Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
652
datasets available to search
ShareScore release 0.9.0
Dataset results
652 results for “amyloid”
Genetic variants beyond amyloid and tau associated cognitive decline: a cohort study
<p>Objective: To identify single nucleotide polymorphisms (SNPs) associated with cognitive decline independent of amyloid &[beta] (A&[beta]) and tau pathology in Alzheimer's disease (AD). Methods: Discovery and replication datasets consisting of 414 subjects (94 cognitively normal control [CN), 185 with mild cognitive impairment [MCI], and 135 AD) and 72 subjects (22 CN, 39 MCI, and 11 AD), respectively, were obtained from the Alzheimer's Disease Neuroimaging Initiative database. Genome-wide association analysis was conducted to identify SNPs associated with individual cognitive function (measured using the MMSE and ADAS-cog) while controlling for the level of A&[beta] and tau (measured as CSF p-tau/A&[beta]1-42). Gene ontology analysis was performed on SNP associated genes.</p> <p>Results: We identified one significant (rs55906536, &[beta]=-1.91,standard error 0.34, P =4.07×10<sup>-8</sup>) and four suggestive variants on chromosome 6, which were associated with poorer cognitive function. Congruent results were found in the replication data. A structural equation model showed that the identified SNP deteriorated cognitive function partially through cortical thinning of the brain in a region-specific manner. Furthermore, a bioinformatics analysis showed that the identified SNPs were associated with genes related to glutathione metabolism.</p> <p>Conclusions: In this study, we identified SNPs related to cognitive decline, in a manner which could not be explained by A&[beta] and tau levels. Our findings provide insight into the complexity of AD pathogenesis and support the growing literature on the role of glutathione in AD. This study suggests anti-oxidative agents may serve therapeutic for AD subjects with the identified SNPs.</p>
Data from: The presubiculum links incipient amyloid and tau pathology to memory function in older persons
Objective: To identify the hippocampal subregions linking initial amyloid and tau pathology to memory performance in clinically normal older individuals, reflecting preclinical Alzheimer's disease (AD). Methods: A total of 127 individuals from the Harvard Aging Brain Study (Mean age: 76.22 years ± 6.42, 68 females (53.5%)) with a Clinical Dementia Rating score of 0, a flortaucipir tau-PET scan, a Pittsburgh Compound B amyloid-PET scan, a structural MRI scan and cognitive testing were included. From these images, we calculated neocortical, hippocampal and entorhinal amyloid pathology, entorhinal and hippocampal tau pathology and the volumes of six hippocampal subregions and total hippocampal volume. Memory was assessed with the selective reminding test. Mediation and moderation analyses modeled associations between regional markers and memory. Analyses included covariates for age, sex and education. Results: Neocortical amyloid, entorhinal tau and presubiculum volume univariately associated with memory performance. The relationship between neocortical amyloid and memory was mediated by entorhinal tau and presubiculum volume, which was modified by hippocampal amyloid burden. With other biomarkers held constant, presubiculum volume was the only marker predicting memory performance in the total sample and in individuals with elevated hippocampal amyloid burden. Conclusions: The presubiculum captures unique AD-related biological variation that is not reflected in total hippocampal volume. Presubiculum volume may be a promising marker of imminent memory problems, and can contribute to understanding the interaction between incipient AD-related pathologies and memory performance. The modulation by hippocampal amyloid suggests that amyloid is a necessary process – but not sufficient – to drive neurodegeneration in memory-related regions.
