• Research dataclose
• 2019-2023close
• Dataset close
• European Commission close
• Neuroinformatics close

EuropaBON list of Essential Biodiversity Variables

Agenda of EuropaBON virtual workshop on EBV workflows

Datasets provided for Open Platform for Ultrasound Localization Microscopy: Performance Assessment of Localization Algorithms. Abstract: Ultrasound Localization Microscopy (ULM) is an ultrasound imaging technique that relies on the acoustic response of sub-wavelength ultrasound scatterers to map the microcirculation with an order of magnitude increase in resolution. Initially demonstrated in vitro, this technique has matured and sees implementation in vivo for vascular imaging of organs, and tumors in both animal models and humans. The performance of the localization algorithm greatly defines the quality of vascular mapping. We compiled and implemented a collection of ultrasound localization algorithms and devised three datasets in silico and in vivo to compare their performance through 18 metrics. We also present two novel algorithms designed to increase speed and performance. By openly providing a complete package to perform ULM with the algorithms, the datasets used, and the metrics, we aim to give researchers a tool to identify the optimal localization algorithm for their usage, benchmark their software and enhance the overall image quality in the field while uncovering its limits. This article provides all materials and post-processing scripts and functions. Methods: 200.000 ultrasound images have been acquired in vivo on a rat brain with skull removal at 1000 Hz with a 15 MHz linear probe. This dataset contains raw radiofrequency data (RF) and beamformed images (IQ) of the brain vascularization with flowing microbubbles (ultrasound contrast agent). Article to be cited: Heiles, Chavignon, Hingot, Lopez, Teston and Couture. Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy, Nature Biomedical Engineering, 2022, (doi.org/10.1038/s41551-021-00824-8). Related processing scripts and codes: github.com/AChavignon/PALA Related datasets: doi.org/10.5281/zenodo.4343435 Acknowledgments: We thank Cyrille Orset (INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders, GIP Cyceron, BB@C, Caen, France) for animals’ preparation and perfusion of contrast agent and the biomedical imaging platform CYCERON (UMS 3408 Unicaen/CNRS, Caen, France).

The Marmoset Brain Mapping Project was launched in Nov. 2016, aiming at building comprehensive marmoset brain atlases and tools to facilitate neuroimaging and translational studies.With supports and helps from multiple labs and teams, including- Cirong Liu's lab (ION/CAS): Xiaojia Zhu, Fufui Feng- Afonso Silva's lab (NINDS/NIH): Afonso Silva, John Newman, Cecil Yen, Diego Szczupak, and Xiaoguang Tian;- David Leopold's lab (NIMH/NIH): David Leopold, and Frank Ye;- @MarmosetBrainConnectityAtlas: Marcello Rosa, and Piotr Majka;- AFNI (NIMH/NIH): Daniel Glen;- Zhifeng Liang's lab and CEBSIT MRI Facility (ION/CAS);- Gustavo Deco's lab (UPF): Gustavo Deco and Yonatan Sanz PerlWe are making significant progress during these years, not only in developing atlases for mapping the marmoset brain, but also in pushing the resolution limit of non-human primate MRI. These efforts resulted in useful atlases and tools for the marmoset research and valuable MRI data that can be of interests to researchers who are in the MRI research field.How to CiteFor Atlas-V1 (Cortical parcellaion) and 150um MRI data:Liu C, et al, A digital 3D atlas of the marmoset brain based on multi-modal MRI. Neuroimage (2018) doi: 10.1016/j.neuroimage.2017.12.004.For Atlas-V2 (White matter atlas) and 150um, 60um, 80um and 50um MRI data:Liu C, et al. A resource for detailed 3D mapping of white matter pathways in the marmoset brain. Nature Neuroscience (2020) doi:10.1038/s41593-019-0575-0.For Atlas-V3 (Population templates) :Liu C, et al. Marmoset Brain Mapping V3: Population multi-modal standard volumetric and surface-based templates. Neuroimage (2021) doi:10.1016/j.neuroimage.2020.117620.For Atlas-V4 (Functional connectivity) and associated in-vivo MRI data:Tian X, et al. An integrated resource for functional and structural connectivity of the marmoset brain. Nature Communications (2022) doi:10.1038/s41467-022-35197-2For Atlas-V5 (Cerebellum):Zhu X, et al. An Anatomical and Connectivity Atlas of the Marmoset Cerebellum. Cell Reports (2023)

