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63 results for “tractography”
A whole-cortex probabilistic diffusion tractography connectome
<p>This is a collection of the results data for the eNeuro article of the same name, <a href="http://doi.org/10.1523/ENEURO.0416-20.2020">https://doi.org/10.1523/ENEURO.0416-20.2020</a>. Please cite this publication when using these data. Files with the .mat extension are matlab v7.3 files. The raw data from which these data were derived are available from <a href="https://db.humanconnectome.org">https://db.humanconnectome.org</a> and <a href="https://f-tract.eu">https://f-tract.eu</a>.</p>
EDEN2020 Ovine Diffusion Tensor Magnetic Resonance Tractography Atlas
<p>This dataset has been created to share the first <em>in vivo, </em>population-averaged Diffusion Tensor Magnetic Resonance Imaging (DTI) Ovine Tractography Atlas (OTA), where the course of the main white matter fiber bundles of the ovine brain has been reconstructed. The OTA has been described in the related paper ‘In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles’ by Pieri <em>et al. </em>(2019) <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a></p> <p>In the context of the EU’s Horizon EDEN2020 project, in vivo brain MRI protocol for ovine animal models was optimized on a 1.5T scanner. High resolution conventional MRI scans and DTI sequences (b-value = 1,000 s/mm<sup>2</sup>, 15 directions) were acquired on ten anesthetized sheep <em>ovis aries</em>, to define the diffusion features of normal adult ovine brain tissue. Topography of the ovine cortex was studied, and DTI maps were derived, to perform DTI deterministic tractography reconstruction of the corticospinal tract (CST), corpus callosum (CC), fornix (FX), visual pathway (VP), and occipitofrontal fascicle (OF), bilaterally for all the animals. Binary masks of the tracts were then coregistered and reported in the space of a standard stereotaxic ovine reference system ('ovine_model_05.nii', Nitzsche B. <em>et al.</em>, <em>Front. Neuroanat.</em> 9:69. <a href="https://doi.org/10.3389/fnana.2015.00069">https://doi.org/10.3389/fnana.2015.00069</a>). Finally, these were combined across animals to obtain population probability masks for each tract, representing voxel- by-voxel probability of the presence of the tract in the 10 animals, thus ranged between 0 and 10. </p> <p>Please don't forget to cite this publication when using the Ovine Tractography Atlas: </p> <p>Pieri V., Trovatelli M., Cadioli M., Zani D.D., Brizzola S., Ravasio G., Acocella F., Di Giancamillo M., Malfassi L., Dolera M., Riva M., Bello L., Falini A., & Castellano A. (2019). In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles. <em>Front Vet Sci, 6</em>(345), 345 <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a> </p> <p>This work has been carried out in the context of the EDEN2020 (Enhanced Delivery Ecosystem for Neurosurgery in 2020, www.eden2020.eu) project, that received funding from the European Union’s EU Research and Innovation programme Horizon 2020 under Grant Agreement No. 688279.</p>
Tractography Challenge ISMRM 2015 b=3000s/mm² Data.
<p>This archive contains a version of the ISMRM 2015 Tractography Challenge data simulated with a b-value of 3000s/mm², 64 non-zero gradient directions and 1 baseline volume. The other simulation parameters are the same as for the original phantom.</p>
HCP-YA Tractography Atlas (Figures)
<p>Yeh, F. C., Panesar, S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., ... & Verstynen, T. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57-68.</p>
HCP-YA Tractography Atlas (NIFTI Files)
<p>Yeh, F. C., Panesar, S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., ... & Verstynen, T. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57-68.</p>
Tractography Challenge ISMRM 2015 High-resolution Data
<p>The ground truth of this tractography validation data set was defined based on the fiber bundle geometry of a high-quality Human Connectome Project (HCP) dataset, constructed from multiple whole-brain global tractography maps. An expert radiologist extracted 25 major tracts (i.e., bundles of streamlines) from the tractogram. These association, projection and commissural fibers covered more than 70% of the white matter across the whole brain. The dataset features a brain-like macro-structure of long-range connections, mimicking an <em>in vivo</em> HCP-quality acquisition based on a simulated diffusion signal. </p>
Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination
