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MRI data of 40 adult participants in response to a cue induced craving task following food fasting, social isolation and baseline (within-subject design)
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Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
Functional MRI data from medetomidine-isoflurane anesthetized marmosets
<p>This dataset contains unprocessed <strong>functional MRI (fMRI)</strong> data acquired in <strong>common marmosets</strong> (<em>Callithrix jacchus</em>), The data were obtained during a continuous infusion of the sedative <strong>medetomidine</strong>, supplemented with a low concentration of <strong>isoflurane</strong>. All experiments were carried out in accordance with the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes.</p> <p><strong>Related paper</strong></p> <p>This dataset supplements the following <a href="https://www.biorxiv.org/content/10.1101/2023.11.21.568138v1.abstract">manuscript</a>.</p> <p><strong>Preserving functional network structure under anesthesia in the marmoset monkey brain</strong></p> <p>M Ortiz-Rios, N Sirmpilatze, J Koenig, S Boretius - bioRxiv, 2023</p> <p><strong>doi: <a href="https://doi.org/10.1101/2023.11.21.568138">https://doi.org/10.1101/2023.11.21.568138</a> </strong></p> <p><strong>Data structure</strong></p> <p>The main data files are organized into eight zipped folders - <em><strong>sub-02.tar.gz, .... sub-09.tar.gz </strong></em> - each constituting a dataset formatted according to the <a href="https://bids.neuroimaging.io/">Brain Imaging Data Structure</a> specifications (BIDS v1.6.0).</p> <ul> <li>Each BIDS-formatted dataset contains subfolders for individual sessions (e.g. <em><strong>ses-0001</strong>, </em>etc.).Additionally, a text file, <strong><em>participants.tsv</em></strong>, with some essential information about the subjects (e.g. age, weight, sex).</li> <li>Each subject-specific folder contains subfolders named <em><strong>func</strong> </em>and <strong><em>anat</em></strong>, storing fMRI and structural MRI data respectively. The (f)MRI data are provided in <a href="https://nifti.nimh.nih.gov/">NIfTI format</a> (suffixed with <strong><em>.nii.gz</em></strong>). </li> <li>The <em><strong>func</strong></em> files are named as <strong><em>{sub-id}_{ses-id}_{run-id}_{task-id}_bold.nii.gz. </em></strong>The task id is based on runs acquired for resting-state or during visual stimulation.</li> </ul> <p><strong>BIDS-formatted dataset</strong></p> <p>The basic characteristics of the datasets are given below. More details can be found in the <a href="https://www.biorxiv.org/content/10.1101/2023.11.21.568138v1.abstract">preprint</a>.</p> <ol> <li><em><strong>Marmoset</strong></em> <ul> <li><strong>Institution:<em> </em></strong>German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany</li> <li><strong>MR system:</strong> Bruker BioSpec 9.4 T, equpped with B-GA 20S gradient</li> <li><strong>Anatomical MRI scan:</strong> Proton density-weighted (PDw) with magnetization transfer (MT) pulse, 1 per subject</li> <li><strong>fMRI scan:</strong> GE-EPI, several runs per subject (between 6 and 9 runs), duration 330 s for visual runs and 600 s for resting-state runs, all with a TR of 2 s and a resolution of 0.4 mm isotropic.</li> <li><strong>Subjects:</strong> 8 <em>Callithrix jacchus</em></li> <li><strong>Age range:</strong> 2.6 - 9.5 years</li> <li><strong>Weight range:</strong> 375 - 517 g</li> <li><strong>Sex:</strong> 5 males and 3 females</li> <li><strong>Ethics oversight:</strong> Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval numbers 33.19-42502-04-17/2496)</li> </ul> </li> </ol>
Data for: Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI
