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1000 Functional Connectomes Project (RRID:SCR_005361)


http://fcon_1000.projects.nitrc.org/

Database of resting state fMRI (R-fMRI) datasets collected from sites around the world. It demonstrates open sharing of R-fMRI data and aims to emphasize the aggregation and sharing of well-phenotyped datasets.


Keywords

resting state functional mri, fmri, brain, neuroimaging, phenotype, function, data sharing, human, mri, r-fmri, rs-fmri, fc-fmri, rs--fcmri, resting-state, dicom, dti, child, adolescent, brain imaging, neuroinformatics, adult human, phenotype, data set

Resource ID

SCR_005361

Alternate IDs

nlx_144428

Website Status

Last checked down

Parent Organization

NITRC

Abbreviation(s)

1000 FCP, FCP

Species

human

Resource Type

Resource, database, image collection, catalog, service resource, storage service resource, image repository, data repository, data or information resource

Used By

NIF Data Federation, DataLad, Integrated Datasets, MetaSearch

Availability

Account required, The community can contribute to this resource, Creative Commons Attribution-NonCommercial License

Synonym(s)

fcon_1000, 1000 Functional Connectomes Project INDI

Proper citation

1000 Functional Connectomes Project, RRID:SCR_005361

Alternate URLs

http://www.nitrc.org/projects/fcon_1000/

Other resources frequently mentioned in the literature with this resource

SciCrunch Registry

Interactive portal for finding and submitting biomedical resources. Resources within SciCrunch are assigned RRIDs which are used to cite resources in scientific manuscripts.

Making data sharing work: the FCP/INDI experience.

  • Mennes M
  • Neuroimage
  • 2013 Nov 15

Over a decade ago, the fMRI Data Center (fMRIDC) pioneered open-access data sharing in the task-based functional neuroimaging community. Well ahead of its time, the fMRIDC effort encountered logistical, sociocultural and funding barriers that impeded the field-wise instantiation of open-access data sharing. In 2009, ambitions for open-access data sharing were revived in the resting state functional MRI community in the form of two grassroots initiatives: the 1000 Functional Connectomes Project (FCP) and its successor, the International Neuroimaging Datasharing Initiative (INDI). Beyond providing open access to thousands of clinical and non-clinical imaging datasets, the FCP and INDI have demonstrated the feasibility of large-scale data aggregation for hypothesis generation and testing. Yet, the success of the FCP and INDI should not be confused with widespread embracement of open-access data sharing. Reminiscent of the challenges faced by fMRIDC, key controversies persist and include participant privacy, the role of informatics, and the logistical and cultural challenges of establishing an open science ethos. We discuss the FCP and INDI in the context of these challenges, highlighting the promise of current initiatives and suggesting solutions for possible pitfalls.

Fully exploratory network independent component analysis of the 1000 functional connectomes database.

  • Kalcher K
  • Front Hum Neurosci
  • 2012 Nov 7

The 1000 Functional Connectomes Project is a collection of resting-state fMRI datasets from more than 1000 subjects acquired in more than 30 independent studies from around the globe. This large, heterogeneous sample of resting-state data offers the unique opportunity to study the consistencies of resting-state networks at both subject and study level. In extension to the seminal paper by Biswal et al. (2010), where a repeated temporal concatenation group independent component analysis (ICA) approach on reduced subsets (using 20 as a pre-specified number of components) was used due to computational resource limitations, we herein apply Fully Exploratory Network ICA (FENICA) to 1000 single-subject independent component analyses. This, along with the possibility of using datasets of different lengths without truncation, enabled us to benefit from the full dataset available, thereby obtaining 16 networks consistent over the whole group of 1000 subjects. Furthermore, we demonstrated that the most consistent among these networks at both subject and study level matched networks most often reported in the literature, and found additional components emerging in prefrontal and parietal areas. Finally, we identified the influence of scan duration on the number of components as a source of heterogeneity between studies.

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1000 Functional Connectomes Project: List

1000 Functional Connectomes Project is a fully open downloadable database of 1200 resting state fMRI datasets collected from 33 sites around the world.

  • Records: 22
License Information:
INDI Data Access Policy Given the increased amount of information provided in the INDI datasets, we are requiring that individuals register with the 1000 Functional Connectomes Project website on NITRC to gain access to the INDI datasets. Upon successful registration, users will have full access to the datasets, and the right to unrestricted usage of the datasets for non-commercial purposes, just as before. Registration is a simple process, as follows: If you have not already established an NITRC user account, you will be required to do so. It is easy to do and free of charge. Once registered as a NITRC user, click on the “REQUEST INDI ACCESS” button at the bottom of this page. In the free text box that appears, enter your name, institution, research level (e.g., grad student, post-doc, research scientist, faculty, etc), email address (we will not use this for any advertisement purposes) and area of expertise. Click submit – that’s it! Upon receipt of the requested info, you will be granted access by a member of our team. This might take a day! ---------------------------------------------------------------------------------------------------------------------------------- License: Attribution Non-Commercial, http://www.nitrc.org/include/glossary.php#546
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