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Dataset results
195 results for “MRI data”
24-shell diffusion MRI data
<p>24-shell diffusion MRI used in <em>Huynh, K.M., Xu, T., Wu, Y., Wang, X., Chen, G., Wu, H., Thung, K.H., Lin, W., Shen, D. and Yap, P.T., 2020. Probing tissue microarchitecture of the baby brain via spherical mean spectrum imaging. IEEE Transactions on Medical Imaging, 39(11), pp.3607-3618.</em></p>
Replication Data for: Continuously moving table MRI with golden angle radial sampling
<p>Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings.</p>
Multi-Site Fat-Water Phantom MRI Data
<p><strong>Dataset description:</strong> Accompanying data for manuscript “Multi-Site,</p> <p>Multi-Vendor Validation of the Accuracy and Reproducibility of</p> <p>Proton-Density Fat-Fraction Quantification at 1.5T and 3T using a</p> <p>Fat-Water Phantom”, submitted for publication in Magnetic Resonance in</p> <p>Medicine in January 2016.</p> <p> </p> <p><strong>Details:</strong> The 'datasets' folder contains multiple MATLAB MAT-files</p> <p>files with chemical shift-encoded (CSE) MRI data for validation of fat</p> <p>quantification techniques in a fat-water phantom. Data was acquired at</p> <p>six sites with different MRI vendors, two field strengths (1.5T and</p> <p>3T) per site, and two protocols per field strength. The goal of these</p> <p>techniques is to measure proton-density fat-fraction (PDFF) accurately</p> <p>and reproducibly. Data from site 1 was acquired both at the beginning</p> <p>(October 2014) and at the end (December 2015) of this study, in order</p> <p>to evaluate the integrity of the phantom. </p> <p> </p> <p>Details on the vendors, platforms and protocols are described in the</p> <p>file 'vendors_platforms_protocols.pdf' in this same folder.</p> <p> </p> <p>The phantom consists of 11 vials with different fat concentrations</p> <p>(PDFF = 0%, 2.6%, 5.3%, 7.9%, 10.5%, 15.7%, 20.9%, 31.2%, 20.9%,</p> <p>31.2%, 41.3%, 51.4% and 100%, respectively). At each magnet, the vials</p> <p>were arranged horizontally along the B0 field and scanned with axial</p> <p>slices using a 3D multi-echo spoiled gradient echo pulse sequence.</p> <p> </p> <p>Each of the MAT-files contains the acquired complex-valued images over</p> <p>the three central slices within the phantom vials. The structure</p> <p>'imDataAll' contains the acquired data, with the following fields:</p> <p>'TE' (echo times), 'FieldStrength' (in Tesla), 'PrecessionIsClockwise'</p> <p>(describing whether water has higher resonance frequency than fat</p> <p>according to the reconstruction convention), 'echo_polarity' (relative</p> <p>polarity of the acquired echoes, ie: monopolar vs bipolar readouts),</p> <p>'images' (five-dimensional array containing the complex valued images,</p> <p>coil-combined in datasets received with multiple channels, with</p> <p>dimensions X x Y x Slices x Coils x Echoes).</p> <p> </p> <p>Additionally, the MAT-files contain reconstruction results as</p> <p>described in the manuscript, in the arrays 'fwmc_*' of size X x Y x</p> <p>Slices. Note that the nomenclature 'fwmc' stands for 'Fat-Water</p> <p>separated with Magnitude fitting (performed after complex fitting in</p> <p>order to obtain full 0-100% range of PDFF while avoiding errors</p> <p>related to phase shifts in the data), and a Common initial phase for</p> <p>the water and fat signals. The specific arrays are 'fwmc_ff' (PDFF</p> <p>map), 'fwmc_r2star' (R2*=1/T2* decay rate), 'fwmc_w' (water image),</p> <p>'fwmc_f' (fat image).</p>
Longitudinal structural MRI, MRS, and behavioral data for mice prenatally exposed to maternal immune activation at gestational day 9
