Toward discovery science of human brain function
Bharat B. Biswala, Maarten Mennesb, Xi-Nian Zuob, Suril Gohela, Clare Kellyb, Steve M. Smithc, Christian F. Beckmannc,
Jonathan S. Adelsteinb, Randy L. Bucknerd, Stan Colcombee, Anne-Marie Dogonowskif, Monique Ernstg, Damien Fairh,
Michelle Hampsoni, Matthew J. Hoptmanj, James S. Hydek, Vesa J. Kiviniemil, Rolf Kötterm, Shi-Jiang Lin, Ching-Po Lino,
Mark J. Lowep, Clare Mackayc, David J. Maddenq, Kristoffer H. Madsenf, Daniel S. Marguliesr, Helen S. Maybergs,
Katie McMahont, Christopher S. Monku, Stewart H. Mostofskyv, Bonnie J. Nagelw, James J. Pekarx, Scott J. Peltiery,
Steven E. Petersenz, Valentin Riedlaa, Serge A. R. B. Romboutsbb, Bart Rypmacc, Bradley L. Schlaggardd, Sein Schmidtee,
Rachael D. Seidlerff,u, Greg J. Sieglegg, Christian Sorghh, Gao-Jun Tengii, Juha Veijolajj, Arno Villringeree,kk,
Martin Walterll, Lihong Wangq, Xu-Chu Wengmm, Susan Whitfield-Gabrielinn, Peter Williamsonoo,
Christian Windischbergerpp, Yu-Feng Zangqq, Hong-Ying Zhangii, F. Xavier Castellanosb,j, and Michael P. Milhamb,1
aDepartment of Radiology, New Jersey Medical School, Newark, NJ 07103;bPhyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York
University Child Study Center, NYU Langone Medical Center, New York, NY 10016;cFMRIB Centre, Oxford University, Oxford OX3 9DU, UK;dHoward Hughes
Medical Institute, Harvard University, Cambridge, MA 02138;eSchool of Psychology, University of Wales, Bangor, UK;fDanish Research Centre for Magnetic
Resonance, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark;gMood and Anxiety Disorders Program, National Institute of Mental Health/
National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892;hBehavioral Neuroscience Department, Oregon Health &
Science University, Portland, OR 97239;iDepartment of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06511;jDivision of Clinical
Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962;kBiophysics Research Institute, Medical College of Wisconsin, Milwaukee,
WI 53226;lDepartment of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland;mDonders Institute for Brain, Cognition, and Behavior, Center for
Neuroscience, Radboud University Nijmegen Medical Center, 6500 HB Nijmegen, The Netherlands;nBiophysics Research Institute, Medical College of
Wisconsin, Milwaukee, WI 53226;oInstitute of Neuroscience, National Yang-Ming University, Taiwan;pImaging Institute, The Cleveland Clinic, Cleveland, OH
44195;qBrain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710;rDepartment of Cognitive Neurology, Max Planck Institute
for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany;sDepartment of Psychiatry and Department of Neurology, Emory University School of
Medicine, Atlanta, GA 30322;tCentre for Advanced Imaging, University of Queensland, Brisbane, Australia;uDepartment of Psychology, University of
Michigan, Ann Arbor, MI 48109;vLaboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205;wDepartment of
Psychiatry, Oregon Health & Science University, Portland, OR 97239;xF.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute,
Baltimore, MD 21205;yFunctional MRI Laboratory, University of Michigan, Ann Arbor, MI 48109;zMcDonnell Center for Higher Brain Functions, Washington
University School of Medicine, St. Louis, MO 63110;aaDepartments of Neurology and Neuroradiology, Klinikum Rechts der Isar, Technische Universität
München, 81675 Munich, Germany;bbInstitute of Psychology and Department of Radiology, Leiden University Medical Center, Leiden University, Leiden,
The Netherlands;ccCenter for Brain Health and School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX 75080;ddDepartment
of Neurology, Washington University School of Medicine, St. Louis, MO 63110;eeDepartment of Neurology, Charité Univesitaetsmedizin-Berlin, 10117
Berlin, Germany;ffSchool of Kinesiology, University of Michigan, Ann Arbor, MI 48109;ggDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA
15213;hhDepartment of Psychiatry, Klinikum Rechts der Isar, Technische Universität München, D-81675 Munich, Germany;iiJiangsu Key Laboratory of
Molecular and Functional Imaging, Department of Radiology, Zhong-Da Hospital, Southeast University, Nanjing 210009, China;jjDepartment of Psychiatry,
Institute of Clinical Medicine and Department of Public Health Science, Institute of Health Science, University of Oulu, Oulu 90014, Finland;kkBerlin
NeuroImaging Center, 10099 Berlin, Germany;llDepartment of Psychiatry, Otto-von-Guericke University of Magdeburg, Magdeburg 39106, Germany;
mmLaboratory for Higher Brain Function, Institute of Psychology, Chinese Academy of Sciences, Beijing 100864, China;nnDepartment of Brain and Cognitive