<p>Magnetic Resonance Imaging data used in the work "Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p> </p> <p>The *_ind.{cfl,hdr} files store the indices of the different spokes of the corresponding datasets:</p> <p> </p> <p><strong>data_res_S{1597,0987,0377,0233,0089,0055,0021}</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 3.2|2.04</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{16,15,13,12,10,9,7}^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_raga, data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{13}^1, 2\psi^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi^1</p> <p>Sampling: GA</p> <p><br><br> </p> <p><strong>data_invivo_ga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi^1</p> <p>Sampling: Golden-Ratio</p> <p><br> </p> <p> </p> <p><strong>data_invivo_raga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi_{13}^1</p> <p>Sampling: RAGA</p> <p><br> </p>
MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. The dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 1.5 T and 3 T (additional 7 T data are provided in separate datasets).</p>
Dynamic Contrast Enhanced MRI Raw Data Acquired with 3D Cones Trajectory
<p>This repository contains the raw data for the second dynamic contrast enhanced (DCE) MRI in <a href="https://arxiv.org/abs/1909.13482">Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions</a>. The data is stored as numpy arrays, containing k-space data (ksp.npy), coordinates (coord.npy), and density compensation factors (dcf.npy). Code to process and reconstruct the data is available here: <a href="https://github.com/mikgroup/extreme_mri">https://github.com/mikgroup/extreme_mri</a></p> <p>For more information about how the data is acquired, please see the linked paper.</p>
Efficient PCA denoising of spatially correlated redundant MRI data
<p>MRI data used for the study: "Henriques, Ianus, Novello, Jovicich, Jespersen, Shemesh. Efficient PCA denoising of spatially correlated redundant MRI data. Imaging Neuroscience (In Press)."</p><p><strong>Preclinical scanner data</strong></p><p>All animal experiments for the collection of these datasets were preapproved by the institutional and national authorities and carried out according to European Directive 2010/63.</p><p>A mouse brain (C57BL/6J) was extracted via transcardial perfusion with 4% Paraformaldehyde (PFA), immersed in 4% PFA solution for 24 h, washed in Phosphate-Buffered Saline (PBS) solution for at least 24 h, and then placed on a 10 mm NMR tube filled with Flourinert (Sigma Aldrich, Lisbon, PT), which was sealed using paraffin film. </p><p>The MRI experiments were performed on a 16.4 T Bruker Aeon Ascend scanner (Bruker, Karlsruhe, Germany), interfaced with an Avance IIIHD console, and equipped with a gradient system capable of producing up to 3000 mT/m in all directions. A constant temperature of 37oC was maintained throughout the experiments using the probe's variable temperature capability. </p><p>Two distinct diffusion-weighted datasets were then acquired using Bruker's standard "Diffusion Tensor Imaging EPI":</p><ul><li><i>Dataset1 </i>(<strong>MB_exp1.nii</strong> and its brain mask<strong> MB_exp1_mask.nii</strong>): For this dataset, we modulated the amount of spatial correlations by acquiring EPI datasets with parameters optimized to mitigate noise spatial correlations, particularly avoiding k-space undersampling acquisition during EPI's gradient ramps and without using partial Fourier, which minimize regridding.</li><li><i>Dataset2 </i>(<strong>MB_exp2.nii</strong> and its brain mask<strong> MB_exp2_mask.nii</strong>): The second dataset was acquired with identical resolution, number of acquisitions, etc., but with large factors inducing spatial correlations, including k-space sampling during gradient ramps (default Bruker's acquisition and reconstruction procedures for acquisition speed) and with a significant phase partial Fourier factor of 6/8 (note for partial Fourier acquisitions, EPI data is reconstructed with zero-padding, according to the default reconstruction procedures by Bruker's pre-clinical reconstruction software Paravision 6.0.1).</li></ul><p>All datasets are acquired for the following diffusion-weighted parameters: 30 gradient directions for b-values 1, 2 and 3 ms/μm2 (Δ = 15 ms, δ = 1.5 ms), and 20 consecutive b-value=0 acquisitions - b-values and diffusion gradient directions are saved in files: <strong>MB.bval</strong> / <strong>MB.bvec</strong>.