<p>Previous evidence from our lab (https://cobralab.ca/) and others suggest that prenatal exposure to maternal immune activation (MIA) can impact trajectories of neurodevelopment as measured through brain anatomy and behavior in mice. Yet, there are still open questions regarding the alterations to developmental trajectories, as well as the impact on brain chemistry, that this data set seeks to explore. The dataset presented here includes magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) data from two timepoints, adolescence (postnatal day [PND 35]) and young adulthood (PND 60) in C57BL/6J mice prenatally exposed either to poly I:C (POL) inducing maternal immune activation (MIA) or saline (SAL) at gestational day (GD) 9. The dataset also includes three behaviors acquired after each scanning session with 2 days of rest between the scans and each behavior: open field test, social novel object preference test, and prepule inhibition. Finally, the data also include cytokine assays acquired from a separate sample of pregnant mice and a test-retest of MRS acquired from a voxel in the anterior cingulate area. </p> <p>The data here published were collected and analyzed for a paper under review, available as a preprint where more details can be found here: https://www.preprints.org/manuscript/202203.0136/v1. In brief, using whole-brain, voxelwise analysis techniques (deformation-based morphometry) we found MIA subtly altered developmental trajectories, reducing volume relative to SAL offspring in the hippocampus and the anterior, right caudate putamen, and increasing volume in the posterior, left caudate putamen and cerebellum. Additionally, there was a trending decrease of myo-inositol and GABA in MIA offspring at PND 60 compared to SAL controls. Finally, there was a trending decrease in ratio of distance travelled in the anxiogenic center zone of an open field compared to the outer areas at PND 35 for MIA offspring. </p> <p>In this dataset you will find a total of <strong>80 preprocessed structural MRIs</strong> in minc format acquired at postnatal day ~35 and ~60 in mice exposed to 5mg/kg poly I:C or vehicle control (0.9% sterile saline) at GD9. The images are included in CUPO_MIA_mncs.zip. These are T1-weighted structural images with two averages; repetition time (TR)/echo time (TE) = 21.55 ms/5.13 ms, matrix size = 260 x 158 x 210, voxel dimensions =&thinsp;70 µm isotropic, flip angle =&thinsp;20°, 23 min total using 5% isoflurane for induction, 1.5% for maintenance of anesthesia during the scan on a cryogenically-cooled surface coil. T1-weighted scans were preprocessed by stripping native coordinates, flipping left-right to maintain fidelity, denoising, correcting inhomogeneities in the bias field using the N4 algorithm, and registering in LSQ6 alignment (i.e. 6 degrees of freedom are allowed for imagine alignment: translations and rotations along x, y, and z dimensions). The demographics information for each animal is included in the <strong>demographics.csv</strong> file. </p> <p>Behavioural tests were performed following the postnatal day 35 and 60 scans in all animals with a 2 day rest period. These include: open field test, three chambered social approach, and prepulse inhibition. The data for all of these tests is presented in individual .csv spreadsheet and includes data for both the timepoints evaluated. Additionally, cytokine panels were collected from an independent cohort of 7 dams. <strong>MRS </strong>data are included in two formats: 1) preprocessed quantifications from LCModel software in csvs, and 2) raw data with press and press_w (respectively water supressed and unsupressed acquisitions) for analysis. The raw data were released in upload version 1.1.0. MRS was acquired from a 1.2 x 2.6 x 2.5 mm3 voxel in the ACA with a Point Resolved Spectroscopy sequence (PRESS; TR/TE=3000/8.5 ms, 256 averages). Within the raw_data.zip,</p> <p>Included in this data set are the structural MRIs in MINC format, the behavioural .csv data, the MRS data (csvs and raw files), and a <strong>README</strong> file providing further detail on the data structure and content, and on how to interpret the data column titles. DICOMS are also available for the structural MRI data, as are the raw (not-preprocessed) MINC files, available upon request to the authors. </p>