Amyloid burden quantification depends on PET and MR image processing methodology
<p>The enclosed datasets refer to the work developed at the University Medical Center Groningen and consist of the minimally required PET image data to replicate the results of the study entitled "Amyloid burden quantification depends on PET and MR image processing methodology" which abstract can be found below:</p> <blockquote> <p>Quantification of amyloid load with positron emission tomography can be useful to assess Alzheimer’s Disease <em>in-vivo. </em>However, quantification can be affected by the image processing methodology applied. This study’s goal was to address how amyloid quantification is influenced by different semi-automatic image processing pipelines. Images were analysed in their <em>Native Space </em>and <em>Standard Space</em>; non-rigid spatial transformation methods based on maximum a posteriori approaches and tissue probability maps (TPM) for regularisation were explored. Furthermore, grey matter tissue segmentations were defined before and after spatial normalisation, and also using a population-based template. Five quantification metrics were analysed: two intensity-based, two volumetric-based, and one multi-parametric feature.</p> <p>Intensity-related metrics were not meaningfully affected by spatial normalisation and did not significantly depend on the grey matter segmentation method, with an impact similar to that expected from test-retest studies (≤10%). Yet, volumetric and multi-parametric features were sensitive to the image processing methodology, with an overall variability up to 45%. Therefore, the analysis should be carried out in <em>Native Space</em> avoiding non-rigid spatial transformations. For analyses in <em>Standard Space</em>, spatial normalisation regularised by TPM is preferred. Volumetric-based measurements should be done in <em>Native Space,</em> while intensity-based metrics are more robust against differences in image processing pipelines.</p> </blockquote>
The steered discrete molecular dynamics simulation data of amyloids with EC1 and EC12 cadherin dimer
<p>The steered discrete molecular dynamics (sDMD) simulation parameters are provided.</p> <p>Binding frequency of amyloids with EC1 and EC1-2 cadherin dimer.</p> <p>Trajectories of sDMD simulations of EC1 cadherin dimer with Abeta species.</p>
Remediation of Metal Oxide Nanotoxicity with A Functional Amyloid
<p>The molecular dynamics simulations on the binding of metal ions with the beta-lactoglobulin (bLg) amyloid fibrils.</p>
The mechanism of amyloid fibril growth from Φ-value analysis - data and analysis repository
<p>Data used for analysis and figure production. Full MD-simulation dataset is available at https://github.com/Aunstrup/_2024_amyloid_PI3KSH3_Phivalues. </p>
Choline acetyltransferase (ChAT) - Amyloid beta peptides complex Molecular dynamics Trajectories
<p>In silico molecular dynamics study was performed for the choline acetyltransferase (ChAT) - Abeta peptides complex. The molecular docking of Aβ<sub>40 </sub>and Aβ<sub>42</sub> on ChAT suggested three most probable binding clusters for both the Aβ peptides. Thus generating ChAT-Aβ<sub>40</sub> Cluster-0, ChAT-Aβ<sub>40</sub> Cluster-1, ChAT-Aβ<sub>40</sub> Cluster-2 for Aβ<sub>40</sub> peptide on ChAT, likewise ChAT-Aβ<sub>42</sub> Cluster-0, ChAT-Aβ<sub>42</sub> Cluster-1, ChAT-Aβ<sub>42</sub> Cluster-2 were generated for Aβ<sub>42</sub> peptide on ChAT.</p> <p>Each of the folders contains the topology file with a ‘.gro’ extension and a trajectory file with ‘.xtc’ extension generated from the 100 ns molecular dynamics performed for each of the clusters mentioned above that were generated from the molecular docking. The folders are named as follows:</p> <ol> <li>ChAT_AB40_Cluster_0: Containing the topology file (ab40_0.gro) and the trajectory file (ab40_0.xtc)</li> <li>ChAT_AB40_Cluster_1: Containing the topology file (ab40_1.gro) and the trajectory file (ab40_1.xtc)</li> <li>ChAT_AB40_Cluster_2: Containing the topology file (ab40_2.gro) and the trajectory file (ab40_2.xtc)</li> <li>ChAT_AB42_Cluster_0: Containing the topology file (ab42_0.gro) and the trajectory file (ab42_0.xtc)</li> <li>ChAT_AB42_Cluster_1: Containing the topology file (ab42_1.gro) and the trajectory file (ab42_1.xtc)</li> <li>ChAT_AB42_Cluster_2: Containing the topology file (ab42_2.gro) and the trajectory file (ab42_2.xtc</li> </ol>
Amyloid-beta 16-22 peptide monomer simulation with the CHARMM-Drude force field and OpenMM (Run 1)
<p>Amyloid-beta 16-22 peptide (monomer) simulations with the CHARMM-Drude force field and OpenMM. This is the first independent simulation runs out of 3.</p> <p>Part 1-2 are 200 ns long, 3-8 are 100 ns each. Total trajectory length is 1 microseconds. Frame saving frequency is 10 ps.</p> <p>The system contains ~ 150 mM NaCl.</p>
Amyloid-beta 16-22 peptide monomer simulation (150 mM NaCl) with the CHARMM36m force field and Gromacs (Run 3)
