NIH Blueprint: The Human Connectome Project

News and Updates

Press Releases,Project News | January 12, 2016

Announcing release of S900 “PTN” and other Group Average Data

Screen Shot 2016-01-12 at 3.04.07 PMWe are pleased to announce the release of extensively processed data associated with the HCP S900 Data Release, which is first HCP data release to capitalize on cortical areal feature-based surface registration (“MSMAlll”, using folding, myelin maps, and resting-state networks).

The release includes (i) the “PTN” dataset (Parcellation + Timeseries + Netmats); (ii) group average dense functional connectomes; and (iii) other group average data (plus composite of individual-subject maps).

Download the data here (requires ConnectomeDB login).

PTN (Parcellation + Timeseries + Netmats) dataset. This analysis is based on data from all 820 subjects in the S900 data release having four complete rfMRI runs (with 100% of collected timepoints), yielding the following outputs:

  1. Group-average “parcellations”, obtained by means of group-ICA.
  2. Subject-specific sets of “node timeseries” – for each subject, a representative time series per ICA component (“parcel”).
  3. A subject-specific “parcellated connectome” – for each subject, a nodes x nodes matrix – the functional connectivity between node timeseries.

Additional information is provided in the pdf (HCP900_GroupICA+NodeTS+Netmats_Sumary_15dec2015.pdf) included in the download.

HCP_S900_GroupAvg_v1 dataset. This dataset (1.4 GB zip file) includes group-average structural and functional MRI data for the HCP S900 data release (December, 2015). Also included are composite files containing MSMAll-registered maps of folding, ‘sulc’, myelin, and thickness that enable efficient navigation of individual subject data. An associated tutorial document aids in viewing the data using Connectome Workbench ‘wb_view’ visualization software.

HCP_S900 group average functional connectivity. Two group average dense functional connectomes have been generated from 820 subjects in the S900 release, one based on MSMAll (recommended for most analyses) and the other based on MSMSulc (less well aligned, available for comparison purposes). Because these are large (33 GB) files, we recommend accessing them remotely, as explained in the tutorial document noted above. However, they can also be downloaded directly from ConnectomeDB via:

https://db.humanconnectome.org/app/action/ChooseDownloadResources?project=HCP_Resources&resource=GroupAvg&filePath= HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.zip

and https://db.humanconnectome.org/app/action/ChooseDownloadResources?project=HCP_Resources&resource=GroupAvg&filePath= HCP_S900_820_rfMRI_MSMSulc_groupPCA_d4500ROW_zcorr.zip

Press Releases,Project News | December 8, 2015

Announcing the HCP 900 Subjects Data Release

S900_Release_vertThe Human Connectome Project (HCP) WU-Minn consortium is pleased to announce the release of 900 Subjects of HCP image and behavioral data, including 95 MEG datasets.

What’s in the HCP 900 Subjects data release? The 900 Subjects release includes behavioral/demographic and 3T MR imaging data from 970 healthy adult participants collected in 2012-spring 2015. Structural scans are available for 897 subjects. 730 subjects completed all of the four 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). In addition to MR scans, 95 subjects also have at least some resting-state MEG (rMEG) and/or task MEG (tMEG) data available, with 50 subjects completing the entire HCP MEG protocol.

The 900 Subjects release also includes:

Addition of MSM-All registration and data in minimally preprocessed and analysis packages. In addition to MSM-Sulc (cortical folding only-based registration) introduced in the S500 release, the structural MRI processing pipeline now also performs intersubject registration using individual cortical folding, myelin map, and rfMRI correlation data together (MSM-All) to a group-average surface template via a multimodal surface matching (MSM) algorithm (Robinson et al. 2014, Smith et al. 2013).

ConnectomeDB offers the “MSM-Sulc + MSM-All” package type for all S900 subjects and the “S500 to S900 Extension Only” packages to “patch” users’ existing S500 data with the MSM-All and other added files available for the S900 release.

Resting State fMRI FIX-Denoised (Extended) packages expanded. FIX-Denoised (Extended) rfMRI packages have been expanded to contain a “stats.dscalar.nii” and a “RestingStateStats” folder. 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.

Physiological data files corrected. We have corrected timing errors in the processed physiological log files for each scan that resulted in variable offsets in timing. These corrected .txt files are in the S900 data packages.

Source-level rMEG and tMEG data expanded to include beamformer connectivity results. New source-level processing pipelines using the Beamformer filters inverse algorithms have been implemented to estimate time-resolved dense connectomes in a number of frequency bands.

