common mouse brain atlas
Open the record for dataset details and reuse information.
Transcriptomic and open chromatin atlas of high-resolution anatomical regions in the rhesus macaque brain
GEO Series GSE128537. Macaca mulatta. 449 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
A single-cell atlas of human brain middle temporal gyrus reveals sex-specific and cell-type-specific gene expression regulation in Alzheimer’s disease
GEO Series GSE188545. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Crnic Institute Human Trisome Project - Trisomy 21 Model Atlas: PolyA RNA-seq from 9-month old mouse brain cortex tissue
GEO Series GSE272690. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Single cell transcriptome atlas of the Drosophila larval brain
GEO Series GSE134722. Drosophila melanogaster. 7 samples. Type: Expression profiling by high throughput sequencing.
A Cellular Atlas of the Down Syndrome Brain Identifies Accelerated Oligodendrocyte Precursor Cell Senescence
GEO Series GSE225554. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment.
GEO Series GSE128855. Mus musculus. 35 samples. Type: Expression profiling by high throughput sequencing.
Melanoma Brain Metastasis Atlas [single-cell/nuclei RNA-sequencing (sc/snRNA-seq)]
GEO Series GSE200218. Homo sapiens. 32 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.