MRI data for "Stress-inducible phosphoprotein 1 (HOP/STI1/STIP1) regulates the spreading, aggregation, and toxicity of α-synuclein in vivo"
<p>Repository for magnetic resonance imaging data from the project titled "Stress-inducible phosphoprotein 1 (HOP/STI1/STIP1) regulates the spreading, aggregation, and toxicity of α-synuclein in vivo"</p> <p>Contains the pre-processed ex vivo T1-weighted images (Bruker 7T; 70 micron isotropic voxel resolution) for WT mice, M83 homozygous, and M83 homozygous mice with one copy of the TPR transgene. Full subject list can be viewed with the prado_MRI_M83dTPR_subjectlist_final.csv file.</p> <p>Whole brain region segmentations are available for these mice. These labels were generated using the MAGeT-Brain segementation pipleine (https://github.com/CobraLab/MAGeTbrain) and using a modified/merged version of the Allen brain atlas. The full list of regions included can be found in the file named Allen_brain_mapping_final.csv.</p> <p>See readme.txt file for more information</p>
Data on the detection of clinically significant prostate cancer by magnetic resonance imaging (MRI)-guided targeted and systematic biopsy
<p>This is a dataset from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy”. From 01/2014 to 04/2019 a sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate carcinoma (PCa) detection by TB and/or additional SB was analyzed.</p>
Data from: The MRi-Share database: Brain imaging in a cross-sectional cohort of 1,870 university students
<p>We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging.</p>
Pre-processed ex vivo MRI data for manuscript titled "Neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy prior to onset of motor symptoms""
<p>Repository for <em>ex vivo</em> magnetic resonance imaging data from the project titled "Presymptomatic neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy"</p> <p>Contains the pre-processed <em>ex vivo</em> T1-weighted images (Bruker 7T; 70 micron isotropic voxel resolution) for M83 alpha-synuclein A53T hemizygous mice that received either a phosphate buffered saline (PBS) or alpha-synuclein pre-formed fibrils (PFF) injection in the right dorsal striatum. Full subject list can be viewed with the "subject_list.csv" file. More details are available in the manuscript. </p>
Datasets for 'Automated characterization of noise distributions in diffusion MRI data'
<p>Datasets we used for the manuscript 'Automated characterization of noise distributions in diffusion MRI data'.</p>
Macaca mulatta and Macaca fascicularis anatomical and functional MRI data
<p> </p> <p>The ION dataset includes anatomical, field map, and fMRI data from 8 monkeys: 4 <em>Macaca mulatta</em> and 4 <em>Macaca fascicularis.</em></p> <p>The data is provided both as a directory structure compressed as <code>all_the_data.zip</code>, and as individual files. The correspondence between the directory structure and the individual files is contained in the file <code>tree.json</code>. The bash command <code>source unflatten.sh</code> can be used to convert the individual files into the original directory structure.</p> <p><strong>Scanner Specifications</strong></p> <ul> <li>Siemens Tim Trio 3T whole-body scanner with or without head-only gradient insert (Siemens AC88)</li> <li>8-channel phased-array transceiver coils</li> <li>Optimization of the magnetic field prior to data acquisition: Manual shimming</li> </ul> <p><strong>Sample Description</strong></p> <ul> <li>Sample size: 8</li> <li>Age distribution: 3.80-5.99 years</li> <li>Weight distribution: 5.0-10.2 kg</li> <li>Sex distribution: 7 male, 1 female</li> </ul> <p>Click <a href="http://fcon_1000.projects.nitrc.org/indi/PRIME/files/ion.csv">here</a> for the full sample description (.csv download)</p> <p><strong>Scan Procedures and Parameters</strong></p> <p><em>Ethics approval:</em> All experimental procedures for nonhuman primate research were approved by the Institutional Animal Care and Use Committee in the Institute of Neuroscience and by the Biomedical Research Ethics Committee, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and conformed to National Institutes of Health guidelines for the humane care and use of laboratory animals.