Announcing updates to the 1200 Subject restricted data available in ConnectomeDB:
“HasGT” (has genotyping data) measure added. This measure enables users to ascertain which twin and family structure relationships are verified by genotyping and which are based on original self report. Full genotyping data is not yet available–stay tuned!
“Family_ID” measure added to facilitate finding HCP subjects who are biological siblings (verified by genotyping, if available). Note: Family_ID does not indicate shared upbringing/rearing environment.
Test-Retest-Interval (days between initial HCP protocol visit and retest visit) measure added for HCP Retest subjects.
Viewing/downloading these restricted data from ConnectomeDB requires HCP restricted data access. If you would like to apply for restricted data access download the application here.
If you have downloaded the S1200 restricted data since the March 1, 2017 release, we recommend updating your files with the current family structure data.
We have also updated the documentation to fully describe the new measures and add overall summary data of total numbers of subjects with data for the HCP protocol modalities, numbers of MZ and DZ twins, and family structure types.
Specifically, look for changes in these HCP S1200 Reference Manual sections: Introduction and Overview (pp. 7-20), S1200 HCP Subject Family Structure (pp. 78-81), Zygosity, Genotyping, Family and Parent IDs (pp. 180-182).
The Human Connectome Project (HCP) WU-Minn consortium is pleased to announce the 1200 Subjects Release of HCP image and behavioral data, its final release of new HCP Subjects.
The 1200 Subjects release includes behavioral/demographic and 3T MR imaging data from 1206 healthy young adult participants collected August 2012‒October 2015, including:
3T MR structural scans available for 1113 subjects.
889 subjects have fully complete data for all of the four 3T MRI modalities in the HCP protocol: structural images (T1w and T2w), resting-state fMRI (rfMRI), task fMRI (tfMRI), and high angular resolution diffusion imaging (dMRI).
46 subjects (all monozygotic twins) have 3T HCP protocol Retest data available.
84 subjects also have 7T multimodal MR scan data available (in addition to 3T MR scans). 175 of the 7T subjects have fully complete data for all of the 7T MRI modalities in the HCP protocol: rfMRI, retinotopy fMRI, movie-watching fMRI, and dMRI.
95 subjects also have at least some resting-state MEG (rMEG) and/or task MEG (tMEG) data available (in addition to 3T MR scans).
What’s new in the S1200 release?
All released 3T Diffusion data on all HCP subjects re-preprocessed with updated diffusion preprocessing pipeline and with the BEDPOSTX diffusion analysis pipeline. All 3T diffusion data was re-preprocessed using an updated diffusion pipeline that supports an updated version of FSL’s EDDY that significantly improves slice outlier detection to remove noise caused by subject movement. Additionally, 3T diffusion MRI data for all subjects is further processed with FSL’s BEDPOSTX to model white matter fiber orientations and crossing fibers for probabilistic tractography.
Addition of 3T Retest data. Subjects (all monozygotic twins, 21 twin pairs + 4 MZ twins without retest of co-twin) were recruited to undergo the full 3T HCP imaging and behavioral protocol for a second time. Retest datasets are available in the separate WU-Minn HCP Retest Data project on the ConnectomeDB splashpage.
Addition of preprocessed 7T Diffusion data. Only unprocessed Diffusion data was released as part of the Initial 7T Release (June 2016). For all 7T subjects with dMRI scans, 7T dMRI data preprocessed with the updated diffusion pipeline is now available.
Resting State Stats added to 7T rfMRI ICA-FIX cleaned datasets. Resting state stats were not available as part of the 7T rfMRI ICA-FIX Extended packages of the Initial 7T Release (June 2016). These files provide information about different types of ’noise’ and ’signal’ in HCP resting state data, gleaned by partitioning the variance according to different processing stages in the FIX denoising pipeline.
Genetically verified family structure measures. We have updated restricted data measures for Mother_ID, Father_ID and ZygosityGT based on genotyping data available from blood and saliva samples from HCP subjects. For some subjects, these genetically verified values for these measures have changed from what they were in previous releases. Notably, 36 HCP twin pairs who self reported (now the ZygositySR measure) as dizygotic twins were found to be genetically monozygotic.
QC_Issue measure added. A subject data measure has been added to ConnectomeDB to flag subjects with notable brain anatomical, processing, or data noise issues found in the HCP Quality Control process. The issues are notable, but were not considered severe enough to exclude the subject’s imaging data from release. Codes for each issue included are detailed in the S1200 Reference Manual and more specific information on these subjects is available on the HCP QC Issues wiki page.
Soon to be available:
Genetic data on all HCP subjects. Genome-wide Association Study (GWAS) analyzed data for all HCP subjects with useable blood or saliva-based genetic material will be deposited and available on NIH’s dbGaP in March/April 2017.
Updated group-average rfMRI dense connectivity data and tfMRI data. MSM-All-registeredgroup-average rfMRI dense connectivity data for a 1000+ group of S1200 subjects with complete rfMRI data and MSM-Sulc and MSM-All-registered group-average tfMRI data for all S1200 subjects with complete tfMRI data are planned to be available in late spring 2017. These data will be released as Connectome Workbench-compatible datasets.
Updated parcellation, timecourse, and netmap (PTN) data for all S1200 subjects with complete rfMRI data. Updated PTN data for a 1000+ group of S1200 subjects is planned to be available in late spring 2017.
All S1200 imaging data soon to be available on the cloud through Amazon S3. HCP has continued our partnership with the Amazon Web Services Amazon Web Services (AWS) Public Data Sets program (http://aws.amazon.com/publicdatasets/) to offer storage and access to all HCP S1200 imaging data on Amazon S3 within several weeks of the 1200 Subjects Release. (Currently, S900 data is still available via S3)
Want more information? Check out the HCP 1200 Subjects Release Reference Manual for a comprehensive guide that includes details on imaging protocols, behavioral measures, and information that will help users obtain and analyze the 1200 Subjects data.
If you are actively using HCP data and tools, we encourage you to join and be active in the hcp-users discussion group (http://www.humanconnectome.org/contact/#subscribe), so that you can tune in to technical discussions on issues that may be of interest.
Thanks again for your interest in the HCP and enjoy the data!
Authors:Matthew F Glasser, Stephen M Smith, Daniel S Marcus, Jesper L R Andersson, Edward J Auerbach, Timothy E J Behrens, Timothy S Coalson, Michael P Harms, Mark Jenkinson, Steen Moeller, Emma C Robinson, Stamatios N Sotiropoulos, Junqian Xu, Essa Yacoub, Kamil Ugurbil & David C Van Essen
Abstract: Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The ‘HCP-style’ paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.
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