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ECCO Ocean 3D Gent-Mcwilliams, Redi, and Background Vertical Diffusivity Coefficients for the Lat-Lon-Cap 90 (llc90) Native Model Grid (Version 4 Release 4)
This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Ocean 3D Gent-Mcwilliams, Redi, and Background Vertical Diffusivity Coefficients for the 0.5 degree Lat-Lon Model Grid (Version 4 Release 4)
This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ASPIDE: SERMAS diffusion-weighted image processing use-case
<p>This repository contains the outputs of the diffusion-weighted image processing use-case of the ASPIDE project for a single subject. The input files were obtained from the Human Connectome Project (HCP) database. HCP is an open project and the full unprocessed dataset can be obtained from <a href="https://db.humanconnectome.org/app/template/Login.vm">https://db.humanconnectome.org/app/template/Login.vm</a>.</p>
Macaca mulatta structural and diffusion weighted MRI data
<p> </p> <p>The AMU dataset includes structural and diffusion weighted MRI data from 4 <em>Macaca mulatta</em> monkeys.</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>Sample Description</strong></p> <ul> <li>Sample size: 4</li> <li>Age distribution: 7-8 years</li> <li>Weight distribution: 7.5-12.5 kgs</li> <li>Sex distribution: 3 male, 1 female</li> </ul> <p>Click <a href="http://fcon_1000.projects.nitrc.org/indi/PRIME/files/amu.csv">here</a> for the full sample description (.csv download)</p> <p><strong>Scan Procedures and Parameters</strong></p> <p><em>Ethics approval:</em> obtained at local Ethics Committee</p> <p><em>Animal care and housing:</em> At Institut de Neurosciences de La Timone</p> <p><em>Any applicable training:</em> N/A</p> <p><strong>Scanning preparations</strong></p> <p><em>Anesthesia procedures:</em> Isoflurane</p> <p><em>Time between anesthesia and scanning:</em> None- anesthesia was performed and monitored during scanning.</p> <p><em>Head fixation:</em> Kopf frame and ear bars</p> <p><em>Position in scanner and procedure used:</em> Sphinx position. <strong>Fiducial marker placed on right side.</strong></p> <p><em>Contrast agent:</em> None</p> <p><strong>During scanning</strong></p> <p><em>Physiological monitoring:</em> Heart rate, respiration</p> <p><em>Additional procedures:</em> Ventilation</p> <p><strong>Scan sequences</strong></p> <ul> <li>Scanner type: Siemens Prisma 3T</li> <li>Head coil: Body transmit array, 11cm loop receiving coil</li> <li>Optimization of the magnetic field prior to data acquisition: Automatic B0 shimming procedures from Siemens</li> <li> </li> <li>Diffusion-weighted: <ul> <li>Diffusion SE-EPI sequence</li> <li>Voxel resolution: 1 x 1 x 1 mm</li> <li>TE: 87.6ms</li> <li>TR: 7520ms</li> <li>64b1000</li> <li>6b300</li> <li>5b0</li> <li>Two repetitions with reversed phase encoding direction</li> </ul> </li> <li>Structural: <ul> <li>T1 <ul> <li>MPRAGE sequence</li> <li>Voxel resolution: 0.8 x 0.8 x 0.8 mm</li> <li>TE: 2.04ms</li> <li>TR: 2900ms</li> <li>TI: 1000ms</li> </ul> </li> <li>T2 <ul> <li>3D SPACE sequence</li> <li>Voxel resolution: 0.8 x 0.8 x 0.8mm</li> <li>TE: 561ms</li> <li>TR: 3200ms</li> </ul> </li> <li>QSM <ul> <li>3D GRE sequence</li> <li>Voxel resolution: 0.93 x 0.93 x 1mm</li> <li>TE: 2.7-42ms</li> <li>TR: 45ms</li> </ul> </li> </ul> </li> </ul> <p><strong>Personnel</strong></p> <ul> <li>Thomas Brochier<sup>1</sup></li> <li>Pascal