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Scientific priorities for the BRAIN Initiative
Aravi Samuel, Herbert Levine & Krastan B Blagoev
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© 2013 Nature America, Inc. All rights reserved.
We present a summary of the scientific deliberations and major conclusions that arose from a workshop on
the BRAIN Initiative.
In his State of the Union address on
21 January 2013 and in a press conference on
2 April 2013, US President Barack Obama
called for a national program to develop
technologies to accelerate our understanding of how the brain works. One aspect of
this new initiative would be the mapping
of neural firing patterns in behaving animals, which would show how brain activity
underlies mental activities and behaviors.
This plan—initially called the Brain Activity
Map—was developed in a series of meetings
and workshops cosponsored by the Kavli
Foundation, Gatsby Foundation and Allen
Brain Institute, culminating in several published reports1–3. Once the White House
signaled interest, the effort, now called the
BRAIN (Brain Research through Advances
in Innovative Neurotechnologies) Initiative,
rapidly became a priority for federally funded scientists4.
Of critical importance for the community
is how the major US federal agencies that
are charged with implementing the BRAIN
Initiative—the National Science Foundation
(NSF), National Institutes of Health (NIH)
and Defense Advanced Research Projects
Agency (DARPA)—will translate the loosely
defined mandate to understand the human
brain and its disorders into specific scientific
priorities and projects. As we discuss below,
major questions remain regarding what the
Aravi Samuel is in the Department of Physics and
Center for Brain Science, Harvard University,
Cambridge, Massachusetts, USA, and
Herbert Levine is in the Department of
Bioengineering, Rice University, Houston, Texas,
USA. Krastan B. Blagoev is at the US National Science
Foundation, Arlington, Virginia; in the Department
of Radiology, Massachusetts General Hospital and
Harvard Medical School, Boston, Massachusetts;
and at the Martinos Center for Biomedical Imaging,
Charlestown, Massachusetts, USA.
e-mail: [email protected]
best strategies are for performing largescale neural recording as well as the synergistic research activities that will enable
the activity patterns to be analyzed and
properly understood. To help address these
issues, the NSF and the Kavli Foundation
cosponsored a 2-day workshop in early
May in Arlington, Virginia, USA, drawing
more than 100 leading scientists (http://
physicsoflivingsystems.org/brainstructureandfunction/). The goal was for the participants to discuss their own views about
what the major obstacles to understanding
the brain are and to present their hopes
for the BRAIN Initiative. The meeting was
live streamed to the general public to promote transparent debate. Members of the
Working Group of the Advisory Committee
to the NIH for the BRAIN Initiative (http://
www.nih.gov/science/brain/acd-roster.
pdf) as well as many government representatives attended the meeting. A full report
of the meeting will be made available at the
workshop website at a later date; here we—
representing one of the meeting sponsors
and two of the organizers—provide a short
summary of the scientific deliberations and
the major conclusions.
As mentioned above, one major focus of
the BRAIN Initiative is to develop technology for recording from many neurons and
manipulating their activities in behaving
animals. Advances in the last decade have
made it possible to measure neural activities
in large ensembles of neurons, as many as
hundreds of neurons at once in the brain of
a mouse5 or across the retina of a primate6.
The view that emerged from prior meetings
that crafted the BRAIN Initiative is that we
cannot hope to understand the brain without being able to record from a substantial
fraction of its neurons; the exact size of that
fraction is a subject of vigorous debate.
Current technologies are starting to make it
possible to record from most of the neurons
in the small brain of the zebrafish larva7, but
substantial technical breakthroughs will be
required to record all of the neural activity
in an animal as large as a mouse.
There was a consensus that brain activity
maps are indeed crucially important, and
that we therefore need to invest heavily in
technology that would enable us to record
from many more neurons than we can
currently. Electrical probes must be made
smaller and more durable, and we need
to develop less invasive devices that allow
longer recording with higher spatial and
temporal resolution with minimal disruption. We must also invest heavily in optical neurophysiology to generate more and
better genetically encoded probes, both for
stimulating neurons and for recording calcium and voltage dynamics. In addition, better microscopes will be needed if they are to
be used most effectively: for recording from
as many neurons as possible, for as long as
possible and with high temporal resolution
to resolve circuit computations.