<p>Submissions for the Tractostorm 2 Project [1] from our collaborators (raters) are available for new analysis.<br> Contains regions of interest (ROIs) as well as resulting bundles. Segmentations were performed with MI-Brain [2] (<a href="https://github.com/imeka/mi-brain">MI-Brain</a>)</p> <p>Initial data is the same as in the initial <a href="https://zenodo.org/record/2547025#.YRV2S3VKiUk">Tractostorm Project</a> [3]<br> Contains the data as sent to collaborators and the written document containing the dissection protocol in detail.</p> <p>[1] Rheault, Francois, et al. "Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination." <em>Human Brain Mapping</em> (2022).<br> [2] Rheault, Francois, et al. "MI-Brain, a software to handle tractograms and perform interactive virtual dissection." <em>Proceedings of the ISMRM Diffusion study group workshop, Lisbon</em>. 2016.<br> [3] Rheault, Francois, et al. "Tractostorm: The what, why, and how of tractography dissection reproducibility." <em>Human brain mapping</em> 41.7 (2020): 1859-1874.</p> <p>Data Organization:<br> The 5 HCP subjects were duplicated 4 times each.<br> 193441 -> A111, B218, C317, D418<br> 219231 -> A127, B228, C320, D426<br> 286650 -> A136, B237, C338, D436<br> 486759 -> A149, B246, C344, D443<br> 615441 -> A156, B252, C359, D450<br> <br> Bundles can be segmented automatically using the <a href="https://github.com/scilus/scilpy">scilpy</a> toolbox.<br> scil_filter_tractogram.py ${INPUT} ${OUTPUT} ${OPTIONS}</p> <ul> <li>${INPUT} would be the whole brain tractogram of an HCP subject in data_to_segment.zip</li> <li>${OUTPUT} would be the bundle filename (preferably .trk format)</li> <li>${OPTIONS} would be the sequence of ROIs to apply, one for each bundle. <ul> <li><strong>CC</strong>: '--drawn_roi CENTRAL_CC.nii.gz any include --drawn_roi LOWER_AXIAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude --drawn_roi POST_C_R.nii.gz any exclude --drawn_roi PRE_C_R.nii.gz any exclude'</li> <li><strong>AF_L</strong>: '--drawn_roi CENTRAL_CS_L.nii.gz any include --drawn_roi MEDIAL_SAGITTAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any include --drawn_roi PRE_C_L.nii.gz any include --drawn_roi TEMPORAL_ENTRY.nii.gz any include --drawn_roi TEMPORAL_STEM.nii.gz any exclude'</li> <li><strong>PYT_L</strong>: '--drawn_roi IC_L.nii.gz any include --drawn_roi MO_L.nii.gz any include --drawn_roi MB_L.nii.gz any include --drawn_roi MO_L_NOT.nii.gz any exclude --drawn_roi MID_SAGITTAL_PLANE.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude'</li> </ul> </li> </ul>
Tractostorm: Rater reproducibility assessment in tractography dissection of the pyramidal tract
<p>Segmentation of the 13 experts and 11 non-experts for the Tractostorm project. Contains the original dataset each participants received to perform their tasks.</p> <p>The tractography file format is *.trk and the image file format is *.nii.gz</p>
Tractography Templates for White Matter Microstructure Analysis in Aging and Alzheimer's Disease
<p>This dataset contains tractography templates derived from several studies focused on white matter microstructure and its associations with neurodegenerative diseases, particularly Alzheimer's disease and Parkinsonism. These templates span key tracts relevant to both cognitive decline and motor function, including but not limited to the medial temporal lobe white matter, transcallosal fibers, sensorimotor tracts, and the fornix. The templates were developed and validated using advanced diffusion MRI techniques across various populations, including aging individuals, dementia patients, and those at risk of neurodegenerative conditions. This resource serves as a valuable tool for researchers investigating the structural integrity of white matter in both health and disease, allowing for cross-study comparability and enhancing the understanding of neurodegenerative processes.</p>
A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI : an application to the ventriclar junctions
<p>Data and materials regarding the submission of the paper. See Data Avaibility section and https://github.com/valeryozenne/Cardiac-Structure-Database</p> <p> </p>
Tractography Challenge ISMRM 2015 Data
<p>The ground truth of this tractography validation data set was defined based on the fiber bundle geometry of a high-quality Human Connectome Project (HCP) dataset, constructed from multiple whole-brain global tractography maps. An expert radiologist extracted 25 major tracts (i.e., bundles of streamlines) from the tractogram. These association, projection and commissural fibers covered more than 70% of the white matter across the whole brain. The dataset features a brain-like macro-structure of long-range connections, mimicking <em>in vivo</em> DWI clinical-like acquisitions based on a simulated diffusion signal. An additional anatomical image with T1-like contrast was simulated as a reference. The dataset contains all files needed to perform the simulation.</p>
Tractography Challenge ISMRM 2015 Videos
<p>These videos supplement the Tractography Challenge ISMRM 2015 </p>
Tractography Challenge ISMRM 2015 Submissions
<p>Submissions to the open international ISMRM 2015 tractography challenge. 20 research groups with extensive expertise in diffusion imaging from 12 countries participated in the competition and submitted a total of 96 tractograms generated using a large variety of tractography pipelines with different pre-processing, local reconstruction, tractography and post-processing algorithms.</p> <p>Each file represents an individual submission. The mapping from file name to team can be inferred from the listing in the paper, as seen in the references.</p>