Parcellated rMEG band limited power envelope and connectivity results now available. Results of the Icablpenv, Icablpcorr, icaimagcoh, and bfblpenv pipelines are additionally available in parcellated versions that were created using the Yeo et al. 2011 17 network parcellation.

Soon to be available:

  • Updated group-average rfMRI dense connectivity and tfMRI data. Group-average rfMRI dense connectivity data and group-average analyzed task data for an 800+ group of S900 subjects with complete rfMRI data and tfMRI data is currently being prepared for release in December 2015. These data will be released as Connectome Workbench-compatible datasets.
  • Updated parcellation, timecourse, and netmap (PTN) data. Individual PTN data for all S900 subjects with complete rfMRI data (800+ subjects) is also being prepared for release.

All S900 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 S900 imaging data on Amazon S3 within weeks of the 900 Subjects Release. (Currently, S500 data is still available via S3)

Access 900 Subjects data on the HCP website. Explore, download, or order the HCP 900 Subjects dataset (~17TB of data!) via the ConnectomeDB database. Most HCP image and behavioral data is openly accessible to investigators worldwide who register and accept a limited set of Open Access Data Use Terms. Note: Please clear your browser cache before logging in to ConnectomeDB.

Want more information?  Check out the HCP 900 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 900 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.

Press Releases,Project News | September 30, 2015

Positive behavioral traits and functional connections covary in HCP Subjects

CCA_modeA just released finding from HCP data reveals a strong link between patterns of functional connectivity in the brain’s default mode network and “positive” personal traits such as fluid intelligence (IQ), language skills, and life satisfaction. The study conducted by WU-Minn HCP consortium investigators was released in Nature Neuroscience this week.

Authors searched patterns of resting state functional connectivity in 461 subjects from the HCP 500 Subjects Release, looked for correlations between 158 out-of-scanner behavioral and demographic measures in the same set of subjects, then applied a canonical correlation analysis (CCA) to find maximal relationships between the two sets of data over the population. The analysis revealed “modes” of co-variation across the population between subjects’ functional connectivity patterns and the behavioral/demographic measures that could be compared.

What they found was remarkable. Over the population studied, only one CCA mode stood out significantly relating functional connectomes to subject measures. This mode is strongly associated with high subject scores in cognition, memory, years of education, income level, and other “positive” traits and negatively associated with “negative” traits such as substance use and high anger/aggression scores. Brain connectivity patterns most strongly associated  with this mode include significantly higher connectivity in components of the default network, including medial frontal and parietal cortex, the temporo-parietal junction, the anterior insula, and the frontal operculum.

This finding resonates with the established finding that multiple cognitive measures are correlated in subjects as the general intelligence factor “g”, but in this case, the correlated attributes include a more general mode of positive life function that are tied to underlying brain function (in the form of strong functional connectivity between certain brain regions).

In the Nature News article that accompanies the study, Marcus Raichle and Deanna Barch, a neuroscientist and psychologist at Washington University and consortium members of the WU-Minn HCP, were interviewed:

[Raichle] is impressed that the activity and anatomy of the brains alone were enough to reveal this ‘positive-negative’ axis. “You can distinguish people with successful traits and successful lives versus those who are not so successful,” he says.

But Raichle says that it is impossible to determine from this study how different traits relate to one another and whether the weakened brain connections are the cause or effect of negative traits. And although the patterns are clear across the large group of HCP volunteers, it might be some time before these connectivity patterns could be used to predict risks and traits in a given individual. Deanna Barch, a psychologist at Washington University who co-authored the latest study, says that once these causal relationships are better understood, it might be possible to push brains toward the ‘good’ end of the axis.

As more subject-specific functional connectome data becomes available, more fine-grained studies will be possible to further understand how the interactions between brain networks can give rise to positive behavioral traits such as those measured by the HCP.  In the near future, look for cutting-edge developments in interpreting individual structural connectivity based on diffusion MRI experiments by HCP to give our understanding of how underlying brain structure contributes to differences between individuals with different behavioral patterns a leap forward.

Check out the original article:

A positive-negative mode of population covariation links brain connectivity, demographics and behavior.

Stephen M Smith, Thomas E Nichols, Diego Vidaurre, Anderson M Winkler, Timothy E J Behrens, Matthew F Glasser, Kamil Ugurbil, Deanna M Barch, David C Van Essen, Karla L Miller
Nature neuroscience, 2015-09-29 | PMID: 26414616

We investigated the relationship between individual subjects’ functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single ‘positive-negative’ axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.
Older Posts »