Belin<sup>1</sup></li> <li>Frédéric Chavanne<sup>1</sup></li> <li>Lionel Velly<sup>1</sup></li> <li>Clémentine Bodin<sup>1</sup></li> <li>Luc Renaud<sup>1,2</sup></li> <li>Marc Martin<sup>1,2</sup></li> <li>Laurence Boes<sup>1,2</sup></li> <li>Julien Sein<sup>1</sup></li> <li>Bruno Nazarian<sup>1</sup></li> <li>Jean-Luc Anton<sup>1</sup></li> </ul> <p><sup>1</sup>Institut de Neurosciences de la Timone (INT), UMR7289 CNRS & Aix-Marseille Université, Marseille, France<br> <sup>2</sup>Centre d’Exploration Fonctionnelle et de Formation en Primatologie (CE2F-PRIM), UMS5737 CNRS & Aix-Marseille Université, Marseille, France</p> <p><strong>Acknowledgements</strong></p> <p>We gratefully acknowledge the support of French France-Life Imaging (FLI) and Infrastructures en Biologie Santé et Agronomie (IBISA)</p> <p><strong>Funding</strong></p> <p>Project PRIMAVOICE, PI Belin, French Agence Nationale de la Recherche</p> <p>Detailed information can be found at <a href="http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html">http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html</a>.</p> <p><strong>Citation</strong></p> <ul> <li>Brochier, T., Belin, P., Chavanne, F., Velly, L., Bodin, C., Renaud, L., Martin, M., Boes, L., Sein, J., Nazarian, B., & Anton, J.-L. (2019). Macaca mulatta structural and diffusion weighted MRI data [Data set]. Zenodo. <a href="https://doi.org/10.5281/ZENODO.3402456">https://doi.org/10.5281/ZENODO.3402456</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 from: Longitudinal diffusion MRI as surrogate outcome measure for myelopathy in adrenoleukodystrophy
Objective: To prospectively determine the potential of diffusion MRI (dMRI) of the cervical spinal cord and the corticospinal tracts in brain as surrogate outcome measure for progression of myelopathy in men with adrenoleukodystrophy, as better outcome measures to quantify progression of myelopathy would enable clinical trials with less patients and shorter follow-up. Methods: Clinical assessment of myelopathy included Expanded Disability Status Scale (EDSS), Severity Scoring system for Progressive Myelopathy (SSPROM), timed up-and-go and 6-minute walk test. Applied dMRI metrics included fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity. Results: Data was available for 33 controls and 52 patients. First, cross-sectionally, differences between groups (controls vs. patients; controls vs. asymptomatic patients vs. symptomatic patients) were statistically significant for fractional anisotropy, mean diffusivity and radial diffusivity in spinal cord and brain corticospinal tracts (effect size 0.31-0.68). Correlations between dMRI metrics and clinical measures were moderate to strong (correlation coefficient 0.35-0.60). Second, longitudinally (n=36), change on clinical measures was significant after 2-year follow-up for EDSS, SSPROM and timed up-and-go (p≤0.021, effect size ≤0.14). Change on brain fractional anisotropy and radial diffusivity was slightly larger (p≤0.002, effect sizes 0.16-0.28). In addition, a statistically significant change was detectable in asymptomatic patients using brain dMRI and not using the clinical measures. Change on clinical measures did not correlate to change on dMRI metrics. Conclusions: Although effect sizes were small, our prospective data illustrate the potential of dMRI as surrogate outcome measures for progression of myelopathy in men with adrenoleukodystrophy.