</p><p>Other acquisition parameters: TR/TE = 3000/50 ms, 9 coronal slices, Field of View = 12×12 mm2, matrix size 80×80, in-plane voxel resolution of 150×150 μm2, slice thickness = 0.7 mm, number of averages = 2, number of segments = 1, double sampling acquisition.</p><ul><li><i>Gold standard acquisitions for dataset 2 </i>(<strong>MB_exp2_20averages.nii</strong>): For a gold standard reference, the second dataset was also repeated for 20 averages. Note, since this dataset is aligned to <strong>MB_exp2.nii</strong> you can use <strong>MB_exp2_mask.nii </strong>for its brain mask.</li></ul><p>For all datasets, Spatial drifts in the image domain were first corrected using a sub-pixel registration technique (Guizar-Sicairos et al., 2008).</p><p> </p><p><strong>Clinical scanner data</strong></p><p>Experiments were approved by the Ethical Committee of the University of Trento and the participant signed an informed consent. </p><p>MRI data was a acquired for a healthy control (male, 54 years) using a 3T MAGNETOM PRISMA scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 64-channel head-neck RF receive coil. </p><p>Diffusion MRI data was acquired using a monopolar single diffusion encoding EPI PGSE (Feinberg et al., 2010; Moeller et al., 2010; Xu et al., 2013) along 30 diffusion gradient directions for five non-zero b-values = 1, 2, 3, 4.5 and 6 ms/μm2 (Δ = 39.1 ms, δ = 26.3 ms) and 17 interspersed b-value=0 acquisitions. b-values and diffusion gradient directions are saved in files: <strong>HB.bval</strong> / <strong>HB.bvec</strong>. Note, only the masked version of these dataset (<strong>HB_masked.nii</strong> and its brain mask <strong>HB_mask.nii</strong>) is provided to guarantee that data privacy standards are met. For noise maps covering all FOV, the noise maps computed as the std of the 5 first repeating unmasked b = 0 acquisitions are provided in file <strong>stdS0i.nii.</strong></p><p>Other acquisition parameters were the following: TR/TE = 4000/80 ms, 63 axial slices, Field of View = 220×220 mm2, matrix size 110×110, isotropic resolution of 2 mm, 6/8 phase partial Fourier, parallel imaging with GRAPPA 2, simultaneous multi-slice factor 3. All diffusion MRI data was reconstructed using zero-padding, which is the default procedure for data acquired with partial Fourier above 70%. </p>
Data to "Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer"
<p>This record includes the DICOM images and analysed data that were used in the multicenter QA program for quantitative MRI in cervical cancer as published (<a href="https://www.sciencedirect.com/science/article/pii/S0167814020307854?via%3Dihub">https://doi.org/10.1016/j.radonc.2020.09.013</a> ).</p> <p>The DICOM data includes the acquired DICOM data for each institute selected to those that were used in the publication. Acquisitions that were not used were removed. Data was anonymized with conquest dicom server tools.</p> <p>The analyzed data files are included giving per measurement the estimated quantitative parameter values as well as the position of the ROIs and extracted signal intensity values per phantom sample. An explanation of the structure of the files is added in the readme file. The analysis was done with in-house written code in matlab.</p> <p>Included are a description of the sequence parameters for each institute (IQEMBRACE_PhantomQA_OverviewInstitutionalSequenceParameters_20241114) and details on the choices in the analysis of the data (IQEMBRACE_PhantomQA_OverviewPhantomData_20241114). As background also the description of the measurements was added, giving more information on how the measurements were performed.</p> <p>This work was in preparation for the IQ-EMBRACE trial (clinicaltrials.gov NCT03210428)</p>
Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing
