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3,309 results for “MRI”
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 3
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Datasets with and without deliberate head movements for detection and imputation of dropout in diffusion MRI
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7 Tesla MRI of the ex vivo human brain at 100 micron resolution
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Repetition of Computer Security Warnings Results in Differential Repetition Suppression Effects as Revealed with Functional MRI
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Deciphering the scopolamine rat model by preclinical functional MRI
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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>
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>
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>
PsPM-SF: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS (monophones/triads), performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 20 healthy unmedicated participants (10 females and 10 males, age range: 19 - 35 years, mean age: 24.2 +/- 4.9) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tones (4 s), and triads in root position or in first inversion, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less. NOTE: In Staib et al. 2015, this dataset is denoted as SC1F</p>
MRI Neonatal Lung Segmentation and 3D Morphologic Features
<p>We developed an ensemble of deep convolutional neural networks (2D-UNets) to perform automated neonatal lung segmentation from MRI sequences. A three-dimensional reconstruction is used to calculate MRI features for lung volume, shape, pixel intensity, and surface.</p> <p>In addition, ML Models for severity prediction of Bronchopulmonary Dysplasia (BPD) are implemented as an applied example of the use of MRI lung volumetric features for disease prognosis.</p> <p>This dataset comprises:</p> <ul> <li>Three pretrained 2D-UNet Models for Neonatal MRI Lung Segmentation.</li> <li>Resulting performances and features per MRI-sequence.</li> </ul> <p>See Publication:</p> <p>Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease (2023)</p> <p><a href="https://doi.org/10.1148/ryai.220239">https://doi.org/10.1148/ryai.220239</a></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>
Relaxation anisotropy of quantitative MRI parameters in biological tissues
<p>Dataset for the manuscript "Relaxation anisotropy of quantitative MRI parameters in biological tissues" published in Scientific Reports 2022</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>
Videos during training and acquisition of awake Sheep MRI
<p>These videos are provided in support of Pluchot, C., Adriaensen, H., Parias, C. <em>et al.</em> Sheep (<em>Ovis aries</em>) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions. <em>Behav Res</em> (2024). <a href="https://doi.org/10.3758/s13428-024-02449-6" target="_blank" rel="noopener">https://doi.org/10.3758/s13428-024-02449-6</a> . One illustrates our technique to train sheep to lie down, while the other shows the acquisition of a T1-weighted image from an awake and unrestrained sheep.</p> <p>This Version 2 also includes the file Sheepvoice-V3.mp4, which contains footage of several training steps. </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>
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>
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.