NIH Blueprint: The Human Connectome Project

News and Updates

Press Releases,Project News,Recommended Reading,The Science of Connectome | July 20, 2016

Nature article: Cortical brain maps at the highest resolution to date

Screen Shot 2016-07-20 at 11.21.20 AM

Matthew Glasser, Ph.D. of the Van Essen lab at Washington University in St. Louis.

Major new work based on Human Connectome Project data and methods published in July 20 issue of Nature promises to be a boon to neuroanalysis research for years to come.

The study, A multi-modal parcellation of human cerebral cortex, led by Matthew Glasser and David Van Essen of Washington University, used information derived from structural and functional MRI data collected on 210 HCP subjects to create a new 180 region per hemisphere map of the cerebral cortex of the human brain. Further improvements to previous maps were achieved by using the multimodal surface matching algorithm pioneered by HCP investigators at Oxford U to precisely align the individual brains before analysis. Results were validated and the maps applied to individuals from an independent set of 210 HCP subjects.

Although the new map will be very important in raising the accuracy of work to delineate the connections between brain regions by the HCP and others, it will continue to improve as more, higher resolution data is added to the analyses.

As Glasser told the Washington University Record:

“We ended up with 180 areas in each hemisphere, but we don’t expect that to be the final number,” Glasser said. “In some cases, we identified a patch of cortex that probably could be subdivided, but we couldn’t confidently draw borders with our current data and techniques. In the future, researchers with better methods will subdivide that area. We focused on borders we are confident will stand the test of time.”

and added in Nature:

“We’re thinking of this as version 1.0,” says Glasser. “That doesn’t mean it’s the final version, but it’s a far better map than the ones we’ve had before.”

The parcellation and Connectome Workbench scenes for each of the main article and supplemental figures are being shared in the new Brain Analysis Library of Spatial maps and Atlases (BALSA) database being developed by the Van Essen lab at Washington University.

The parcellation for use as a reference is most easily accessed in the Glasser_et_al_2016_HCP_MMP1.0_5_StudyDataset.scene study dataset.

Nature produced a video highlighting the work in the context of previous brain mapping efforts:

In addition to the article itself, Nature is distributing a wealth of supplemental information, as David Van Essen told the Washington University Record:

“We were able to persuade Nature to put online almost 200 extra pages of detailed information on each of the 180 regions as well as all of the algorithms we used to align the brains and create the map,” Van Essen said. “We think it will serve the scientific community best if they can dive down and get these maps onto their computer screens and explore as they see fit.”

The seminal work has also garnered much press:

B.T. Thomas Yeo & Simon B. Eickhoff authored a Nature News and Views article.

Nature News: Human Brain Mapped in Unprecedented Detail

Washington University Record: Map provides detailed picture of how the brain is organized

NIH News: Connectome map more than doubles human cortex’s known regions

New York Times: Updated Brain Map Identifies Nearly 100 New Regions

Wall Street Journal: Brain Mappers Create a Detailed Atlas of the Human Cortex

The Scientist: Mapping the Human Connectome

Popular Science: This New, Ultra-Detailed Map Of The Brain Could Change Medicine

Wired: A New Map of the Brain Redraws the Boundaries of Neuroscience


Press Releases,Project News | May 19, 2016

Check out Connectome Workbench v1.2.0!

WBv1.2.0releaseThe WU-Minn HCP Consortium is pleased to announce that version 1.2.0 of Connectome Workbench (WB) brain visualization and analysis software is now available at  for 64-bit Mac OSX, Windows, and Linux. The Workbench distribution includes wb_view, a GUI-based visualization platform, and wb_command, a command-line program for performing a variety of algorithmic tasks using volume, surface, and grayordinate data.

New features in WB v1.2.0 include:


  • Annotations are a powerful new feature for placing text and symbols within wb_view tabs and windows. They enable generation of publication-ready figures without the need for further annotation in a separate application (e.g., Photoshop).  Annotation features are extensively documented in “Guide to WB Annotations”.
  • New options for locking the aspect ratio for tabs and windows when converting between Tile Tabs and single tile views and when adjusting the wb_view viewport (aka viewing area).
  • Improved control over gaps and margins between tabs.
  • More flexible control over color bars and associated labels.
  • Refinements to scene files, including the option to override the user’s default background/foreground color settings.
  • Separate panning and zooming enabled between left and right hemisphere flatmaps.

Refinements to wb_command:

    -surface-average now takes per-surface weights, uses much less memory

    -spec-file-relocate changes the location of a spec file relative to its data files

    -scene-file-relocate changes the location of a scene file relative to its data files

    -label-to-volume-mapping – new command, uses ribbon mapping method

…plus many refinements to existing commands and also various bug fixes.

You will also notice in the Scenes window, some reference to BALSA Scene and Study IDs. These are WB features that will become useful in the soon to be released Brain Analysis Library of Spatial Maps and Atlases (BALSA) database (stay tuned!).

WB v1.2.0 is compatible with the WB v1.0 tutorial and the processed 900 Subjects Group Average Data available to download at and on the ConnectomeDB HCP project page. Access to both datasets require ConnectomeDB login and signature of the HCP Open Access Data Use Terms.

To download the WB v1.2.0 source code from GitHub: follow the link, click releases (near the top of the page), then under v1.2.0, click the “Source code (zip)” or “Source code (tar.gz)” button.

We encourage you to check out Connectome Workbench v1.2.0 for visualizing and performing analysis on imaging data from the HCP and elsewhere and for making scenes and figures. Discussion of Connectome Workbench usage, bugs, and features can be posted to the hcp-users discussion list. Sign up for hcp-users at

Press Releases,Project News,Upcoming events | May 11, 2016

Attend the HCP Course 2016 in Boston Aug 28-Sept 1!


We are pleased to announce the 2016 HCP Course: “Exploring the Human Connectome”, to be held August 28-September 1 (Sunday-Thursday) at the Joseph B. Martin Conference Center at Harvard Medical School, in Boston, Massachusetts, USA.

This 5-day intensive course will provide training in the acquisition, analysis and visualization of whole-brain imaging and behavioral data from the Human Connectome Project (HCP) using methods and informatics tools developed by the WU-Minn HCP consortium plus data made freely available to the neuroscience community.

The course is designed for investigators who are interested in:

  • using data being collected and distributed by HCP
  • acquiring and analyzing HCP-style imaging and behavioral data at your own institution
  • processing your own non-HCP imaging data using HCP pipelines and methods
  • learning to use Connectome Workbench tools and the CIFTI connectivity data format
  • learning HCP multi-modal neuroimaging analysis methods, including those that combine MEG and MRI data
  • positioning yourself to capitalize on HCP-style data from forthcoming large-scale projects (e.g., Lifespan HCP and Connectomes Related to Human Disease)

Participants will learn how to acquire, analyze, visualize, and interpret data from four major MR modalities (structural MR, resting-state fMRI, diffusion imaging, task-evoked fMRI) plus magnetoencephalography (MEG) and extensive behavioral data.  Lectures and labs will provide grounding in neurobiological as well as methodological issues involved in interpreting multimodal data, and will span the range from single-voxel/vertex to brain network analysis approaches.

The course is open to graduate students, postdocs, faculty, and industry participants.  The course is aimed at both new and existing users of HCP data, methods, and tools, and will cover both basic and advanced topics. Prior experience in human neuroimaging or in computational analysis of brain networks is desirable, preferably including familiarity with FSL and Freesurfer software.

For more info and to register visit the HCP Course website.

We hope to see you in Bah-ston!

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