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281 results for “Whole brain”
Data from: Diffantom: whole-brain diffusion MRI phantoms derived from real datasets of the Human Connectome Project
Diffantom is a whole-brain diffusion MRI (dMRI) phantom publicly available through the Dryad Digital Repository (doi:10.5061/dryad.4p080). The dataset contains two single-shell dMRI images, along with the corresponding gradient information, packed following the BIDS standard (Brain Imaging Data Structure, Gorgolewski et al., 2015). The released dataset is designed for the evaluation of the impact of susceptibility distortions and benchmarking existing correction methods. In this Data Report we also release the software instruments involved in generating diffantoms, so that researchers are able to generate new phantoms derived from different subjects, and apply these data in other applications like investigating diffusion sampling schemes, the assessment of dMRI processing methods, the simulation of pathologies and imaging artifacts, etc. In summary, Diffantom is intended for unit testing of novel methods, cross-comparison of established methods, and integration testing of partial or complete processing flows to extract connectivity networks from dMRI.
Supplemental Data - Intact Drosophila Whole Brain Cellular Quantitation reveals Sexual Dimorphism
<p>Supplemental Figures, Datasets and Movies supporting the manuscript -</p> <p><strong>Intact Drosophila Whole Brain Cellular Quantitation reveals Sexual Dimorphism</strong>. Wei Jiao, Gard Spreemann, Evelyne Ruchti, Soumya Banerjee, Ying Shi, R. Steven Stowers, Kathryn Hess and Brian D. McCabe<br> <br> Brain Mind Institute, EPFL - Swiss Federal Institute of Technology Lausanne, Switzerland - <a href="https://mccabelab.org">https://mccabelab.org</a></p>
Supplementary data to 'acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset'
<p>Supplemental results of our analysis</p>
Figure 1 from: Rane S, Jolly E, Park A, Jang H, Craddock C (2017) Developing predictive imaging biomarkers using whole-brain classifiers: Application to the ABIDE I dataset. Research Ideas and Outcomes 3: e12733. https://doi.org/10.3897/rio.3.e12733
Figure 1 - Weights (β-coefficients) for voxel-wise ReHo features from a support vector machine (SVM) classifier mapped on the glass brain to separate individuals with and without Autism Spectrum Disorder
3D ground truth annotations of cleared whole mouse brain nuclei imaged with a mesoSPIM system
<p>Please see Achard et al. 2024 for Data Card and other information.</p>
FlyWire Whole-brain Connectome Connectivity Data
<p>This repository contains the connectivity data for the FlyWire Connectome release. Currently, the latest release is version 783 (see also codex.flywire.ai).</p> <p>The synapses represent a combination of four different data releases, which are combined by the FlyWire whole-brain connectome release. The synapses (as points in space) were detected and published by <a href="https://www.nature.com/articles/s41592-021-01183-7">Buhmann et al., 2021</a> who made use of a cleft segmentation produced by <a href="https://link.springer.com/chapter/10.1007/978-3-030-00934-2_36">Heinrich et al., 2018</a>. The neurotransmitter for these synapses was then predicted and released by <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)00307-6">Eckstein, Bates et al., 2024</a>. The segmentation and neuron IDs (= root IDs) were proofread by the FlyWire consortium and are released by <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a> as part of the <a href="https://www.nature.com/collections/hgcfafejia">FlyWire connectome paper package</a>. </p> <p>Because multiple methods were involved in the production of this resource, the description of the methods is distributed across these manuscripts. We provide a summary in Dorkenwald et al., 2024 (Methods->Synaptic connections).</p> <p>Some of the files made available use feather as a file format. See a code example for reading these files, including a chunk-wise streaming to handle the large file.</p> <h3>flywire_synapses_783.feather</h3> <p>a pandas dataframe with all ~130 million synapses, their locations, neurotransmitter predictions and pre and postsynaptic partners (=root ids). This table contains all synapses that passed the thresholds (see methods section in <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a>), but not all synapses were associated with proofread neurons (e.g., see Discussion->Limitations of our reconstruction in <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a>). Hence, not all root IDs in this table will have a match in the proofread root IDs array. This table is provided for completeness, and to allow calculations about total synaptic input and output of neurons independent of other limitations. </p> <p>Columns:</p> <ul> <li>id: synapse ID</li> <li>pre_pt_root_id: presynaptic neuron ID</li> <li>post_pt_root_id: postsynaptic neuron ID</li> <li>connection_score: score assigned by Buhmann et al.; higher is better. We did not use this score to threshold synapses in any analysis</li> <li>cleft_score: score derived from the cleft segmentation by Heinrich et al.; higher is better. We used a threshold of 50 for all analyses and the released dataset. Synapses with lower score are not made available but can be made available on demand.