Multishell Diffusion MR Tractography Yields Morphological and Microstructural Information of the Anterior Optic Pathway: A Proof-of-Concept Study in Patients with Leber's Hereditary Optic Neuropathy. RAW DATA
<p>Raw data used to prepare Figures and Tables in article "Multishell Diffusion MR Tractography Yields Morphological and Microstructural Information of the Anterior Optic Pathway: A Proof-of-Concept Study in Patients with Leber’s Hereditary Optic Neuropathy", accepted for publication at International Journal of Environmental Research and Public Health, 27 May 2022.</p>
Bundles for tractography file format testing and example
<p>Bundle files and their reference anatomy for testing and examples IO in Dipy</p>
Tracing pathways from high-resolution tractography, transcription, and temporal dimensions
<p>The neural circuits supporting human cognition are topics of enduring interest. The lack of tools available to map circuits has precluded our ability to trace the evolution of the human connectome. We harnessed high-resolution connectomic, anatomic, and transcriptomic data to develop enhanced tools to test for modifications in developmental programs across species. We found corresponding ages across species and transcriptionally define neurons with stereotypical projections in humans and macaques. We used these data to test for modifications in frontal cortex circuit. Frontal cortex circuitry development is extended in primates, which is concomitant with an expansion in cortico-cortical pathways compared with mice in adulthood. These parameters varied little across humans and macaques. We identify a collection of conserved features in frontal cortex circuits in studied primates. We demonstrate that the integration of transcriptional and connectomic data across temporal dimensions is a robust approach to trace the evolution of connections in primates. This dataset contains scripts as well as diffusion MR scans of mouse brains.</p>
Ex vivo 100 μm isotropic diffusion MRI‐based tractography of connectivity changes in the end‐stage R6/2 mouse model of Huntington's disease
<div> <div> <div> <div> <p><strong>Background</strong>: Huntington's disease is a progressive neurodegenerative disorder. Brain atrophy, as measured by volumetric magnetic resonance imaging (MRI), is a downstream consequence of neurodegeneration, but microstructural changes within brain tissue are expected to precede this volumetric decline. The tissue microstructure can be assayed non-invasively using diffusion MRI, which also allows a tractographic analysis of brain connectivity.</p> <p><strong>Methods</strong>: We here used ex vivo diffusion MRI (11.7T) to measure microstructural changes in different brain regions of end‐stage (14 weeks of age) wild type and R6/2 mice (male and female) modeling Huntington's disease. To probe the microstructure of different brain regions, reduce partial volume effects and measure connectivity between different regions, a 100 μm isotropic voxel resolution was acquired.</p> <p><strong>Results</strong>: Although fractional anisotropy did not reveal any difference between wild‐type controls and R6/2 mice, mean, axial, and radial diffusivity were increased in female R6/2 mice and decreased in male R6/2 mice. Whole brain streamlines were only reduced in male R6/2 mice, but streamline density was increased. Region‐to‐region tractography indicated reductions in connectivity between the cortex, hippocampus, and thalamus with the striatum, as well as within the basal ganglia (striatum—globus pallidus—subthalamic nucleus—substantia nigra—thalamus).</p> <p><strong>Conclusions</strong>: Biological sex and left/right hemisphere affected tractographic results, potentially reflecting different stages of disease progression. This proof‐of‐principle study indicates that diffusion MRI and tractography potentially provide novel biomarkers that connect volumetric changes across different brain regions. In a translation setting, these measurements constitute a novel tool to assess the therapeutic impact of interventions such as neuroprotective agents in transgenic models, as well as patients with Huntington's disease.</p> </div> </div> </div> </div>
MRI Diffusion Tensor Tractography to Monitor Peripheral Nerve Recovery After Severe Crush or Cut/Repair Nerve Injury
ClinicalTrials.gov study NCT02960516. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: From circuits to lifespan: Translating mouse and human timelines with neuroimaging based tractography
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Tracing pathways from high-resolution tractography, transcription, and temporal dimensions
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International Brain Laboratory public data
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OpenNeuro
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