Listeners sensitivity to different locations of diffusive surfaces in performance spaces: the case of a shoebox concert hall
<p>The data set represent the auralization files of the subjective investigation on Listeners sensitivity to different locations of diffusive surfaces in performance spaces: the case of a shoebox concert hall (ESPRO-IRCAM). </p> <p>The name of each file (e.g. 1_DummyHead1_C_No13_ceiling10.0_Run1__Dode1_mahler) gives infomation on:</p> <p>- Two Artificial heads locations: DummyHead1 and DummyHead1</p> <p>- Two configurations for the Absoprtive condition (C_No16=Df-A and C_No18=Dr-A) and Two configurations for the Reflective condition (C_No13=Df-R and C_No15=Dr-R)</p> <p>- Two source locations: Dode 1 and Dode 2</p> <p>- Music/signal tested: Mahler, Trumpet, Pink noise</p> <p>- paper: https://www.mdpi.com/2076-3417/10/12/4370</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Churchill Diffuse Auroral Campaign Tape-18 2002-03-15
<p>A raw video file taken March 15, 2002 during an auroral campaign in Churchill MB, Canada. The images were taken from an intensified, narrow field-of-view CCD-based TV camera known as the Portable Auroral Imager (PAI) . They span from approximately 6:40 to 8:40 UT. The imager was equipped with a 25 mm lens in addition to a Wratten 89B IR filter with a cutoff wavelength of 650 nm. The field-of-view is 30.9 by 23.2 degrees. The images face approximately south of zenith and between elevation angles 66.8 to 90 degrees. </p> <p>The video contains examples of an auroral phenomenon known as Diffuse Auroral Eraser Events. </p>
Supplementary materials for Gast, V. and M. Koptjevskaja-Tamm, 'Colexification patterns in Europe: A study of persistence and diffusibility in the lexicon, based on the Database of Crosslinguistic Colexifications (CLICS3)
<p>The folder contains the data, scripts and plots for the paper. It includes the following third party material (Open Access) in the folder DataStageI:</p> <p>1) the clics.sqlite database from https://github.com/clics/clics3; cf. Rzymski, Christoph and Tresoldi, Tiago et al. 2019. The Database of Cross-Linguistic Colexifications, reproducible analysis of cross- linguistic polysemies. <a href="https://doi.org/10.1038/s41597-019-0341-x">DOI: 10.1038/s41597-019-0341-x</a><br> 2) the files ccCosineDist.csv and pmiWorld.csv from Jäger (2018), 'Global-scale phylogenetic linguistic inference from lexical resources', Scientific Data 5, Article number: 180189<br> 3) languoid.csv from https://glottolog.org/meta/downloads; cf. Hammarström, Harald & Forkel, Robert & Haspelmath, Martin & Bank, Sebastian. 2020. Glottolog 4.2.1. Jena: Max Planck Institute for the Science of Human History.<br> (Available online at http://glottolog.org, Accessed on 2020-07-11.)<br> * asjp.tsv from https://asjp.clld.org/, cf. Wichmann, Søren, Eric W. Holman, and Cecil H. Brown (eds.). 2018. The ASJP Database (version 18). (current version: 2020)</p>
Data from: Alkaline phosphatase, lactic dehidrogenase, inflammatory variables and apparent diffusion coefficients from MRI for prediction of chemotherapy response in osteosarcoma
<p><span><b><i>Background</i></b><b>. </b>This present study aimed to assess if clinical, laboratory and MRI were an accurate benchmark in assessing the effectiveness of neoadjuvant chemotherapy in osteosarcoma patients.<b> <i>Methods. </i></b>This was an observational analytic study with a cross-sectional design. Research subjects were selected using the total sampling method from osteosarcoma patients who underwent neoadjuvant chemotherapy during the period between January 2017– July 2019. <b><i>Results</i>.</b>Of the 58 patients included in this study, 38 were male and 20 were female aged 5 - 67 years (mean,16-year-old. 37(63.8%) patients underwent neoadjuvant chemotherapy with CAI regimens and 13 (36.2%) with CA. The tumors were classified as stage <b>IIB </b>in 43 (74.1%) patients and stage III in15 (25.9%) patients. After undergoing neoadjuvant chemotherapy, 4 patients had poor MSTS, 30 patients had fair MSTS, 17 and 7 patients had good and excellent MSTS score, respectively. Spearman's test revealed no correlation between tumor necrosis after neoadjuvant chemotherapy with a reduction in tumor size and MSTS score. Wilcoxon test showed significant differences between ALP, ESR, and NLR before and after neoadjuvant chemotherapy in the poor-response group. We found no significant difference between L<b>DH</b> and LMR before and after neoadjuvant chemotherapy in the good-response group. We had 9 patients for ADC value only. No significant statistical differences were found in tumor volumes after chemotherapy in both groups. <b><i>Conclusion.</i></b> We demonstrated that ALP level after neoadjuvant chemotherapy was markedly decreased, and was statistically significant in the poor-response group. We also demonstrated that LDH value before neoadjuvant chemotherapy had a strong correlation with degree of necrosis and could be used as a predictive indicator. NLR and LMR cannot be independent prognostic factors. MRI plays an important role in evaluating tumor volumes and preoperative radiological changes, using DWI and water diffusion to predict histological necrosis. </span></p> <p> </p> <p> </p>
