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60 results for “whole-brain”
Memantine Hydrochloride and Whole-Brain Radiotherapy With or Without Hippocampal Avoidance in Reducing Neurocognitive Decline in Patients With Brain Metastases
ClinicalTrials.gov study NCT02360215. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART I)
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Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART II)
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Functional and structural computed connectivity profiles for pre-defined whole-brain and cortical hull maps
<p>This Repository holds Lead-DBS Lead-Mapper v.2.5.2 computed functional and structural estimated connectivity. The structural dMRI connectivity is computed based on the "HCP1000 6K" connecome and the fMRI connectivity "GSP 1000 Yeo 2011" connectome.</p> <p>Both connectivity metrices were computed using two pre-defined grids:</p> <ol> <li> Cortical hull (1025 points). The grid was estimated based on the <a href="https:/github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/_get_grid_hull.m">SPM cortex_20484 grid</a> and subsampled by every 20th point. The grid is saved as a <a href="(https:/github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/mni_coords_cortical_surface.mat)">.mat file</a> and can be acessed through the main repository .</li> <li>Whole-brain (1236 points). Given the Lead-DBS provided <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/Automated%20Anatomical%20Labeling%203%20(Rolls%202020).nii">AAL3 atlas</a> the whole-brain .nii grid is subsampled by a factor of 150. See here the <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/_get_grid_whole_brain.py">grid computation</a>.</li> </ol> <p>Both grids can be accessed trough the <a href="https://github.com/neuromodulation/py_neuromodulation/tree/main">py_neuromodulation</a> API using the <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/nm_RMAP.py">nm_RMAP.py</a> module.</p>
Chemotherapy and Whole-Brain Radiation Therapy in Treating Patients With Primary Central Nervous System Non- Hodgkin's Lymphoma
ClinicalTrials.gov study NCT00002676. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Stereotactic Radiotherapy of the Resection Cavity of Brain Metastases vs. Post-operative Whole-brain Radiotherapy
ClinicalTrials.gov study NCT03285932. IPD Sharing: NO. Countries: 1. Publications: 21.
Whole-Brain Dynamics in the Natural Menstrual Cycle vs. an Ovarian Stimulated Cycle: Impact of Hormonal Fluctuations on Healthy Women Undergoing Ovarian Stimulation
ClinicalTrials.gov study NCT06910293. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Phase Ⅲ Trial of WBRT Versus Erlotinib Concurrent Whole-brain Radiation Therapy as first-line Treatment for Patients With Multiple Brain Metastases From Non-small-cell Lung Cancer(ENTER): a Multicentre
ClinicalTrials.gov study NCT01887795. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.
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
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>
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>
D-LMBmap: A fully automated deep learning pipeline for whole-brain profiling of neural circuitry
<p>Source data file of D-LMBmap.</p>
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.
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.
Data from: Diffantom: whole-brain diffusion MRI phantoms derived from real datasets of the Human Connectome Project
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Whole-brain transcriptome of Arid1b heterozygote mutation mice age of postnatal day 3
GEO Series GSE203214. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Whole-brain Neuron Reconstructions for Building Neuronal Connections at Multi-levels
<p>The dataset for construct connection networks of single neurons, including 1,877 neuron reconstructions, predicted bouton data, and 18,370 neurons. All neuronal morphologies were resampled with a spacing of 1 µm between consecutive points to ensure uniform reconstruction of the structures. The generated connectomes are also archived on the repository.</p> <p>1877-CELL_METADATA.xlsx: metadata of 1,877 neurons;</p> <p>SEU_1um.zip: 1,877 neuron reconstructions (full morphology);</p> <p>DEN-18370.zip: 1,877 neuron reconstructions (dendritic morphology);</p> <p>predicted_bouton_locations.zip: predicted bouton data (only coordinates and the corresponding neurons);</p> <p>jupyter tutorial: to generate connectivity matices of connectomes, and perform related analysis;</p> <p>arbor-net connections: arbor-net, single-neuron connectivity;</p> <p>bouton-net connections: bouton-net, single-neuron connectivity;</p> <p>all bouton from F1877: processed bouton locations, including their coordinates and brain regions;</p> <p>PACs from F1877: PACs from F1877 dataset, including coordinates, soma locations, and region of locations;</p> <p>SEU_net_w_corticalLayers and SEU_net_wo_corticalLayers: SEU-net brain region connectivity w or wo cortical layers.</p>
Creation of a Database of Healthy Subjects With 18FDG PET Brain Imaging as Part of the MOBILE Project (Multimodal Whole-Brain Imaging in Epilepsy)
ClinicalTrials.gov study NCT06976788. IPD Sharing: UNDECIDED. 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.