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163
datasets available to search
ShareScore release 0.9.0
Dataset results
163 results for “tensor”
Diffusion Tensor MRI to Distinguish Brain Tumor Recurrence From Radiation Necrosis
ClinicalTrials.gov study NCT00285324. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Diffusion Tensor Imaging of the Median Nerve Before and After Carpal Tunnel Corticosteroid Injection in Patients With Carpal Tunnel Syndrome: Feasibility Study
ClinicalTrials.gov study NCT03299361. IPD Sharing: NO. Countries: 0. Publications: 12.
MRI Volumetry and Diffusion Tensor Imaging in Temporal Lobe Epilepsy
ClinicalTrials.gov study NCT07384351. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Diffusion Tensor Imaging of Myelopathy
ClinicalTrials.gov study NCT03665935. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Data from: Validation of diffusion tensor imaging measures of nigrostriatal neurons in macaques
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Data from: Using matrix and tensor factorizations for the single-trial analysis of population spike trains
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Data from: Diffusion tensor imaging of dolphin brains reveals direct auditory pathway to temporal lobe
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Non-human primate white matter development during the first year of life as assessed by diffusion tensor imaging and quantitative relaxometry
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Data from: Tensor analysis reveals distinct population structure that parallels the different computational roles of areas M1 and V1
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Data from: Delay of gratification is associated with white matter connectivity in the dorsal prefrontal cortex: a diffusion tensor imaging study in chimpanzees (Pan troglodytes)
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Data from: Magnetic resonance imaging and tensor-based morphometry in the MPTP non-human primate model of Parkinson’s disease
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Progression of white matter degeneration in amyotrophic lateral sclerosis: A diffusion tensor imaging study
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Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-Seq II]
GEO Series GSE159964. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution
GEO Series GSE142328. Homo sapiens. 196 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution
GEO Series GSE142332. Homo sapiens. 262 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-seq_HOXA13 KD]
GEO Series GSE142331. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-seq]
GEO Series GSE142329. Homo sapiens. 40 samples. Type: Expression profiling by high throughput sequencing.
Characterization of the Fiber Orientations in Non-Crimp Glass Fiber Reinforced Composites using Structure Tensor
<p>This dataset contains data, notebooks and code used in the publication:</p> <p>[1] Jeppesen, N., V.A. Dahl, A.N. Christensen, A.B. Dahl, L.P. Mikkelsen, Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor. <em>IOP Conf. Ser.: Mater. Sci. Eng.</em> <strong>942</strong>, 012037, <a href="https://doi.org/10.1088/1757-899X/942/1/012037">https://doi.org/10.1088/1757-899X/942/1/012037</a>, 2020</p> <p>If you reference this dataset, please also consider referencing the paper above.</p> <p>HF401TT-13_FoV16.5_Stitch.zip contains an X-ray CT scan of a non-crimp glass fiber composite sample saved in the TXM file format.</p> <p>HF401TT-13_FoV16.5_Stitch.txm.nii contains a cut-out of the TXM data, where air around the sample has been removed and the result has been saved in the NIfTI file format.</p> <p>The two notebooks, <em>StructureTensorFiberAnalysisDemo</em> and <em>StructureTensorFiberAnalysisAdvancedDemo</em> rely on the NIfTI scan data, HF401TT-13_FoV16.5_Stitch.txm.nii. They demonstrate how to do structure tensor orientation analysis on the data.</p> <p>The HF401TT-13_FoV16.5_Stitch notebook use the TXM scan data, HF401TT-13_FoV16.5_Stitch.zip. It can be used to recreate the results of the published experimental results.</p> <p>To run the notebooks, the Python file, structure_tensor_workers.py, must be in the same directory as the notebook.</p> <p>By default, the notebooks expect the following folder structure:</p> <p>/notebooks: Folder with the notebooks and Python files.<br> /originals: Folder with the data (TXM/NIfTI files).<br> /tmp: Folder for temporary files and output generated running the notebooks.<br> /notebooks/figures: Folder for exporting figures as files (only needed if you want to save figures as files).</p>
Data from: Diffusion tensor imaging in patients with glioblastoma multiforme using the supertoroidal model
Purpose: Diffusion Tensor Imaging (DTI) is a powerful imaging technique that has led to improvements in the diagnosis and prognosis of cerebral lesions and neurosurgical guidance for tumor resection. Traditional tensor modeling, however, has difficulties in differentiating tumor-infiltrated regions and peritumoral edema. Here, we describe the supertoroidal model, which incorporates an increase in surface genus and a continuum of toroidal shapes to improve upon the characterization of Glioblastoma multiforme (GBM). Materials and Methods: DTI brain datasets of 18 individuals with GBM and 18 normal subjects were acquired using a 3T scanner. A supertoroidal model of the diffusion tensor and two new diffusion tensor invariants, one to evaluate diffusivity, the toroidal volume (TV), and one to evaluate anisotropy, the toroidal curvature (TC), were applied and evaluated in the characterization of GBM brain tumors. TV and TC were compared with the mean diffusivity (MD) and fractional anisotropy (FA) indices inside the tumor, surrounding edema, as well as contralateral to the lesions, in the white matter (WM) and gray matter (GM). Results: The supertoroidal model enhanced the borders between tumors and surrounding structures, refined the boundaries between WM and GM, and revealed the heterogeneity inherent to tumor-infiltrated tissue. Both MD and TV demonstrated high intensities in the tumor, with lower values in the surrounding edema, which in turn were higher than those of unaffected brain parenchyma. Both TC and FA were effective in revealing the structural degradation of WM tracts. Conclusions: Our findings indicate that the supertoroidal model enables effective tensor visualization as well as quantitative scalar maps that improve the understanding of the underlying tissue structure properties. Hence, this approach has the potential to enhance diagnosis, preoperative planning, and intraoperative image guidance during surgical management of brain lesions.
Data from: Diffusion tensor imaging reveals diffuse white matter injuries in locked-in syndrome patients
Locked-in syndrome (LIS) is a state of quadriplegia and anarthria with preserved consciousness, which is generally triggered by a disruption of specific white matter fiber tracts, following a lesion in the ventral part of the pons. However, the impact of focal lesions on the whole brain white matter microstructure and structural connectivity pathways remains unknown. We used diffusion tensor magnetic resonance imaging (DT-MRI) and tract-based statistics to characterise the whole white matter tracts in seven consecutive LIS patients, with ventral pontine injuries but no significant supratentorial lesions detected with morphological MRI. The imaging was performed in the acute phase of the disease (26 ± 13 days after the accident). DT-MRI-derived metrics were used to quantitatively assess global white matter alterations. All diffusion coefficient Z-scores were decreased for almost all fiber tracts in all LIS patients, with diffuse white matter alterations in both infratentorial and supratentorial areas. A mixture model of two multidimensional Gaussian distributions was fitted to cluster the white matter fiber tracts studied in two groups: the least (group 1) and most injured white matter fiber tracts (group 2). The greatest injuries were revealed along pathways crossing the lesion responsible for the LIS: left and right medial lemniscus (98.4% and 97.9% probability of belonging to group 2, respectively), left and right superior cerebellar peduncles (69.3% and 45.7% probability) and left and right corticospinal tract (20.6% and 46.5% probability). This approach demonstrated globally compromised white matter tracts in the acute phase of LIS, potentially underlying cognitive deficits.
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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.