</p> <p><em>Animal care and housing:</em> Animals were housed in single cage</p> <p><em>Any applicable training:</em> none</p> <p><strong>Scanning preparations</strong></p> <p><em>Anesthesia procedures:</em> Anesthesia of the animals was inducted with an intramuscular injection of a cocktail of dexmedetomidine (18 - 30 µg/kg) and midazolam (0.2 - 0.3 mg/kg), supplemented with atropine sulfate (0.05 mg/kg). After intubation, anesthesia was maintained using the lowest possible concentration of isoflurane gas via a MRI-compatible ventilator.</p> <p><em>Time between anesthesia and scanning:</em> Scanning lasted about 1.5 hours after induction.</p> <p><em>Head fixation:</em> Custom-built MRI-compatible stereotaxic frame</p> <p><em>Position in scanner and procedure used:</em> Sphinx position</p> <p><em>Contrast agent:</em> none</p> <p><strong>During scanning</strong></p> <p><em>Physiological monitoring:</em> Physiological parameters including blood oxygenation, ECG, rectal temperature, respiration rate and end-tidal CO<sub>2</sub> were monitored. Oxygen saturation was kept over 95%.</p> <p><em>Additional procedures:</em> Animals were ventilated by a MRI-compatible ventilator. Body temperature was kept constant using hot water blanket.</p> <p><strong>Scan sequences</strong></p> <ul> <li>Resting-state: <ul> <li>Gradient-echo EPI</li> <li>TR: 2000ms</li> <li>TE: 29ms</li> <li>Flip angle: 77°</li> <li>Field of view: 96 x 96 mm</li> <li>In plane resolution: 1.5 x 1.5 mm</li> <li>Slices number: 32</li> <li>Slice thickness: 2.5mm</li> <li>GRAPPA factor: 2</li> <li>Measurements: 200</li> <li>Slice direction: Coronal slice</li> </ul> </li> <li>Structural: <ul> <li>T1 MPRAGE Sequence</li> <li>Voxel resolution: 0.5 x 0.5 x 0.5 mm</li> <li>TE: 3.12ms</li> <li>TR: 2500ms</li> <li>TI: 1100ms</li> <li>Flip angle: 9°</li> <li>Slice direction: 44 sagittal slices</li> <li>Number of averages: 2</li> </ul> </li> <li>Additional: <ul> <li>Field map: a pair of gradient echo images</li> <li>TE1: 4.22ms</li> <li>TE2: 6.68ms</li> <li>Orientation and resolution: same as resting-state images</li> <li>Intended for EPI distortion correction</li> </ul> </li> </ul> <p><strong>Publications</strong></p> <ul> <li>Lv, Q., Yang, L., Li, G., Wang, Z., Shen, Z., Yu, W., Jiang, Q., Hou, B., Pu, J., Hu, H., & Wang, Z. (2016). Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders. Biological Psychiatry, 79(9), 765–775. <a href="https://doi.org/10.1016/j.biopsych.2015.02.028">https://doi.org/10.1016/j.biopsych.2015.02.028</a></li> </ul> <p><strong>Personnel</strong></p> <p>Zheng Wang<sup>1</sup></p> <p><sup>1</sup>Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China</p> <p><strong>Acknowledgements</strong></p> <p>The authors thank Drs. Lawrence Wald, Ravi Menon, John Gore, Franz Schmitt, Renate Jerecic, Thomas Benner, Kecheng Liu, Ignacio Vallines, and Hui Liu for their generous help and contribution to the construction of our custom-tuned gradient-insert (AC88) 3T MRI facility for nonhuman primate subjects.</p> <p><strong>Funding</strong></p> <ul> <li>Hundred Talent Program of Chinese Academy of Sciences (Technology) (Zheng Wang)</li> <li>Chinese 973 Program (2011CBA00400)</li> <li>The “Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02030004)</li> <li>The Outstanding Youth Grant (Hailan Hu)</li> </ul> <p>Detailed information can be found at <a href="http://fcon_1000.projects.nitrc.org/indi/PRIME/ion.html">http://fcon_1000.projects.nitrc.org/indi/PRIME/ion.html</a>.</p> <p><strong>Citation</strong> </p> <ul> <li>Wang, Z. (2019). Macaca mulatta and Macaca fascicularis anatomical and functional MRI data [Data set]. Zenodo. <a href="https://doi.org/10.5281/ZENODO.3402112">https://doi.org/10.5281/ZENODO.3402112</a>.</li> <li>Milham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., … Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61–74.e2. <a href="https://doi.org/10.1016/j.neuron.2018.08.039">https://doi.org/10.1016/j.neuron.2018.08.039</a>.</li> </ul>