Quality-controlled predictions of deep learning models for cell counting This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper: A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain. The dataset integrates the raw data published on a previous upload on Zenodo. Cell locations were obtained using two deep-learning models for cell counting (publicly available on GitHub, details in the paper by Ciampi et al., 2022). The output of the deep-learning pipeline was filtered based on the score assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. Content The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper. Folder Structure and file naming conventions There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells). This work was funded by: AI4Media - A European Excellence Centre for Media, Society and Democracy (EC, H2020 n. 951911); the Tuscany Health Ecosystem (THE) Project (CUP I53C22000780001), funded by the National Recovery and Resilience Plan (NRPP), within the NextGeneration Europe (NGEU) Program; PRIN2017 2017HMH8FA to T.P., R.M. was supported by Fondazione Umberto Veronesi.

IsSupplementTo: Andreas Schuh, Antonios Makropoulos, Emma C. Robinson, Lucilio Cordero-Grande, Emer Hughes, Jana Hutter, Anthony N. Price, Maria Murgasova, Rui Pedro A. G. Teixeira, Nora Tusor, Johannes Steinweg, Suresh Victor, Mary A. Rutherford, Joseph V. Hajnal, A. David Edwards, and Daniel Rueckert, Unbiased construction of a temporally consistent morphological atlas of neonatal brain development, bioRxiv, 2018. (https://doi.org/10.1101/251512) This repository contains multi-channel spatio-temporal MRI atlas of the normal fetal brain development during 21-36 weeks GA cretated as a part of the Developing Human Connectome Project (dHCP).

The Glossaryfication Web Service: an automated glossary creation tool to support the One Health community

This repository contains the raw data from 8-probe Neuropixels recordings in the mouse brain, as described in this paper: C. Stringer, M. Pachitariu, C. Bai Reddy, M. Carandini, K. D. Harris, Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, 255 (2019). https://doi.org/10.1126/science.aav7893 Electrophysiological recordings were performed from each of three mice (named Krebs, Robbins, and Waksman) with eight Neuropixels 1.0 probes simultaneously.  This repository contains: raw electrophysiological traces in the high-frequency "AP band"  raw electrophysiological traces in the low-pass filtered "LFP band"; movies of the face and of the eye;  details of the visual stimuli presented to the mice; synchronization information between these datasets; output of spike sorting (these have not been manually reviewed and should be reviewed before use).  For details on the experimental preparation, see the aforementioned paper (Stringer, Pachitariu, et al, Science 2019). The traces in the "AP band" were losslessly compressed using mtscomp algorithm available here: https://github.com/int-brain-lab/mtscomp  For details about the behavior videos, or to download these separately, see:  https://figshare.com/articles/media/Behavioral_videos_for_8-Neuropixels_recordings_from_Stringer_et_al_2019_Science/8378360 For details about the LFP data, or to download it separately, see:  https://figshare.com/articles/dataset/LFP_data_for_8-Neuropixels_recordings_from_Stringer_et_al_2019_Science/8784311 For information about probe locations in CCF coordinates, see: https://figshare.com/articles/dataset/Eight-probe_Neuropixels_recordings_during_spontaneous_behaviors/7739750 For the spiking data used in Stringer, Pachitariu, et al 2019, which are carefully curated spike sorting results from a subset of each recording (containing spontaneous activity but not visual stimulus periods), see: https://figshare.com/articles/dataset/Eight-probe_Neuropixels_recordings_during_spontaneous_behaviors/7739750

If you are stuck in a traffic jam, the more numerous the queuing cars are, the longer you expect to wait. Time and numerosity are stimulus dimensions often associated in the same percept and whose interaction can lead to misjudgements. At brain level it is unclear to which extent time and numerosity recruit same/different neural populations and how their perceptual integration leads to changes in these populations' responses. Here we used high-spatial resolution fMRI with neural model-based analyses to investigate how the topographic representation of numerosity and time change when these dimensions are varied together on the same visual stimulus in a congruent (the more numerous the items, the longer the display time) or incongruent manner. Compared to baseline conditions, where only one dimension was changed at a time, the variation of both stimulus dimensions led to changes in neural population responses that either became more sensitive to the two features or to one of them. Magnitude integration led also to degradation of topographies and shifts in response preferences. These changes were more pronounced in the comparison between parietal and frontal maps. Our results while pointing out to partially distinct representations of time and numerosity show a common neural response to magnitude integration.

Additional file 1: Supplementary file 1: Table 1. Full hub metabolite results (models 1-3).