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495 results for “Brain Development”
Brain Correlates of Math Development
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Brain Development of Deductive Reasoning
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A dataset recorded during development of an affective brain-computer music interface: calibration session
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A dataset recorded during development of an affective brain-computer music interface: testing session
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A dataset recorded during development of an affective brain-computer music interface: training sessions
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A dataset recorded during development of a tempo-based brain-computer music interface
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Data for "Gut microbial genes are associated with neurocognition and brain development in healthy children"
<p><strong>Datasets accompanying<em> Gut microbial genes are associated with neurocognition and brain development in healthy children</em>, submitted to Nature Microbiology.</strong></p> <p><strong>Contents:</strong></p> <ul> <li> fecal_samples_master.csv <ul> <li>Metadata for all fecal samples processed by the Klepac-Ceraj Lab at Wellesley College</li> </ul> </li> <li>filemakerdb.csv <ul> <li>Initial export and parsing (long form) of deidentified patient metadata from internal filemnaker pro database</li> </ul> </li> <li>gbm.txt <ul> <li>Info about potentially neuroactive gene sets</li> <li>This was acquired as Supplementary Dataset 1 from <a href="https://doi.org/10.1038/s41564-018-0337-x">https://doi.org/10.1038/s41564-018-0337-x</a></li> </ul> </li> <li>batchXXX_analysis_noknead.tar.gz <ul> <li>Sequencing batches 001-012 (see fecal_samples_master.csv for metadata about samples contained in each batch)</li> <li>Each tarball contains: <ul> <li><strong>cluster.yaml</strong>: configuration file for snakemake pipeline (<a href="https://github.com/Klepac-Ceraj-Lab/snakemake_workflows">repo link</a>)</li> <li><strong>config.yaml</strong>: run configuration for snakemake pipeline</li> <li><strong>.snakemake/</strong>: metadata about snakemake pipeline runs on engaging cluster at MIT</li> <li><strong>output/</strong>: outputs from metaphlan2 and humann2 analysis runs. Note: kneaddata sequence files were not included, but will be uploaded to SRA (link to come)</li> </ul> </li> </ul> </li> <li>All <a href="https://www.uniprot.org/">uniprot</a> searches were performed 2019-09-19 <ul> <li>uniprot-abxr.tsv <ul> <li>search term: "keyword:\"Antibiotic resistance [KW-0046]\" AND reviewed:yes"</li> </ul> </li> <li>uniprot-carbohydrate.tsv <ul> <li>search term: "keyword:\"Carbohydrate metabolism [KW-0119]\" AND reviewed:yes"</li> </ul> </li> <li>uniprot-fa.tsv <ul> <li>search term: (keyword:\"Fatty acid biosynthesis [KW-0275]\" OR keyword:\"Fatty acid metabolism [KW-0276]\") AND reviewed:yes"</li> </ul> </li> </ul> </li> </ul>
A Spatio-temporal Atlas of the Developing Fetal Brain with Spina Bifida Aperta
<p>This version contains two zipped folders.</p> <ol> <li><a href="https://zenodo.org/api/files/c84cc018-9d2d-4adb-9558-9ab96649c922/spina_bifida_atlas.zip">spina_bifida_atlas.zip</a> contains a copy of our spina bifida aperta fetal brain atlas.<br> This folder is available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication), as indicated in the LICENSE file in this folder.<br> This is the same version of the atlas as the one available on synapse (<a href="https://www.synapse.org/#!Synapse:syn25887675/wiki/611424">https://www.synapse.org/#!Synapse:syn25887675/wiki/611424</a>, DOI: 10.7303/syn25887675).</li> <li><a href="https://zenodo.org/api/files/c84cc018-9d2d-4adb-9558-9ab96649c922/LucasFidon/spina-bifida-MRI-atlas-0.1.0.zip?versionId=31751dfa-7b17-4c84-89d5-2769d648eed8">LucasFidon/spina-bifida-MRI-atlas-0.1.0.zip</a> is a copy of the code that was used to compute the fetal brain atlas for spina bifida aperta in this repository.<br> This folder is available under BSD-3-Clause license, archived from GitHub, as indicated in the LICENSE file in this folder.</li> </ol> <p><strong>How to cite:</strong><br> If you find the data in this folder useful for your research please cite:</p> <p>L. Fidon, E. Viola, N. Mufti, A. L. David, A. Melbourne, P. Demaerel, S. Ourselin, T. Vercauteren, J. Deprest, M. Aertsen. A Spatio-temporal Atlas of the Developing Fetal Brain with Spina Bifida Aperta, 2021.</p>
Sex chromosomes and hormones independently influence healthy brain development but act similarly after cranial radiation
<h2><strong>Description</strong></h2> <p>Biological sex influences prevalence of developmental disorders through sex hormones and sex chromosomes. However, our understanding of their impacts in neurodevelopment and response to injury remains limited. In this project, we use high resolution magnetic resonance imaging (MRI) to investigate the four core genotype mouse model (FCG) that separates the influences of sex hormones and sex chromosomes during normal brain development and after cranial radiation therapy. </p> <p>Sex differences are attributed to either sex hormones or sex chromosomes. This can be distinguished by the FCG model which decouples the sex determining region (SRY) from the Y chromosome by moving SRY onto an autosome. This gives us four core sex genotypes: XX NULL, XY NULL, XX SRY, and XY SRY.</p> <p>This dataset represents the <em>most comprehensive mouse brain imaging study</em> employing the FCG model to date with 5 timepoints (P14, P23, P42, P63, P98), Ccl2 wildtype (+/+) and knockouts (-/-), irradiation (7Gy) and sham (0Gy) mice. All in all, a total of <strong>1071 images</strong>! The results presented here is published in PNAS.</p> <p>In vivo MRI scans were obtained using a 7-T MRI scanner (Bruker BioSpin, Ettlingen, Germany) equipped with four cryocoils for simultaneous imaging of four mice. The scans were performed with the following settings: T1-weighted, 3D-gradient echo sequence, 75μm isotropic resolution, TR=26ms, TE=8.25ms, flip angle=26°, field of view=25×22×22mm, and matrix size=334×294×294.