</li> <li>gaba: probability for neurotransmitter=GABA</li> <li>ach: probability for neurotransmitter=Acetylcholine</li> <li>glut: probability for neurotransmitter=Glutamate</li> <li>oct: probability for neurotransmitter=Octopamine</li> <li>ser: probability for neurotransmitter=Serotonin</li> <li>da: probability for neurotransmitter=Dopamine</li> <li>neuropil: the name of the neuropil associated with this synapse. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>post_pt_position_{x,y,z}: Coordinate within the postsynaptic neuron (synapses were identified with two points, one in each neuron). Coordinates are in nanometers.</li> <li>pre_pt_position_{x,y,z}: Coordinate within the presynaptic neuron (synapses were identified with two points, one in each neuron). Coordinates are in nanometers.</li> </ul> <p> </p> <h3>per_neuron_neuropil_count_post_783.feather</h3> <p>a pandas dataframe containing the number of postsynapses per neuropil and segment id, i.e. this is a summarized version of <em>flywire_synapses_783.feather</em></p> <p>Columns:</p> <ul> <li>post_pt_root_id: segment ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For a mapping to long names see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>Count: number of synapses for this segment ID and neuropil </li> </ul> <h3> </h3> <h3>per_neuron_neuropil_count_pre_783.feather</h3> <p>a pandas dataframe containing the number of presynapses per neuropil and segment id, i.e. this is a summarized version of <em>flywire_synapses_783.feather</em></p> <p>Columns:</p> <ul> <li>pre_pt_root_id: segment ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>Count: number of synapses for this segment ID and neuropil </li> </ul> <h3> </h3> <h3>proofread_root_ids_783.npy</h3> <p>an array of all proofread neuron ids (=root ids)</p> <p> </p> <h3>proofread_connections_783.feather</h3> <p>a pandas dataframe containing the proofread subset from <em>flywire_synapses_783.feather </em>and summarized per neuron-neuron pair and neuropil, i.e. this table contains one entry per neuron-neuron pair and neuropil if there is 1 or more synapses for a given combination</p> <p>Columns:</p> <ul> <li>pre_pt_root_id: presynaptic neuron ID</li> <li>post_pt_root_id: postsynaptic neuron ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>syn_count: number of synapses between these two neurons in this neuropil</li> <li>gaba_avg: average probability across the synapses for neurotransmitter=GABA</li> <li>ach_avg: average probability across the synapses for neurotransmitter=Acetylcholine</li> <li>glut_avg: average probability across the synapses for neurotransmitter=Glutamate</li> <li>oct_avg: average probability across the synapses for neurotransmitter=Octopamine</li> <li>ser_avg: average probability across the synapses for neurotransmitter=Serotonin</li> <li>da_avg: average probability across the synapses for neurotransmitter=Dopamine</li> </ul> <p> </p> <h3>Code for reading and streaming feather files</h3> <p>Read feather files with pandas:</p> <p><code>import pandas as pd</code></p> <p><code>df = pd.read_feather(path)</code></p> <p> </p> <p>Stream large feather files in chunks:</p> <p><code>import pyarrow.feather as feather</code></p> <p><code>table = feather.read_table(path)</code></p> <p><code># Total number of rows in the Feather file</code><br><code>num_rows = table.num_rows</code></p> <p><code># Define chunk size</code><br><code>chunk_size = 1000</code></p> <p><code># Read and process the data in chunks</code><br><code>for i in range(0, num_rows, chunk_size):</code><br><code> end_row = min(i + chunk_size, num_rows)</code><br><code> chunk = table.slice(i, end_row - i) # Slice the table from i to end_row</code><br><code> </code></p> <p><code> # Convert to pandas DataFrame if needed</code><br><code> df_chunk = chunk.to_pandas()</code><br><code> </code><br><code> # Now you can process each chunk DataFrame as needed</code><br><code> print(df_chunk.head())</code></p>
Few-Shot Meta-Learning Applied to Whole Brain Activity Maps Improves Systems Neuropharmacology and Drug Discovery
<p>Data and Code are all included</p>
A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images