Sciences, Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Boston, MA 02139;ooDepartment of Psychiatry,
University of Western Ontario, London, ON N6A3H8, Canada;ppCenter for Medical Physics and Biomedical Engineering, Medical University of Vienna,
Vienna, Austria; andqqState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
Edited* by Marcus E. Raichle, Washington University, St. Louis, MO, and approved January 20, 2010 (received for review October 14, 2009)
Although it is being successfully implemented for exploration of
the genome, discovery science has eluded the functional neuro-
imaging community. The core challenge remains the development
of common paradigms for interrogating the myriad functional
systems in the brain without the constraints of a priori hypoth-
eses. Resting-state functional MRI (R-fMRI) constitutes a candidate
approach capable of addressing this challenge. Imaging the brain
during rest reveals large-amplitude spontaneous low-frequency
(<0.1 Hz) fluctuations in the fMRI signal that are temporally corre-
lated across functionally related areas. Referred to as functional
connectivity, these correlations yield detailed maps of complex
neural systems, collectively constituting an individual’s “functional
connectome.” Reproducibility across datasets and individuals sug-
gests the functional connectome has a common architecture, yet
each individual’s functional connectome exhibits unique features,
with stable, meaningful interindividual differences in connectivity
patterns and strengths. Comprehensive mapping of the functional
connectome, and its subsequent exploitation to discern genetic
influences and brain–behavior relationships, will require multicen-
ter collaborative datasets. Here we initiate this endeavor by gath-
ering R-fMRI data from 1,414 volunteers collected independently
at 35 international centers. We demonstrate a universal architec-
ture of positive and negative functional connections, as well as
consistent loci of inter-individual variability. Age and sex emerged
as significant determinants. These results demonstrate that inde-
pendent R-fMRI datasets can be aggregated and shared. High-
throughput R-fMRI can provide quantitative phenotypes for
molecular genetic studies and biomarkers of developmental and
pathological processes in the brain. To initiate discovery science of
brain function, the 1000 Functional Connectomes Project dataset is
freely accessible at www.nitrc.org/projects/fcon_1000/.
database|neuroimaging|open access|reproducibility|resting state
challenge to the functional neuroimaging community. As dem-
uch like the challenge of decoding the human genome, the
complexities of mapping human brain function pose a
Author contributions: B.B.B., R.L.B., J.S.H., R.K., A.V., Y.Z., F.X.C., and M.P.M. designed
research; B.B.B., M.M., XN.Z., S.G., C.K., S.M.S., C.F.B., J.S.A., R.L.B., S.C., A.-M.D., M.E.,
D.F., M.H., M.J.H., J.S.H., V.J.K., R.K., SJ.L., CP.L., M.J.L., C.E.M., D.M., K.H.M., D.S.M., H.S.
M., K.M.,C.S.M., S.M.,B.J.N., J.J.P.,S.J.P., S.E.P.,V.R., S.A.R.,B.R., B.L.S.,S.S., R.D.S.,G.S., C.S.,
GJ.T., J.M.V., A.V., M.W., L.W., XC.W., S.W.-G., P.W., C.W., Y.Z., HY.Z., F.X.C., and M.P.M.
R.K., SJ.L., CP.L., M.J.L., C.E.M., D.M., K.H.M., D.S.M., H.S.M., K.M., C.S.M., S.M., B.J.N., J.J.P.,
S.J.P., S.E.P., V.R., S.A.R., B.R., B.L.S., S.S., R.D.S., G.S., C.S., GJ.T., J.M.V., A.V., M.W., L.W.,
analytic tools; B.B.B., M.M., XN.Z., S.G., C.K., F.X.C., and M.P.M. analyzed data; and B.B.B.,
M.M., XN.Z., C.K., J.S.A., F.X.C., and M.P.M. wrote the paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
Freely available online through the PNAS open access option.
Data deposition: All data used in this work were released on December 11, 2009 via www.
1To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/cgi/content/full/
| March 9, 2010
| vol. 107
| no. 10www.pnas.org/cgi/doi/10.1073/pnas.0911855107
onstrated by the 1000 Genomes Project (1), the accumulation
and sharing of large-scale datasets for data mining is necessary
for the first phase of discovery science.
on hypothesis-driven task-based approaches, resting-state func-
tional MRI (R-fMRI) has recently emerged as a powerful tool for
discovery science. Imaging the brain during rest reveals large-
fMRI signal that are temporally correlated across functionally
used to interrogate a multitude of functional circuits simulta-
neously, without the requirement of selecting a priori hypotheses
(6). Building on the term “connectome,” coined to describe the
comprehensive map of structural connections in the human brain
functional connections in the human brain.