Data for Contrast Enhanced Prostate MRI paper
<p>DICOM and MATLAB .mat data files used in paper:</p> <p>"<strong>Optimisation of contrast enhanced MR sequences for detection of prostate cancer"</strong></p> <p>This data is referred to in the code as 20Mar2018.</p> <p>Associated MATLAB .m files are on the GitHub site: <a href="https://github.com/UCL/CEPMRI-opt.">https://github.com/UCL/CEPMRI-opt </a></p> <p> </p>
Longitudinal structural MRI and behavioural data for mice prenatally exposed to maternal immune activation either early or late in gestation
<p>Prenatal maternal immune activation (MIA) is a risk factor for neurodevelopmental disorders. How the gestational timing of MIA-exposure differentially impacts downstream development remains unclear. The data presented here includes longitudinal structural magnetic resonance imaging (MRI) data from weaning to adulthood, and behavioural testing in adolescence and adulthood on C57BL/6 mice exposed to MIA induced by the viral mimetic, polyinosinic:polycytidylic acid (poly I:C) either early (gestational day [GD]9) or late (GD17) in gestation. </p> <p>The data published here was collected and analyzed for the following publication, where more details can be found (Guma et al., 2021 https://doi.org/10.1016/j.biopsych.2021.03.017). Briefly, we found that early MIA-exposure was associated with accelerated brain volume increases in adolescence/early-adulthood that normalized in later adulthood, in regions including the striatum, hippocampus, and cingulate cortex. Similarly, alterations in anxiety-like, stereotypic, and sensorimotor gating behaviours observed in adolescence normalized in adulthood. In contrast, MIA-exposure in late gestation had less impact on anatomical and behavioural profiles. </p> <p>In addition to the univariate analyses described above, we also undertook a multivariate analysis (partial least squares) to relate imaging and behavioural variables for the time of greatest alteration, i.e. adolescence/early adulthood. We further explored the molecular underpinnings of region-specific alterations in early MIA-exposed mice in adolescence using RNA sequencing (data for differentially expressed genes in the anterior cingulate cortex, dorsal hippocampus, and ventral hippocampus are available via the original publication https://doi.org/10.1016/j.biopsych.2021.03.017 for a separate cohort of adolescent mice prenatally exposed to MIA or vehicle at GD9). </p> <p>In this dataset, you will find a total of <strong>376 preprocessed structural MRIs</strong> (in MINC format) acquired at postnatal day ~21, ~38, ~60, and ~90 in mice exposed to poly I:C or vehicle control (0.9% sterile saline) at GD9 or 17. These are T1-weighted, manganese enhanced (50mg/kg 24 hours pre-scan), structural images at 100 micron isotropic resolution acquired on a 7 Tesla Bruker Biospec 70/30; matrix size of 180 x 160 x 90; 14.5 minutes, 2 averages, using 5% isoflurane for induction, 1.5% for maintenance of anesthesia during the scan. T1-weighted scans were preprocessed by stripping native coordinates, flipping left-right to maintain fidelity, denoising, correcting inhomogeneities in the bias field using the N4 algorithm, and registering in LSQ6 alignment (i.e. 6 degrees of freedom are allowed for imagine alignment: translations and rotations along x, y, and z dimensions). The demographics information for each animal is included in the <strong>demographics.csv</strong> file. </p> <p>Behavioural tests were performed following the postnatal day 38 and 90 scans in all animals with a 2 day rest period. These include: open field test, marble burying test, three chambered social approach, and prepulse inhibition. The attentional set shifting task was also performed following the final behavioural test in the postnatal day 90 wave of behaviours. The data for all of these tests is presented in its own individual .csv spreadsheet and includes data for both the timepoints evaluated.