However, if the quest is to understand the
brain, then just creating large-scale maps
of brain activity is not going to be enough.
First, to deal with the enormous amount of
data that come from massive recordings,
sophisticated methods will be needed to
store, access, distribute and analyze that
data. A strong cyber infrastructure will
be essential to the success of laboratories,
departments and research universities: this
can be built on experience from other ‘big
data’ fields of science such as genomics or
high-energy physics. Bringing experts from
these fields into neuroscience will be integral to the BRAIN Initiative.
More critically, brain activity maps need
to be augmented by other data and by an
nature methods | VOL.10 NO.8 | AUGUST 2013 | 713
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© 2013 Nature America, Inc. All rights reserved.
commentary
analysis framework informed by theory.
Important information is contained in the
structure and wiring of the neural circuits
that are being recorded from. Having in
hand the connectivity of the wiring diagram will allow researchers not merely to
find correlations in the activity in distant
neurons but also to understand the mechanistic basis of such correlations as arising
in the flow of information through neural
wiring and across synapses. Reconstruction
of connectivity patterns across whole brains
(‘connectomes’ that are detailed enough to
specify every synapse between every pair of
cells) would provide the structural framework for helping interpret neural dynamics. Efforts in dealing with vast data sets of
structural information will also benefit from
an investment in cyber infrastructure.
For many years, it has been possible to
record from all the neurons in the stomatogastric ganglion of the lobster8 or from
all the neurons in a region of the retina9.
And yet it cannot be said that these systems are solved and that the principles of
information processing are fully understood. Thus, another emerging consensus
was that advanced theoretical concepts
regarding how activity encodes information and computation would need to be
developed hand in hand with the proposed
advances in measurement technology.
Emphasis should be placed on connecting
general ideas (such as the brain performing
Bayesian inference) with precise predictions as to how these could be detected in
the soon-to-be-available data.
A quantitative understanding about what
the brain does requires an understanding
of how to quantify animal behavior. Recent
years have seen rapid advances in behavior
studies of small animals such as worms,
flies, fish and mice. Machine learning
approaches have been successful in reducing multidimensional behavioral dynamics to specific behavioral rules that might
be instantiated in brain circuits. For want
of a better word, full ‘behavior-omes’ that
describe time-varying behavior of animals
in defined environments would provide
large data sets across which to analyze connectomes and activity maps, thereby linking behavior to the structure and function
of underlying circuits.
In recent years, the technology that is
needed to understand the brain has been
rapidly advancing. However, the technologies for mapping brain activity, behavior
and structure must be brought together to
answer the most fundamental questions.
Most of these extraordinary tools will not
be available to most researchers because
they are expensive or require too much
expertise for small individual labs to readily learn and use. Thus, beyond making
lists of important scientific questions and
methods (that are easily agreed upon to be
important), the neuroscience community
needs to engage in an important discussion
as to how to reconfigure its sociology for
the coming era. This would include how to
share resources, how to reward collaboration and how to promote multidisciplinary
research and find proper slots for the
researchers who will bring needed technical know-how to these new activities but
who will not by themselves carry out traditional neuroscientific studies. An important recent development in the sociology
of neuroscience has been marked by the
establishment of private laboratories, such
as the Allen Brain Institute and Howard
714 | VOL.10 NO.8 | AUGUST 2013 | nature methods
Hughes Medical Institute’s Janelia Farm
Research Campus, that have been able to
contribute powerful and widely used tools
and data sets.
In summary, the workshop called for a
broad interpretation of the major challenges to neuroscience. Recording from as
many neurons as possible in whole brains
should be viewed as one necessary part of
a multipronged approach that combines
quantitative behavioral analysis of whole
animals, synapse-level studies and functional analysis with single-cell resolution
across whole brains. Each of these parts has
technological needs, data handling requirements and informative theoretical underpinnings. The different traditions of the
NSF, NIH and DARPA may be extremely
valuable in promoting synergistic progress
in these disparate areas. To quote President
Obama at the end of his White House
address, “Let’s get to work.”
ACKNOWLEDGMENTS
We thank J. Lichtman, J. Sanes and E. Marder for
their careful reading of this report.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
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