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Human Connectome Project Awards $40 Million

The National Institutes of Health (NIH) awarded $40 million in grants to support two collaborating research consortia as part of the Human Connectome Project (HCP). The grants are the first awarded under the HCP, which was launched in 2009 by the NIH Blueprint for Neuroscience Research in an effort to utilize the most advanced brain imaging technologies to map the circuitry of the healthy adult human brain. HCP funding comes from 16 components of NIH under the Blueprint for Neuroscience Research. Researchers at Washington University, St. Louis (WUSTL), and the University of Minnesota (UMN) will lead one of the consortia, while the other will be led by investigators at Massachusetts General Hospital (MGH)/Harvard University, Boston and the University of California Los Angeles (UCLA).

The WUSTL/UMN team will work with healthy adult twin pairs and their siblings from 300 families to map each of their connectomes. Resulting maps will demonstrate the anatomical and functional connections between parts of the brain for each individual, and will be related to behavioral test data. Researchers will compare connectomes and genetic data of genetically identical twins with fraternal twins to determine relative genetic and environmental contributions in brain circuitry development and to identify relevant genetic variation. All participants will undergo structural and functional MRI scans while at rest and when challenged by activity. A subset will also be scanned using more powerful 7 and 10.5 Tesla MRI units at the UMN, and yet another subset will include video capture of millisecond brain electrical activity. Magnetoencephalography and electroencephalography will take place at WUSTL. After analysis, the resulting data will become web accessible via a customized Connectome Database Neuroinformatics Platform. Nine research centers, which include national and international institutions, will collaborate on this five-year project.

The MGH/Harvard-UCLA Connectome consortium will be focused on optimizing MRI technology using …

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