</p> <p>Included in this data set are the structural MRIs in MINC format, the behavioural .csv data, and a <strong>readme.txt</strong> file providing further detail on the data structure and content, and on how to interpret the data column titles. DICOMS are also available for the structural MRI data, as are the raw (not-preprocessed) MINC files, available upon request to the authors. </p> <p>Finally, the authors would like to acknowledge the funding bodies that supported the completion of this work including the Canadian Institute for Health Research, the Fonds de Recherche du Québec en Santé, and the Healthy Brains for Healthy Lives at McGill University.</p>
Data Supplement: GIRFReco.jl: An Open-Source Pipeline for Spiral Magnetic Resonance Image (MRI) Reconstruction in Julia
<p><strong>Dataset for GIRFReco.jl Paper</strong><br> <br> Please download this and extract to an appropriate location prior to running the demonstration code in GIRFReco.jl. The extracted folder will serve as the root directory in the demo code.</p>
Data from: Direct segmentation of cortical cytoarchitectonic domains using ultra-high-resolution whole-brain diffusion MRI
Open the record for dataset details and reuse information.
Data from: The MRi-Share database: Brain imaging in a cross-sectional cohort of 1,870 university students
Open the record for dataset details and reuse information.
Reorient. A Web tool for reorienting and cropping MRI data.
<p>Having a properly oriented MRI dataset is a fundamental step in any neuroimaging workflow. Reorient (<a href="https://neuroanatomy.github.io/reorient">https://neuroanatomy.github.io/reorient</a>) is an open source Web application for the manual alignment and cropping of MRI nifti volumes using an intuitive graphic interface. Even for completely arbitrarily oriented brains, with different data encodings and anisotropic voxels, the tool provides a fast and precise method to include manual alignment in a reproducible workflow.</p> <p>This figure shows the interface, the reorientation process, and the final result.</p> <p> </p>
Alzheimer's Disease versus Bipolar Disorder versus Health Control MRI data and processed results
<p><strong>README</strong></p> <p>The data is structured as follows:</p> <p>Clinical_data folder contains the .csv that can be read by spreadsheet software, as well as from Python, Matlab or R. There are separate files for each biomarker. The file "clinical_data_id_age_gender.csv" contains the numerical random key of the patient for anonymity, diagnostic key, age and gender for each entry in the other files. The file "clinical_data_corrected.csv" can be ignored.</p> <p>Diagnostic keywords: "crl" == healthy control, "tb" == bipolar disorder, "ea" == Alheimer's disease</p> <p>Imaging data is nifti encoded. The name of the file starts with the diagnostic key followed by the numerical random key and some nemotechnic for the contents. For instance: "crl_132_diff_dti_FA_FA_to_target.nii.gz" is the spatially normalized FA data of healthy control 132. Imaging data can be read with FSL, SPM, and any other nifti reading soft.