<p>MD simulations of the Amyloid-beta 16-22 monomer at 150 mM NaCl concentration with CHARMM36m force field and Gromacs. This repository contains the third out of three independent runs. </p> <p>Files belong to the publication "<a href="https://doi.org/10.1021/acs.jcim.0c01063">https://doi.org/10.1021/acs.jcim.0c01063</a>"</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with Gromacs 2018.3</p> <p>Total simulation time is 500 ns. Frames are saved with 100 ps frequency. </p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 2)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the second out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 3)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the third out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 1)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the first out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Data for: Sticker-and-Spacer Model for Amyloid Beta Condensation and Fibrillation
<p>Data for: Sticker-and-Spacer Model for Amyloid Beta Condensation and Fibrillation</p> <p>Preprint of the paper on bioRxiv:</p> <p>https://www.biorxiv.org/content/10.1101/2022.06.04.494837v1</p>
Molecular dynamics trajectories and portable binary run files for the self-assembly of heparin and amyloid-β(16-22) with the ProMPT forcefield
<h1>About this repository</h1> <p>Self-assembly simulations of heparin and amyloid-β(16-22) were performed at various numbers of peptides (<em>N_pep</em>), number of heparin molecules (<em>N_hep</em>), degrees of polymerization of heparin (<em>hep_dp</em>) and rigidity factors (<em>rig_f</em>) for heparin. This repository contains one folder per system, characterized by a combination of four system variables: <em>N_pep</em>, <em>N_hep</em>, <em>hep_dp</em> and <em>rig_f. </em></p> <p>The simulations were performed on the GROMACS 2019.4 molecular dynamics engine, with the ProMPT forcefield for coarse-grained molecular dynamics. Four independent trials: <em>trial_A</em>, <em>trial_B</em>, <em>trial_C</em> and <em>trial_D</em> were performed per system, each with different starting velocities. Each trial was run for 3000 ns.</p> <h1>Contents</h1> <h2><code>> heparin_abeta_trajectories.zip</code></h2> <p>The repository contains the zip file <code>heparin_abeta_trajectories.zip</code> with 13 folders named according to the convention,<code> "{<em>N_pep</em>}pep_{<em>N_hep</em>}hep_dp{<em>hep_dp</em>}_{<em>rig_f</em>}xRigid"</code>. For example, data for the system consisting of 16 peptides (<em>N_pep</em>), 1 heparin (<em>N_hep</em>), 18 monosaccharides in length (<em>hep_dp</em>) with a rigidity factor of 100 (<em>rig_f</em>) would be stored in the directory <code>16pep_1hep_dp18_100xRigid/</code>.</p> <p>If the system did not contain heparin, <em>N_hep, hep_dp</em> and <em>rig_f</em> were set to 0 by default. For example, data for the system consisting of 16 peptides (<em>N_pep</em>) and no heparin would be stored in the directory <code>16pep_0hep_dp0_0xRigid/</code>.</p> <p>A directory such as <code>16pep_1hep_dp18_100xRigid/</code> will have the following contents:</p> <ul> <li><code><strong>solute_only.ndx</strong></code><br>Contains an index group for solute molecules (peptides and/or heparin) only.</li> <li><code><strong>trial_A/</strong></code> <ul> <li><code><strong>md.tpr</strong></code> <br>Portable run file with which the current trajectory was generated. This file may be used to reproduce the trajectory as well.</li> <li><code><strong>solute_only.cluster_center.xtc </strong></code><br>A gromacs trajectory containing only the solute molecules (peptides and/or heparin), centered with the gromacs tool <em>gmx trjconv</em></li> <li><code><strong>solute_only.tpr </strong></code><br>A gromacs portable binary input file containing data for the solute molecules (peptides and/or heparin) only, generated with the <em>gmx convert-tpr</em> tool.<br>This file may be used during analysis of the solute_only.cluster_center.xtc trajectory.</li> </ul> </li> <li><code><strong>trial_B/</strong></code><br>contents same as <code><strong>trial_A/</strong></code></li> <li><strong><code>trial_C/</code><br></strong>contents same as <code><strong>trial_A/</strong></code></li> <li><strong><code>trial_D/</code><br></strong>contents same as <code><strong>trial_A/</strong></code></li> </ul> <h2><code>> psf_files_for_VMD_visualization.zip</code></h2> <p>This zip file contains thirteen .psf files, one per system, that can be used in accord with <code>solute_only.cluster_center.xtc </code>files to visualize trajectories on the Visual Molecular Dynamics (VMD) software.</p> <p><strong>Please seek out the associated publication for essential context on these trajectories. </strong></p> <p><strong>To access the source files with which these simulations were set-up, and a brief tutorial, see: </strong><a href="https://github.com/suhasgotla/heparin_amyloid_self-assembly">https://github.com/suhasgotla/heparin_amyloid_self-assembly</a></p>
Figure 6 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 6. Treatment Group with resveratrol standard after induced by Beta-Amyloid (10 x10).