Data from: Post-operative follow-up for selected diffuse low-grade gliomas with WHO grade III/IV foci
Objective: Diffuse low-grade gliomas (DLGG) are defined by a continuous growth and an almost unavoidable malignant transformation. Foci of malignant glioma can be found within DLGG samples obtained from surgical resections. As the medical management of patients is classically based on the higher tumor grade, an immediate adjuvant treatment is usually proposed. To determine whether postponing the medical treatment in selected patients is feasible, we conducted a single-center retrospective study. Methods: Single-center retrospective analysis of a consecutive series of DLGG managed with this conservative strategy. Inclusion criteria were: at least one focus of malignant tumor (grade III-IV, WHO 2016), no previous chemotherapy or radiotherapy, no less than a subtotal resection of the FLAIR tumor volume, no intention of treating with immediate adjuvant therapy, minimum two years of follow-up. The time interval to the following oncological medical treatment was analyzed, as well as the functional and survival results. Results: 45 patients met the inclusion criteria (median age 36.5, median time interval from diagnosis: 7.3 months). Most tumors (86.7%) were IDH-mutant and 1p19q intact (60.0%); 10 presented with grade IV foci. With a median follow-up of 6.3 years, 75.5% of patients received a subsequent medical treatment, after a median time of 3.7 years since surgery. At the time of analysis, 9 patients (20.0%) had died (5-years and 7-years survival rates: 95.2% and 67.0%). Most surviving patients were still active professionally, without seizures. Conclusions: Postponing the medical treatment in DLGG with foci of malignant tumor following total or subtotal resection should be considered in selected patients.
Energy Balance and Time Dependence of a Magnetotail Electron Diffusion Region
<p>The data sets and python notebook included are for a submitted manuscript to the Journal of Geophysical Review: Space Physics.</p> <p>An IDL save file has also been included which contains variables from a reconstruction of the event in the manuscript. The time variable in tt2000. Other variables are in units of nW/m^3 by default. <br> <br> The purpose of the work is to determine how electromagnetic energy is converted into plasma energy in the electron diffusion region of magnetic reconnection. Using both spacecraft data and simulations, we investigate how the terms in Poynting's theorem change as reconnection begins and evolves in time. </p>
Bayesian inference of tree species using diffusion models: tabulated posterior statistics for SNAPP and SNAPPER analyses
<p>We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of datasets containing hundreds or thousands of individuals. The method, which we call \snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements and accuracy recovering known model parameters. A re-analysis of soybean SNP data demonstrates that the models implemented in \snapp and \snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP dataset sampled from 399 fresh water turtles in 41 populations.</p>
Data from: Phylogeographic estimation and simulation of global diffusive dispersal
<p>The analysis of time-resolved phylogenies (timetrees) and geographic location data allows estimation of dispersal rates, for example for invasive species and infectious diseases. Many estimation methods are based on the Brownian Motion model for diffusive dispersal on a 2-dimensional plane, however the accuracy of these methods deteriorates substantially when dispersal occurs at global scales because Spherical Brownian Motion (SBM) differs from planar Brownian Motion. No statistical method exists for estimating SBM diffusion coefficients from a given timetree and tip coordinates, and no method exists for simulating SBM along a given timetree. Here I present new methods for simulating SBM along a given timetree, and for estimating SBM diffusivity from a given timetree and tip coordinates using a modification of Felsenstein's independent contrasts and maximum-likelihood. My simulation and fitting methods can accommodate arbitrary time-dependent diffusivities and scale efficiently to trees with millions of tips, thus enabling new analyses even in cases where planar BM would be a sufficient approximation. I demonstrate these methods using a timetree of marine and terrestrial Cyanobacterial genomes, as well as timetrees of two globally circulating Influenza B clades. My methods are implemented in the R package "castor."</p>
Scale and Space Dependent Horizontal Eddy Diffusivity at the Global Surface Ocean
