The National Institutes of Health today awarded grants totaling
$40 million to map the human brain’s connections in high
resolution. Better understanding of such connectivity promises
improved diagnosis and treatment of brain disorders.
The grants are the first awarded under the Human Connectome
Project. They will support two collaborating research consortia.
The first will be led by researchers at Washington University, St.
Louis, and the University of Minnesota, Twin Cities. The other will
be led by investigators at Massachusetts General Hospital
(MGH)/Harvard University, Boston, and the University of California
Los Angeles (UCLA).
“We’re planning a concerted attack on one of the great
scientific challenges of the 21st. Century,” explained Washington
University’s Dr. David Van Essen, Ph.D., who co-leads one of the
groups with Minnesota’s Kamil Ugurbil, Ph.D. “The Human Connectome
Project will have transformative impact, paving the way toward a
detailed understanding of how our brain circuitry changes as we age
and how it differs in psychiatric and neurologic illness.”
The Connectome projects are being funded by 16 components of NIH
under its Blueprint for Neuroscience Research.
“On a scale never before attempted, this highly coordinated
effort will use state-of-the-art imaging instruments, analysis
tools and informatics technologies – and all of the resulting
data will be freely shared with the research community,” said
Michael Huerta, Ph.D., of the National Institute of Mental Health,
who directs the NIH Connectome initiative. “Individual variability
in brain connections underlies the diversity of our thinking,
perception and motor skills, so understanding these networks
promises advances in brain health.”
The Washington U./Minnesota team will map the connectomes in
each of 1,200 healthy adults – twin pairs and their siblings
from 300 families. The maps will show the anatomical and functional
connections between parts of the brain for each individual, and
will be related to behavioral test data. Comparing the connectomes
and genetic data of genetically identical twins with fraternal
twins will reveal the relative contributions of genes and
environment in shaping brain circuitry and pinpoint relevant
genetic variation. The maps will also shed light on how brain
networks are organized.
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In tooling up for the screening, the researchers will optimize
magnetic resonance imaging (MRI) scanners to capture the brain’s
anatomical wiring – and its activity, both when participants
are at rest and when challenged by tasks. All participants will
undergo such structural and functional scans at Washington
University. For these, researchers will use a customized MRI
scanner with a magnetic field of 3 Tesla. This Connectome Scanner
will incorporate new imaging approaches developed by consortium
scientists at Minnesota and Advanced MRI Technologies and will
provide ten-fold faster imaging times and better spatial
resolution.
Additionally, a subset of twin pairs will also be scanned using
more powerful 7 and 10.5 Tesla MRI units at the University of
Minnesota, which has pioneered the use of such advanced, ultra high
magnetic field imaging. For another subset of twins, the scans will
be complemented by movies of millisecond brain electrical activity
obtained at St. Louis University, using magnetoencephalography
(MEG) and electroencephalography (EEG).
After processing with sophisticated analysis tools using a
supercomputer, the data will become web accessible via a customized
Connectome Database Neuroinformatics Platform. All-told, the $30
million five-year project will involve 33 collaborators from nine
research centers, including Oxford University, U.K.; Indiana
University, Bloomington; University of California, Berkeley;
Warwick University, U.K.; University d’Annunzio, Italy; and the
Ernst Strungmann Institute, Germany.
Also collaborating with this larger project, the
MGH/Harvard-UCLA Connectome consortium will focus on optimizing MRI
technology for imaging the brain’s structural connections using
diffusion MRI with unprecedented resolution. This way of using a
MRI scanner, employed in both projects, maps the brain’s fibrous
long distance connections by tracking the motion of water.
Different types of tissues are detectable by telltale water
diffusion patterns characteristic of different types of cells. So
the long extensions of neurons, called white matter, can been seen
in sharp relief.
“The MRI scanner system we are assembling will be 4 to 8 times
as powerful as conventional systems, enabling imaging of human
neuroanatomy with much greater sensitivity than is currently
possible,” explained Bruce Rosen, M.D., Ph.D., who is co-directing
the project with MGH/Harvard colleague Van Wedeen, M.D., and Arthur
Toga, Ph.D., of UCLA.
The planned Connectome Scanner, to be built by Siemens Medical
Systems for this project, is the first of a new class of MRI
instruments. It will boost resolving power while also shortening
the scan times required to image each subject, Rosen said.
The MGH/Harvard team has pioneered the use of a diffusion MRI
technique called Diffusion Spectrum Imaging (DSI) to create
stunning maps of neural fibers crisscrossing the brain. DSI offers
a higher resolution, more multidimensional view than an older
technique called Diffusion Tensor Imaging. This makes it possible,
for example, to see the different orientations of multiple neural
fibers that cross at a single location.
“Today we know less about the connectivity of the human brain
than about a dozen other species,” said Wedeen. “Learning more
about variation in our own brain’s connections will lay the
groundwork for using brain imaging measures of connectivity as an
aid in diagnosis.”
“Creating these maps requires sophisticated statistical and
visual informatics approaches,” added UCLA’s Toga. “Understanding
the similarities and differences in these maps among
sub-populations will improve our understanding of human brain in
health and disease.”
Supported by an $8.5 million grant over three years, the project
will scan healthy adults, including some participants from the
other consortia’s project. Data and research know-how will also be
shared across the two projects.