<p><strong>Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing</strong></p> <p><strong>Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip)</strong></p> <p>Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing</p> <p>Open code and documentation of prediction models available via <a href="https://github.com/major-s/mouse-mcao-outcome-predictor">https://github.com/major-s/mouse-mcao-outcome-predictor</a></p> <p><strong>Content:</strong></p> <p>README.txt</p> <p>This information</p> <p><strong>dat</strong></p> <p>Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/<br>Files used for the manuscript:<br>t2.nii: t2 weighted image acquired 24 h post stroke<br>masklesion.nii: manually delineated lesion<br>x_masklesion.nii: lesion in atlas space<br>ix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)<br>Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.nii<br>Overlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region</p> <p><strong>prediction_models</strong></p> <p>Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.<br>The files have following sturcture:<br>Column 1: animal ID<br>Columns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)<br>Column 538: lesion volume<br>Column 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6<br>Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficit<br>Column 541: residual performance/deficit<br>Column 542: test or training group<br>Consecutive rows contain data for each animal specified by the animal id</p> <p>The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.<br>The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.<br>The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigm<br>Folder 'subacute deficit prediction' contains:<br> - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume<br> - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mri<br>Folder 'long-term outcome prediction' contains:<br> - lesion_volume: prediction of residual deficit using lesion volume<br> - segmented_mri: prediction of residual deficit using segmented_mri<br> - initial_performance: prediction of residual deficit using subacute deficit<br>Folder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.</p> <p>Files with equal file name and different extension always contain the same data</p> <p><strong>templates</strong><br>Allen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2<br> </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>
Functional MRI data from isoflurane-anesthetized macaques, marmosets, and rats
<p>This dataset contains unprocessed task-free <strong>functional MRI (fMRI)</strong> data acquired in three different mammalian species: <strong>long-tailed macaques</strong> (<em>Macaca fascicularis</em>), <strong>common marmosets</strong> (<em>Callithrix jacchus</em>), and <strong>rats</strong> (<em>Rattus Norvegicus</em>, Wistar strain). The data were obtained during <strong>isoflurane anesthesia</strong>, with the animals intubated and mechanically ventilated. All experiments were carried out in accordance with the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes.</p> <p><strong>Related paper</strong></p> <p>This dataset supplements the following <a href="http://doi.org/10.7554/eLife.74813">manuscript</a>:</p> <p>Sirmpilatze N, Mylius J, Ortiz-Rios M, Baudewig J, Paasonen J, Golkowski D, Ranft A, Ilg R, Gröhn O, Boretius S. <em>Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents.</em> eLife 2022;11:e74813. DOI: https://doi.org/10.7554/eLife.74813</p> <p><strong>Data structure</strong></p> <p>The main data files are organized into four zipped folders - <em><strong>Macaque.zip, Marmoset.zip, Rat1.zip, Rat2.zip</strong></em> - each constituting a dataset formatted according to the <a href="https://bids.neuroimaging.io/">Brain Imaging Data Structure</a> specifications (BIDS v1.6.0).</p> <ul> <li>Each BIDS-formatted dataset contains subfolders for individual subjects (e.g. <em><strong>sub-01</strong>, <strong>sub-02</strong>,</em> etc.), as well as a tab-separated text file, <strong><em>participants.tsv</em></strong>, with some essential information about the subjects (e.g. age, weight, sex).</li> <li>Each subject-specific folder contains subfolders named <em><strong>func</strong> </em>and <strong><em>anat</em></strong>, storing fMRI and structural MRI data respectively. The (f)MRI data are provided in <a href="https://nifti.nimh.nih.gov/">NIfTI format</a> (suffixed with <strong><em>.nii.gz</em></strong>). Each NIfTI file is accompanied by a <strong><em>.json sidecar</em></strong> holding metadata.