</p> <p>All structural MR images are stored in <strong>images.tar.gz</strong>. Images were segmented and registered using an automated pipeline which are stored in <strong>labels.tar.gz</strong>. The consensus average and labels are <strong>final_average.mnc </strong>and <strong>final_labels.mnc</strong>, respectively. Extracted structure volumes alongside the metadata are included in <strong>df_micevolumes.csv</strong>. Structural MRIs are in MINC format and the <strong>readme.txt</strong> provides further information on this dataset. </p> <p>The authors express their sincere gratitude for the research funding recieved from the Canadian Institutes of Health Research (158622, 168037) and the Ontario Institute for Cancer Research (IA-024) with funding from the Government of Ontario and Restracomp from the SIckKids Research Training Centre.</p> <p><strong>Publication</strong>: https://www.pnas.org/doi/10.1073/pnas.2404042121</p> <h2><strong>Code/Software </strong></h2> <p><strong>MINC</strong><br>https://www.bic.mni.mcgill.ca/ServicesSoftware/MINC</p> <p><strong>RMINC</strong><br>https://github.com/Mouse-Imaging-Centre/RMINC</p> <p><strong>PydPiper</strong><br>https://github.com/Mouse-Imaging-Centre/pydpiper/tree/v2.0.19.1</p>
Data supporting the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence"
<p>This data supports the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence" by Dorfschmidt et al. Part of these <a href="https://doi.org/10.6084/m9.figshare.11551602">data</a> were initially released by Váša et al. (2020) as part of their <a href="https://doi.org/10.1073/pnas.1906144117">manuscript</a>. Please cite them when using these data. </p> <p> </p>
Figure 2 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
Figure 2. Linear relationship between neonate brain volume and gestation duration (in days). The regression includes only delphinids. Other species were plotted but not included in the regression. The species O. orca is indicated by a black arrow. There is a strong, positive correlation between neonatal delphinid brain volume and gestation duration; gestation duration scales to the 0.23 power of neonatal brain volume.
Figure 1 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
Figure 1. There is a strong, positive correlation between maternal body mass and neonatal brain mass in these four delphinid species; neonatal brain mass scales to the 0.51 power of maternal body mass.
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 20. Overview about which Binding Mechanisms Work at what Hierarchical Levels and Development Stages of the Brain
<p>As a result of our research, in [60], a solution to the binding problem for perception was suggested by<br> combining the already existing binding hypotheses in a conclusive way, supplementing them with<br> other insights about the perceptual system of the brain, and translating them into a technically<br> implementable model. It was demonstrated via computational simulations that different binding<br> mechanisms proposed in literature are not mutually inclusive. On the contrary! At different<br> hierarchical levels and in different development stages, different binding mechanisms are acting in<br> perception. An overview about these circumstances is given in Figure 20. A detailed description can<br> be found in.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure7. Model 3D of women body.
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 7 .</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 2. Human body sizes for men/women.
<p>We propose an efficient, simple and robust human body feature extraction based on the front and side images of a human body. Description of anthropometric data - men/women: Dataset based on an experiment is used to test the system data describing the anthropometric features of men, includes 12 sizes of the human body, which are presented in figure 2. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 1. Flowchart of anthropometric system
<p> Our purpose is to develop an automatic measurement and modeling system based on 2D images (front and side images). This system used to image processing methods and machine learning algorithms. Our system has 3 main parts; there are human body feature extraction, training and testing processes, and the classification for new data. The novelty of our approach: - Classification of anthropometric features based on machine learning algorithms. - Development a non-contact anthropometric program for the smartphones on operation system Android. - Construction of a 3D-model of the human body based on the results of anthropometric features extraction. Our system can also be used to integrate to different environments, such as online shopping websites to support users fitting their clothes sizes and medical applications. The flowchart of our anthropometric system is described in figure 1. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 5. Flowchart of data classification
<p>The Random Forest is a powerful classification method because of the following. First, errors are minimized as a result of a random forest, synthesizing through training (learner). The second, random choice at every stage in the Random Forest will reduce the correlation between the learners in the synthesis of the results. In addition, we also found that the total error of layered forest trees depends on their individual errors in forest trees, as well as the correlation between the trees. The article uses the wrapper model (Christopher Tong, 2000) with the objective function for the evaluation, Random Forest algorithm is shown in figure 5. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 8. Model 3D of man body
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 8 .</p>
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.