NIH Blueprint: The Human Connectome Project

News and Updates

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.

Press Releases,Project News | September 24, 2015

2015 HCP Course Materials Released

Course PracticalHCP Course Materials Image Data and Lecture Slides Now Available

We are pleased to announce that the materials for the 2015 HCP Course: Exploring the Human Connectome that was held June 8-12 in Honolulu, Hawaii are now available for all to learn from and use. This includes:

  • slides for all 21 lectures (PDF and PPT formats)
  • instructions for all 10 practicals (PDF)
  • virtual machine (VM) archive for download (275 GB) that replicates the software configuration/directory structure of the computers used at the course + VMware and VirtualBox VM install instructions
  • data archive (215 GB) and details on prerequisite software and installation advice for advanced users that choose to configure their own systems to run the practicals

Check it out at!

If you have questions about using the HCP Course materials, we encourage you to join the discussion on the HCP-Users list.

Wish you had been there live? Good news! We are making initial plans for the 2016 HCP Course to be held in summer 2016, stay tuned in the coming months for more details!

Project News | July 24, 2015

Connectome Workbench Bug fix update v1.1.1 Released

Announcing the release of Workbench v1.1.1, that fixes a few bugs that were found in the recently released Workbench v.1.1.

Updates in v1.1.1:

*New Feature: Copy loaded connectivity row data to a connectivity dense scalar file from group average rs-fMRI dense connectome data loaded through a link from ConnectomeDB (see further instructions below).

*New Command:  -metric-to-volume-mapping command added to map a metric (scalar) file to volume space.

*Scenes now save data by full path name so that files that are named the same, but have a different paths, can be correctly displayed.

*For border files in which names have been modified or created and are invoked by a scene, only border names with a status set to On are displayed when the scene is loaded.

*Averaging connectivity across voxels in a Volume label/ROI now works properly.


How to copy/save connectivity row data to a connectivity dense scalar file:

When you have the group average functional connectivity file displayed from ConnectomeDB (as in Scenes 5 & 6 from the WB v1.0 tutorial), go to the Connectivity tab and click the Copy button to the left of the listed Connectivity File link. This creates a *.dscalar file with the particular connectivity map you are displaying.

These files should show up in the Layer tab File dropdowns in the Overlay Toolbox. You can save the files you create with File menu > Save/Manage Files before quitting Workbench, for later use.

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Posted by Jenn Elam @ 1:47 pm
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