</li> <li>The <em><strong>func</strong></em> subfolders also include tab-separated text files named as <strong><em>{sub-id}_scans.tsv</em></strong> (e.g. <em><strong>sub-01_scans.tsv</strong></em>). These files provide additional information on the fMRI runs within the <em><strong>func</strong></em> subfolder, such as the isoflurane concentration during the acquisition of the fMRI run, duration of the run, etc.</li> <li>The column names in <em><strong>participants.tsv</strong></em> and <em><strong>{sub-id}_scans.tsv</strong></em> files are explained in accompanying <em><strong>participants.json </strong></em>and <em><strong>{sub-id}_scans.json</strong></em> files.</li> </ul> <p><strong>BIDS-formatted datasets</strong></p> <p>The basic characteristics of the datasets are given below. More details can be found in the <a href="https://www.biorxiv.org/content/10.1101/2021.10.15.464515">preprint</a>.</p> <ol> <li><em><strong>Macaque</strong></em> <ul> <li><strong>Institution:<em> </em></strong>German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany</li> <li><strong>MR system:</strong> Siemens MAGNETOM Prisma 3T</li> <li><strong>Anatomical MRI scan:</strong> T1-weighted (MPRAGE), 1 per subject</li> <li><strong>fMRI scan:</strong> GE-EPI, 1 or 2 runs per subject, run duration 600 - 1200 s</li> <li><strong>Subjects:</strong> 13 <em>Macaca fascicularis</em></li> <li><strong>Age range:</strong> 6.8 - 19.8 years</li> <li><strong>Weight range:</strong> 3.6 - 8.1 kg</li> <li><strong>Sex:</strong> all females</li> <li><strong>Ethics oversight:</strong> Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval number 33.19-42502-04-16/2278)</li> </ul> </li> <li><em><strong>Marmoset</strong></em> <ul> <li><strong>Institution:<em> </em></strong>German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany</li> <li><strong>MR system:</strong> Bruker BioSpec 9.4 T, equpped with B-GA 20S gradient</li> <li><strong>Anatomical MRI scan:</strong> Proton density-weighted (PDw) with magnetization transfer (MT) pulse, 1 per subject</li> <li><strong>fMRI scan:</strong> GE-EPI, 1 run per subject, run duration 600 s (except for sub-21, containing 4 runs of 300 s duration each).</li> <li><strong>Subjects:</strong> 21 <em>Callithrix jacchus</em></li> <li><strong>Age range:</strong> 1.9 - 14.2 years</li> <li><strong>Weight range:</strong> 337 - 517 g</li> <li><strong>Sex:</strong> 11 females</li> <li><strong>Ethics oversight:</strong> Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval numbers 33.19-42502-04-17/2496 and 33.19-42502-04-17/2535)</li> </ul> </li> <li><em><strong>Rat1</strong></em> <ul> <li><strong>Institution:<em> </em></strong>German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany</li> <li><strong>MR system:</strong> Bruker BioSpec 9.4 T, equpped with B-GA 12S2 gradient</li> <li><strong>Anatomical MRI scan:</strong> T2-weighted (TurboRARE), 1 per subject</li> <li><strong>fMRI scan:</strong> GE-EPI, 6 runs per subject (except for sub-10: 4 runs), run duration 720 s</li> <li><strong>Subjects:</strong> 11 <em>Rattus norvegicus</em>, Wistar strain</li> <li><strong>Weight range:</strong> 350 - 450 g</li> <li><strong>Sex:</strong> all females</li> <li><strong>Ethics oversight:</strong> Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval number 33.19-42502-04-15/2042)</li> </ul> </li> <li><em><strong>Rat2</strong></em> <ul> <li><strong>Institution:<em> </em></strong>A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland</li> <li><strong>MR system:</strong> Bruker PharmaScan 7 T</li> <li><strong>Anatomical MRI scan:</strong> NOT provided</li> <li><strong>fMRI scan:</strong> GE-EPI, 6 runs per subject (except for sub-10: 4 runs), run duration 720 s</li> <li><strong>Subjects:</strong> 6 <em>Rattus norvegicus</em>, Wistar strain</li> <li><strong>Weight range:</strong> 265 - 350 g</li> <li><strong>Sex:</strong> all males</li> <li><strong>Ethics oversight:</strong> Animal Ethics Committee of the Provincial Government of Southern Finland</li> </ul> </li> </ol> <p><strong>Example data</strong></p> <p>Before you commit to downloading the BIDS-formatted datasets, we encourage you to examine the example data that we provide in the root folder. These include one anatomical (stuctural MRI) and one functional (fMRI) scan from each of the four datasets (Rat2 contains functional scans only), with their respecitve <strong><em>.json sidecars</em></strong>. A preview of these example scans is provided by <em><strong>0_preview.pdf.</strong></em></p>
