| 词条 | Draft:1000 Functional Connectomes Project |
| 释义 |
Introduction1000 Functional Connectomes Project is a project collecting resting-state functional magnetic resonance imaging data around the world to build a neuroimaging database for further research. It demonstrates open sharing of R-fMRI data and aims to emphasize the aggregation and sharing of well-phenotyped datasets. The 1000 Functional Connectomes dataset aims to:[1]
It is the parent project for ABIDE, ADHD-200 (ADHD200), INDI, CORR, NKI-Rockland (NKI), Healthy Brain Network (HBN) and other projects. The project is described in the article [2] by Bharat B. Biswal et al. The image repository for the ‘1000 Functional Connectomes’ Project is located at here, which is a fully open downloadable database of over 1200 resting state fMRI datasets collected from 35 sites around the world. Project SpecificationsRelated information about datasets in this project can be found [https://www.nitrc.org/projects/fcon_1000/ here] Data SourceAll data sets are donated by key researchers at member sites to provide full access to large-scale functional imaging data sets for the wider imaging community. Age, gender and imaging center information for each data set contained in the repository are provided.[3] The NIFTI image format (direction: RPI; TR information set in the file) is used to organize the functional imaging data set. All functional datasets provided by contributors have been added to the repository without modification, except for conversion to NIFTI format and uniform direction. Anatomical images undergo facial disruption to protect participants'identities. Data from each site in the repository can be downloaded separately to provide maximum flexibility for users. Whether the data quality or the specific purpose of the repository data recipient is appropriate or not, the 1000 Functional Connection Group project data set is freely available without any guarantee or qualification. Because data publishing is not restricted, researchers are free to publish any part of the data set they choose. CommentsYou can see the comments of this platform from [https://www.nitrc.org/rating/show_results.php?group_id=296 here] SupportYou may get support from the [https://www.nitrc.org/forum forum] and [https://www.nitrc.org/tracker tracker] of nitrc. Similar Projects
UsageData TypePhenotypic DataPhenotypic data are clinical information about the symptoms of a patient's illness and related demographic data, such as age, race and gender.Such information is collected and stored by patient registries and biobanks.[4] Medical ImagingData categoryR-fMRI, DTI, sMRIThe joint multivariate analysis of multiple data types (e.g., resting state fMRI, task-related fMRI, DTI, and sMRI) will improve our ability to understand brain diseases.(Combination of resting state fMRI, DTI, and sMRI data to discriminate schizophrenia by N-way MCCA + jICA[5] ) Structural and functional brain scansTake the cross-sectional SWU Adult Life Data Set as an example. The data set covers functional magnetic resonance imaging (fMRI) data of adult life span, including structural MRI and resting state functional MRI. Four hundred and ninety-four healthy adults (aged 19-80 years, including 187 males) were recruited. Two multi-mode MRI scans were performed at Brain Imaging Center of Southwest University of Chongqing, China.(Structural and functional brain scans). Behavioral assessments and phenotypic informationThe data set consists of topics used by Adelstein and others in the PLoS ONE study in 2011, entitled "Personality is reflected in the brain’s intrinsic functional architecture." Investigators can include these data in their own data sets so that they can copy our results or conduct similar surveys using improved methods. The data set consisted of male and female adults, and all healthy controls had no history of mental illness. The subjects were scanned in the Siemens 3T Allegra scanner with a continuous eye-opening program, and fixed on relaxation. Detailed information can be found in the attached materials.[6] MorphometricData download & Retrieval
Data FormatBrain imaging data structure (BIDS)In neuroimaging experiments, complex data are arranged in many different ways. Therefore, there was no consensus on how to organize and share the data. BIDS, however, is a simple and easy-to-use method for tissue neuroimaging and behavioral data.[7] A good introduction to the BIDS standard can be found in the [8]paper published in Nature Scientific Data Age, sex and imaging center information are provided for each of the datasets.[9] Related toolsThere are file transfer programs that can handle S3 natively and will allow you to navigate through the data using a file browser. Cyberduck is one such program that works with Windows and Mac OS X. Cyberduck also has a command line version that works with Windows, Mac OS X, and Linux.[10] Download from Amazon S3Other Related StudiesThe 1000 Functional Connectomes Project's organization platform, funding structures, standardized methodologies, and open datasharing approaches have been used in a number of different studies(Database Foundation & Article Studying). Databases related
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