HCP Young Adult

Extensively Processed fMRI Data

Release Date: Jul 21, 2017
Type: Data Release
                                                                

HCP_S1200_GroupAvg_v1 Dataset (1096, 1003, 812 and 997 Subjects): This dataset (2.3 GB zip file) includes group-average structural and functional MRI data for the final HCP S1200 data release, including group average volumes, structural maps, Cohen's D effect-size maps for task contrasts, and links for viewing seed-based dense functional connectivity. Composite files containing maps for all 1096 MSMAll-registered individual subjects enable efficient between subjects comparisons of folding, ‘sulc’, myelin, and thickness data. To facilitate viewing and comparing data, the dataset includes a Connectome Workbench scene file, associated tutorial, and published cortical parcellations for reference, including the HCP-MMP1.0 (Glasser et al. 2016).

                                

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Group Average Functional Connectivity (1003 and 812 Subjects): Two MSMAll-registered group average dense functional connectomes for the final HCP S1200 data release are available from 1003 subjects having complete rfMRI data (recons r177 and r227), and a subset of 812 of these subjects whose fMRI data was reconstructed using the improved r227 recon algorithm that was in place in the latter two thirds of the HCP project. These large group average functional connectivity data are also viewable in a Connectome Workbench scene available in the HCP-S1200_GroupAvg_v1 dataset.

                                

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HCP1200 Parcellation + Timeseries + Netmats (1003 Subjects): Analyses based on data from 1003 subjects having complete rfMRI data (4800 timepoints, recon 177 + r227), yielding the following outputs at 6 ICA dimensionalities: Group-average parcellations yielded by group-ICA, subject-specific parcellations, subject-specific node timeseries, and a set of subject-specific parcellated connectomes.

                                

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Netmats Megatrawl (820 Subjects):  An analysis of the relationships between imaging and non-imaging measures in HCP S900 subjects using multivariate-prediction and univariate-regression.

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