Data from "Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography"
<p>Data presented in the Science Advances manuscript "<em>Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography</em>" by Yushchenko M., Sarracanie M., Salameh N.</p> <p>See further details in <em>Description.txt.</em></p> <p>The 3D wave datasets acquired in humans at 0.1 T can be used for elastogram reconstruction with appropriate methods.<br> <br> </p>
An Automatic Neuroimaging Infrastructure For Synthesis and Analysis of Structural MRI Data
<p>We have designed, implemented and distributed a fully automatic neuroimaging infrastructure for the synthesis and analysis of structural magnetic resonance imaging (MRI) data. The framework provides a concrete environment for the quantitative validation of various methods for the analysis of brain asymmetries, for comparisons of methods and measures of brain shape asymmetry, and possibly for clarifying contradicting neuroimaging findings of brain lateralizations.</p> <p>See <a href="https://sites.google.com/site/brainmorphorg/home">https://sites.google.com/site/brainmorphorg/home </a></p> <p>and </p> <p>A. Pepe, I. Dinov, and J. Tohka . An Automatic Framework for Quantitative Validation of Voxel Based Morphometry Measures of Anatomical Brain Asymmetry. <a href="http://dx.doi.org/10.1016/j.neuroimage.2014.06.029">NeuroImage , 100: 444 - 459, 2014</a><a href="https://doi.org/10.1016/j.neuroimage.2014.06.029"> </a></p> <p>for more information. </p>
7T MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI 7 T raw data for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset only contains additional raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively.</p>
QUIQI II Dataset - Statistical analyses of motion-corrupted MRI relaxometry data
<p>QUIQI II package includes supporting material for the scientific article by Corbin et al. entitled ‘Statistical analyses of motion-corrupted MRI relaxometry data’.</p> <p>The complete support package for the QUIQI method includes:</p> <ol> <li>A copy of the original analysis code used to compile the results presented in the original scientific publication (doi: 10.5281/zenodo.7612032) </li> <li>A subset of the data used in the original publication for computation of the results. This data also includes a set of analysis results obtained by running the code described in 1. on the provided data.</li> </ol> <p><br> The combination of 1. and 2. allows users to replicate the computation of the provided analysis results.<br> <strong>The material provided here only concerns part 2. of the QUIQI support package described above - subset of the data used in the original publication.</strong></p>
CAMRI Mouse Brain MRI Data
Open the record for dataset details and reuse information.
CAMRI Rat Brain MRI Data
Open the record for dataset details and reuse information.
Data for: Cardiac and Respiratory Self-Gating in Radial MRI using an Adapted Singular Spectrum Analysis (SSA-FARY)
<p>Magnetic Resonance Imaging measurement data used in our paper about self-gating with SSA-FARY (DOI: <a href="https://doi.org/10.1109/TMI.2020.2985994">10.1109/TMI.2020.2985994</a>). Cardiac data was obtained from eight volunteers with no known illness using single-slice radial (SS), simultaneous multi-slice radial (SMS), and stack-of-stars (SoS) FLASH and bSSFP sequences and is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p>
Open data repository, Boehm-Sturm et al., Phenotyping placental oxygenation in Lgals1 deficient mice using 19F MRI
<p>Open data repository of journal article "Phenotyping placental oxygenation in Lgals1 deficient mice using <sup>19</sup>F MRI"</p>
The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data
<p>This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER</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.