</p> <p>Imaging folders contain the following data<br> DWI_origin - > the original diffusion weighted MRI data and their corresponding b-vector values</p> <p>FA - > the FA coefficients computed using FSL</p> <p>FA_to_target - > the FA volumes registered to MNI template using FSL tools</p> <p>T1_preprocessed - > the T1-weighted volumes at 1mm resolution registered to the MNI template using FSL no-linear registration tools</p> <p>T1_VBM_SPM_1mm - > the results of applying SPM implementation of voxel based morphometry (VBM) on the T1-weighted data, including results of the correlation between biomarkers and the detected clusters . Results can be checked using SPM (https://www.fil.ion.ucl.ac.uk/spm/)</p> <p>TBSS_results -> contains track based spatial statistics (TBSS) results obtained with FSL software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS)</p> <p> </p> <p><strong>Publications using this dataset</strong></p> <p>M. Graña, M. Termenon, A. Savio, A. Gonzalez-Pinto, J. Echeveste, J. M. Pérez, A. Besga, Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson’s correlation, Neuroscience letters,Volume 502, Issue 3, 20 September 2011, Pages 225-229</p> <p>A. Besga, M. Termenon, M. Graña, J. Echeveste, J. M. Perez, A. Gonzalez-Pinto "Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features, <strong>Neuroscience Letters</strong>, Volume 520, Issue 1, 27 June 2012, Pages 71–76.</p> <p>M. Termenon, M. Graña, A. Besga, J. Echeveste, A. Gonzalez-Pinto, Lattice Independent Component Analysis feature selection on Diffusion Weighted Imaging for Alzheimer’s Disease Classification, Neurocomputing (2013) Volume 114, 19 August 2013, Pages 132–141</p> <p>Ariadna Besga, Itxaso González-Ortega, Enrique Echeburúa, Alexandre Savio, Borja Ayerdi, Darya Chyzhyk, Jose LM Madrigal, Juan C. Leza, Manuel Graña, Ana González-Pinto, "Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder using multivariate analysis" Frontiers in Aging Neuroscience, 7:231</p> <p>Ariadna Besga-Basterra, Darya Chyzhyk, Itxaso González-Ortega, Alexandre Savio, Borja Ayerdi, Jon Echeveste, Manuel Graña, Ana González-Pinto, Eigenanatomy on fractional anisotropy imaging provides white matter anatomical features discriminating between Alzheimer’s Disease and Late Onset Bipolar Disorder, Current Alzheimer Research, 13(5): 557 - 565 (2016)</p> <p>Ariadna Besga, Darya Chyzhyk, Itxaso Gonzalez Ortega, Jon Echeveste, Marina Grana-Lecuona, Manuel Grana, Ana González-Pinto, White Matter Tract Integrity in Alzheimer’s Disease versus Late Onset Bipolar Disorder and its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers, Frontiers in Aging Neuroscience, 9:179 (2017)</p>
Lung MRI Raw Data Acquired with 3D UTE Radial Trajectory
<p>This repository contains the raw data for the second lung MRI result 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>
Data from: Multi-modal ultra-high resolution structural 7-Tesla MRI data repository
Structural brain data is key for the understanding of brain function and networks, i.e., connectomics. Here we present data sets available from the 'atlasing of the basal ganglia (ATAG)' project, which provides ultra-high resolution 7Tesla (T) magnetic resonance imaging (MRI) scans from young, middle-aged, and elderly participants. The ATAG data set includes whole-brain and reduced field-of-view MP2RAGE and T2*-weighted scans of the subcortex and brainstem with ultra-high resolution at a sub-millimeter scale. The data can be used to develop new algorithms that help building high-resolution atlases both relevant for the basic and clinical neurosciences. Importantly, the present data repository may also be used to inform the exact positioning of electrodes used for deep-brain-stimulation in patients with Parkinson's disease and neuropsychiatric diseases.
An MRI DICOM data set of the head of a normal male human aged 52
<p>This zip file contains a DICOM data set of magnetic resonance images a normal male mathematics professor aged 52. The experimental subject is the author. The MRI scans are T2 weighted turbo-spin-echo (T2W TSE) and T1 weighted Fast Field Echo (T1W FFE).</p> <p>The subject suffers from a small vertical strabismus (hypertropia), a misalignment of the eyes, which is visible in this data set.</p> <p>The author would like to thank the Radiology Department at the Macclesfield General Hospital for performing the scan.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.