<p>Sea surface temperature (SST) and idealized temperature tracer based estimates of horizontal diffusivity at the ocean surface. See https://doi.org/10.5281/zenodo.4126315 for the code to reproduce these estimates.</p> <p>There are two files which include the following</p> <p>KxKy_SST_MITGCM_025.nc</p> <p>SST based estimates are based on 35 years of satellite SST data (1982-2017) and we have divided the data in 30 chuncks, both using a 5 year and 10 year sub-sampling. The 'ens' dimension marks these chuncks whereas 'lat' and 'lon' mark the two Cartesian directions (latitude and longitude). Note that 'ens' can be also viewed as time.</p> <p>There are two fields 'Kx' and 'Ky' for each dataset ('SST' for the OI-SST based estimates or 'MITGCM' for the tracer simulation based estimates) and length of sub-sampling. 'Kx' is the diffusivity in 'lon' direction whereas 'Ky' is the diffusivity in 'lat' direction. Data is given at 0.25° resolution, but the effective resolution is twice that i.e. 0.5°.</p> <p>K_SST_MITGCM_2deg.nc</p> <p>Scale dependent estimate of the diffusivity magnitude i.e. <span class="math-tex">\(K= \sqrt{K_xK_y}\)</span> . As for the other data, here as well the estimates are done using 5 year and 10 year sub-sampling, and all the data is interpolated to a common 2° by 2° global grid. The coordinate 'dg' defines the scale in degrees. Note that again the effective resolution of the estimates is twice the 'dg'.</p> <p> </p> <p> </p>
Inland limits to diffusion of thinning along Greenland Ice Sheet outlet glaciers
<p>This dataset contains limits to the inland diffusion of terminus-initiated thinning along 141 Greenland Ice Sheet outlet glaciers. The data is separated in 187 files and each file contains data for an individual outlet glacier or a branch of an outlet glacier. Shapefiles and NetCDF files are provided with data for 6 primary flowlines, and up to 18 iterated flowlines, spanning the width of each glacier. The iterated flowlines are used to find the furthest inland thinning limits (see the associated publication for more details). Files are named <em>glacierXXXX </em>where <em>XXXX </em>is the identifier for an individual glacier or branch. Identifiers that begin with a letter ('a', 'b', 'c', or 'd') represent a separate branch of an outlet glacier. Shapefiles filenames have an additional <em>_iterNN </em>suffix, where <em>NN</em> identifies the iteration number of that set of flowlines.</p> <p>The shapefiles contain multiple features, each of which is one flowline. Each flowline has a "flowline" attribute, a two-digit identifier that corresponds to the same identifier in the NetCDF files. The NetCDF files contains glacier geometry, dynamic thinning, Peclet number, and identified knickpoints, extracted and calculated along the primary and iterated flowlines. The flowlines in the NetCDF files have a regular 50-meter spacing between nodes, whereas the flowlines in the shapefiles have a coarser and irregular spacing.</p> <p>More information on how the flowlines were derived and how data was extracted and calculated is in the associated publication (Felikson et al., 2020), with a DOI to be provided. Example code that can be used to read and plot the data can be obtained at http://doi.org/10.5281/zenodo.4284715.</p>
Data from: Non-invasive monitoring of temporal and spatial blood flow during bone graft healing using diffuse correlation spectroscopy
Vascular infiltration and associated alterations in microvascular blood flow are critical for complete bone graft healing. Therefore, real-time, longitudinal measurement of blood flow has the potential to successfully predict graft healing outcomes. Herein, we non-invasively measure longitudinal blood flow changes in bone autografts and allografts using diffuse correlation spectroscopy in a murine femoral segmental defect model. Blood flow was measured at several positions proximal and distal to the graft site before implantation and every week post-implantation for a total of 9 weeks (autograft n = 7 and allograft n = 10). Measurements of the ipsilateral leg with the graft were compared with those of the intact contralateral control leg. Both autografts and allografts exhibited an initial increase in blood flow followed by a gradual return to baseline levels. Blood flow elevation lasted up to 2 weeks in autografts, but this duration varied from 2 to 6 weeks in allografts depending on the spatial location of the measurement. Intact contralateral control leg blood flow remained at baseline levels throughout the 9 weeks in the autograft group; however, in the allograft group, blood flow followed a similar trend to the graft leg. Blood flow difference between the graft and contralateral legs (ΔrBF), a parameter defined to estimate graft-specific changes, was elevated at 1–2 weeks for the autograft group, and at 2–4 weeks for the allograft group at the proximal and the central locations. However, distal to the graft, the allograft group exhibited significantly greater ΔrBF than the autograft group at 3 weeks post-surgery (p < 0.05). These spatial and temporal differences in blood flow supports established trends of delayed healing in allografts versus autografts.