Figure 3 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 3. Treatment group: 2- ME + Resveratrol isolated from Tempeh (10 x10).
Data from: Flortaucipir PET uncovers relationships between tau and β-amyloid in aging, primary age related tauopathy, and Alzheimer disease
<p>[<sup>18</sup>F]-Flortaucipir PET is considered a good biomarker of Alzheimer's disease. However, it is unknown how flortaucipir is associated with the distribution of tau across brain regions and how these associations are influenced by β-amyloid. It is also unclear whether flortaucipir can detect tau in definite primary age-related tauopathy (PART). We identified 248 individuals at Mayo Clinic that had undergone [<sup>18</sup>F]-flortaucipir PET during life, had died, and undergone an autopsy, 239 cases of which also had β-amyloid PET. We assessed nonlinear relationships between flortaucipir uptake in nine medial temporal and cortical regions, Braak tau stage and Thal β-amyloid phase using generalized additive models. We found that flortaucipir uptake was greater with increasing tau stage in all regions. Increased uptake at low tau stages in medial temporal regions was only observed in cases with high β-amyloid phase. Flortaucipir uptake linearly increased with β-amyloid phase in medial temporal and cortical regions. The highest flortaucipir uptake occurred with high Alzheimer's disease neuropathologic change (ADNC) scores, followed by low-intermediate ADNC scores, then PART, with entorhinal cortex providing the best differentiation between groups. Flortaucipir PET had limited ability to detect PART and imaging defined PART did not correspond with pathologically defined PART. In summary, spatial patterns of flortaucipir mirrored histopathological tau distribution, were influenced by β-amyloid phase, and were useful for distinguishing different ADNC scores and PART.</p>
Accurate and Unbiased Quantitation of Amyloid-β Fluorescence Images Using ImageSURF
<p>Software tools and image files needed to reproduce the results of the image classifier evaluation in the Current Alzheimer Research manuscript of the same title.</p>
Microscopy images - " Particulate matter constituents trigger the formation of extracellular amyloid β and Tau -containing plaques and neurite shortening in vitro"
<p>This repository contains microscopy data from the manuscript "Particulate matter constituents trigger the formation of extracellular amyloid β and Tau -containing plaques and neurite shortening in vitro" by Aleksandar Sebastijanović, Laura Maria Azzurra Camassa, Vilhelm Malmborg, Slavko Kralj, Joakim Pagels, Ulla Vogel, Shan Zienolddiny-Narui, Iztok Urbančič, Tilen Koklič, and Janez Štrancar (published in Nanotoxicology, 18(4), 335–353. https://doi.org/10.1080/17435390.2024.2362367).</p> <p>Raw data are organized in folders named by image number, containing a subfolder with the date (year-day-month) of image acquisition. Followed by a subfolder named by a nanomaterial to which neurons were exposed. Each folder contains images from individual multi-channel, multi-position time-lapse experiments with different combinations of cells exposed to one nanomaterial. Files are named as: IMGxxxx_[ExperimentCode]_ROIxx_[Channel].tif, where each of the varying elements in [..] denotes the following:<br>• [ExperimentCode]: a short name of the experiment<br>• [Channel]: membrane (MEM), cytoplasm neuronal cells (NEU), nanomaterial (NANO), amyloid beta (AMY)</p>
Figure 8 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 8. Treatment Group with resveratrol tempeh + 2-ME after induced by Beta-Amyloid (10 x10).
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.