<p><span>Neuron morphology and sub-neuronal patterns offer vital insights into cell typing and the structural organization of brain networks. The community-collaborative BRAIN Initiative Cell Census Network (BICCN) project has yielded a vast amount of whole-brain imaging data. However, reconstructing multi-scale neuron morphometry at a whole-brain scale requires not only the integration of diverse hardware devices, tools, and algorithms but also a dedicated production workflow. To address these challenges, we developed a cloud-based, collaborative platform capable of handling peta-scale imaging data. Using this platform, we generated the largest multi-scale morphometry dataset from hundreds of sparsely labeled mouse brains. The morphometry dataset comprises 182,497 annotated cell bodies, 15,441 locally traced morphologies, and 1,876 fully reconstructed morphologies. We also identified sub-neuronal arborizations for both axons and dendrites, along with the primary axonal tracts connecting them. In addition, we identified 2.63 million putative boutons. All morphometric data were registered to the Allen Common Coordinate Framework (CCF) atlas. The morphometry dataset has proven to be an invaluable resource for whole-brain cross-scale morphological studies in mouse.</span></p>
Data from: T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 μm
We present an ultrahigh resolution in vivo human brain magnetic resonance imaging (MRI) dataset. It consists of T1-weighted whole brain anatomical data acquired at 7 Tesla with a nominal isotropic resolution of 250 μm of a single young healthy Caucasian subject and was recorded using prospective motion correction. The raw data amounts to approximately 1.2 TB and was acquired in eight hours total scan time. The resolution of this dataset is far beyond any previously published in vivo structural whole brain dataset. Its potential use is to build an in vivo MR brain atlas. Methods for image reconstruction and image restoration can be improved as the raw data is made available. Pre-processing and segmentation procedures can possibly be enhanced for high magnetic field strength and ultrahigh resolution data. Furthermore, potential resolution induced changes in quantitative data analysis can be assessed, e.g., cortical thickness or volumetric measures, as high quality images with an isotropic resolution of 1 and 0.5 mm of the same subject are included in the repository as well.
Whole Rat Brain (with S1, side view)
SciDraw upload
Whole Rat Brain (side view, v2)
SciDraw upload
D-LMBmap: A fully automated deep learning pipeline for whole-brain profiling of neural circuitry
<p>Source data file of D-LMBmap.</p>
The raw data of transcriptomes in whole brains when EcR-A was knocked down in pC1 neurons or not
<p class="MsoNormal">The establishment of neural circuits for female sexual receptivity is essential to reproduction, but the underlying mechanism of associated neurodevelopment is still not fully understood. Here we found that neuropeptide prothoracicotropic hormone (PTTH), hormone of the PG axis, regulates virgin female receptivity through ecdysone in <em>Drosophila melanogaster</em>. PTTH-positive PG neurons are doublesex-expressing neurons, they regulate virgin female receptivity before the metamorphosis during the 3<sup>rd</sup>-instar larval stage. Furthermore, the genetic knocking down of ecdysone receptor EcR-A in pC1 neurons regulates the morphological development of pC1 neurons and further the virgin female copulation rate. However, the activity of pC1 neurons did not change, suggesting the changes of synaptic connection between pC1 neurons and other neurons. Analysis of brain transcriptomes when EcR-A was knocked down in pC1 neurons reveals that additional genes are regulated downstream of EcR-A in pC1 neurons. Our work suggests a mechanism underlying how the neurodevelopment of neural circuits regulates virgin female receptivity in adults.</p>
Pilot Study of Whole Blood Transfusion for Trauma Brain Injury (TBI)
ClinicalTrials.gov study NCT06184828. IPD Sharing: NO. Countries: 0. Publications: 4.
Testing the Safety of M6620 (VX-970) When Given With Standard Whole Brain Radiation Therapy for the Treatment of Brain Metastases From Non-small Cell Lung Cancer, Small Cell Lung Cancer, or Neuroendoc
ClinicalTrials.gov study NCT02589522. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Phase II Trial of Low-Dose Whole Brain Radiotherapy With Concurrent Temozolomide and Adjuvant Temozolomide in Patients With Newly-Diagnosed Glioblastoma Multiforme
ClinicalTrials.gov study NCT01822275. IPD Sharing: Not stated. Countries: 1. Publications: 0.
RO4929097 and Whole-Brain Radiation Therapy or Stereotactic Radiosurgery in Treating Patients With Brain Metastases From Breast Cancer
ClinicalTrials.gov study NCT01217411. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of Enzastaurin Versus Placebo in the Treatment of Patients With Brain Metastases of Lung Cancer, After Whole Brain Radiation Therapy
ClinicalTrials.gov study NCT00415363. IPD Sharing: Not stated. Countries: 5. Publications: 0.
Study of Motexafin Gadolinium With Whole Brain Radiation Therapy Followed by Stereotactic Radiosurgery Boost in the Treatment of Patients With Brain Metastases
ClinicalTrials.gov study NCT00121420. IPD Sharing: Not stated. Countries: 2. Publications: 5.
Whole-Brain Radiation Therapy and Pemetrexed in Treating Patients With Brain Metastases From Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00280748. IPD Sharing: NO. Countries: 1. Publications: 0.
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.