Fiber-tract localized diffusion coefficients highlight patterns of white matter disruption induced by proximity to glioma
Gliomas account for 26.5% of all primary central nervous system tumors. Recent studies have used diffusion tensor imaging (DTI) to extract white matter fibers and the diffusion coefficients derived from MR processing to provide useful, non-invasive insights into the extent of tumor invasion, axonal integrity, and gross differentiation of glioma from metastasis. Here, we extend this work by examining whether a tract-based analysis can improve non-invasive localization of tumor impact on white matter integrity. This study retrospectively analyzed preoperative magnetic resonance sequences highlighting contrast enhancement and DTI scans of 13 subjects that were biopsy confirmed to have either high or low-grade glioma. We reconstructed the corticospinal tract and superior longitudinal fasciculus by applying atlas-based regions of interest to fibers derived from whole-brain deterministic streamline tractography. Within-subject comparison of hemispheric diffusion coefficients (e.g., fractional anisotropy and mean diffusivity) indicated higher levels of white matter degradation in the ipsilesional hemisphere. Novel application of along-tract analyses revealed that tracts traversing the tumor region showed significant white matter degradation compared to the contralesional hemisphere and ipsilesional tracts displaced by the tumor.
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)
Individual variability in delay of gratification (DG) is associated with a number of important outcomes in both non-human and human primates. Using diffusion tensor imaging (DTI), this study describes the relationship between probabilistic estimates of white matter tracts projecting from the caudate to the prefrontal cortex (PFC) and DG abilities in a sample of 49 captive chimpanzees (Pan troglodytes). After accounting for time between collection of DTI scans and DG measurement, age and sex, higher white matter connectivity between the caudate and right dorsal PFC was found to be significantly associated with the acquisition (i.e. training phase) but not the maintenance of DG abilities. No other associations were found to be significant. The integrity of white matter connectivity between regions of the striatum and the PFC appear to be associated with inhibitory control in chimpanzees, with perturbations on this circuit potentially leading to a variety of maladaptive outcomes. Additionally, results have potential translational implications for understanding the pathophysiology of a number of psychiatric and clinical outcomes in humans.
Data from: Indirect interactions influence contact network structure and diffusion dynamics
Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT) based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics with a high rate of disease transmission. By making the underlying connectivity denser and stronger due to the inclusion of indirect transmissions, SPDT models are more realistic than SPST models for the study of various airborne diseases outbreaks. Importantly, we also find that the diffusion dynamics including indirect links are not reproducible by the current SPST models based on direct links, even if both SPDT and SPST networks assume the same underlying connectivity. This is because the transmission dynamics of indirect links are different from those of direct links. These outcomes highlight the importance of the indirect links for predicting outbreaks of airborne diseases.
Data from: Histopathology of diffusion imaging abnormalities in cerebral amyloid angiopathy
Objective: we sought to determine the underlying mechanism for altered white matter diffusion tensor imaging (DTI) measures at the histopathologic level in patients with cerebral amyloid angiopathy (CAA). Methods: formalin-fixed intact hemispheres from nine CAA cases and two elderly controls were scanned at 3 tesla MRI, including a diffusion-weighted sequence. DTI measures (i.e. fractional anisotropy (FA) and mean diffusivity (MD)) and histopathology measures were obtained from two tracts: the anterior thalamic radiation (ATR) and inferior longitudinal fasciculus (ILF). Results: FA was reduced in both tracts and MD was increased in CAA cases compared to controls. Regional FA was significantly correlated with tissue rarefaction, myelin density, axonal density, and white matter microinfarcts. MD correlated significantly with tissue rarefaction, myelin density, and white matter microinfarcts, but not axonal density. FA and MD did not correlate with oligodendrocytes, astrocytes, or gliosis. Multivariate analysis revealed that tissue rarefaction (β=-0.32±0.12, p=0.009) and axonal density (β=0.25±0.12, p=0.04) were both independently associated with FA, whereas myelin density was independently associated with MD (β=-0.32±0.12, p=0.013). Finally, we found an association between increased MD in the frontal white matter and CAA severity in the frontal cortex (p=0.035). Conclusions: these results suggest that overall tissue loss, and in particular axonal and myelin loss, are major components underlying CAA-related alterations in DTI properties observed in living patients. The findings allow for a more mechanistic interpretation of DTI parameters in small vessel disease and for mechanism-based selection of candidate treatments to prevent vascular cognitive impairment.
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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.