Buttressed by moderate to high test–retest reliability (8–10) and
replicability (11, 12), as well as widespread access, R-fMRI has
such an apparently unconstrained state (5, 8, 14). Recent R-fMRI
studies have identified putative biomarkers of neuropsychiatric
illness (12, 15–18), provided insight into the development of func-
tional networks in the maturing and aging brain (19–22), demon-
strated a shared intrinsic functional architecture (23) between
humans and nonhuman primates (24, 25), and delineated the
effects of sleep (26), anesthesia (27), and pharmacologic agents on
R-fMRI measures (28, 29). Given the many sources of variability
inherent in fMRI, the remaining challenge is to demonstrate the
feasibility and utility of adopting a high-throughput model for R-
to have the power to detect both single gene and combinatorial
genetic and environmental effects on complex phenotypes.
Accordingly, the 1000 Functional Connectomes Project was
formed to aggregate existing R-fMRI data from collaborating cen-
the ability to pool functional data across centers. As of December
11, 2009, the repository includes data from 1,414 healthy adult
to expandthis openresource as additionaldataare madeavailable.
Here we provide an initial demonstration of the feasibility of
pooling R-fMRI datasets across centers. Specifically, we (i)
establish the presence of a universal functional architecture in
the brain, consistently detectable across centers; (ii) investigate
the influence of center on R-fMRI measures; (iii) explore the
potential impact of demographic variables (e.g., age, sex) on R-
fMRI measures; and (iv) demonstrate the use of an intersubject
variance–based method for identifying putative boundaries
between functional networks.
depicts group-level maps for representative seed-based (column 1) and ICA-based (column 3) functional connectivity analyses (SI Results), as well as fALFF
(column 2). Group-level maps were derived from one-way ANOVA across 1,093 participants from 24 centers (factor: center; covariates: age and sex). All group-
level maps depicted were corrected for multiple comparisons at the cluster level using Gaussian random-field theory (Z > 2.3; P < 0.05, corrected). For each
measure, the second row shows robust between-center concordances (Kendall’s W), with the voxelwise coefficients of variation above the diagonal and the
voxelwise means below the diagonal. Kendall’s W concordance between any two centers was calculated across all voxels in the brain mask for the mean (or
coefficient of variation) connectivity map across all participants included in each center. Rows 3, 4, and 5 depict voxels exhibiting significant effects of center,
age, and sex, respectively, as detected by one-way ANOVA. “Male” refers to significantly greater connectivity (or amplitude, i.e., fALFF) in males; similarly,
“female” refers to significantly greater connectivity (or amplitude) in females. “Older” refers to significantly increasing connectivity (or amplitude) with
increasing age, whereas “younger” refers to significantly increasing connectivity (or amplitude) with decreasing age. “Pos” refers to positive functional
connectivity, and “neg” refers to negative functional connectivity. The PCC seed region is indicated by a white dot. (Top Left) Surface map legend: LL, left
lateral; RL, right lateral; LM, left medial; RM, right medial. All surface maps are rendered on the PALS-B12 atlas in CARET (http://brainvis.wustl.edu).
Independent center-, age-, and sex-related variations detected in R-fMRI measures of functional connectivity and amplitude fluctuation. The first row
Biswal et al. PNAS
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We applied three distinct analytic methods commonly used in the
R-fMRI literature: seed-based functional connectivity, inde-
pendent component analysis (ICA), and frequency-domain
analyses. Across the three approaches, we found evidence of (i) a
universal intrinsic functional architecture in the human brain, (ii)
center-related variation in R-fMRI measures, and (iii) consistent
effects of age and sex on R-fMRI measures, detectable across
centers despite the presence of center-related variability (Fig. 1).
Specifically, seed-based correlational analyses revealed highly
consistent patterns of functional connectivity across centers for
both the “default mode” (30) and “task-positive” networks (31),
supporting a universal functional architecture (Fig. S1). Similarly,
a data-driven, temporal concatenation ICA approach, combined
with dual regression (32–34), revealed consistent patterns of
functional networks (Fig. 1 and Figs. S2 and S3). In addition, for
each of the functional connectivity measures, within-center
coefficient of variation maps showed a high degree of con-
cordance across centers (Fig. S4). This suggests that common loci
of variation exist: centers demonstrated a high degree of agree-
ment on which connections are characterized by relative variance
or invariance. Despite the high degree of concordance between
centers, there were appreciable center-related variations in the
strength of functional connectivity throughout the brain (8). The
effect of center was especially prominent in regions exhibiting
greater interregional connection strength, because these have the
least within-center variability (See SI Results and Fig. S5 for fur-
ther discussion of center-related variability.) However, even when
effects of age and sex remained appreciable (Fig. 2 and Figs. S1
and S2). (See SI Results and Fig. S6 for an examination of the
impact of sample size on effects of age and sex.)
The detection of sex differences was particularly noteworthy,
because these differences are rarely appreciated in the R-fMRI
literature (35). Sexual dimorphism in human genomic expression
(36) is known to affect numerous physiological variables that can
influence the fMRI signal (37, 38). For example, males and
females differ in terms of hemoglobin concentrations and hema-
tocrit (39). However, global variables such as these do not explain
the regionally specific sex-related phenomenon noted in the
present work. Hormonal effects (e.g., estrogen), operating both
during brain development (40) and acutely (41), are known to
have regional specificity (42), making them potential contributors
to the differences observed. Given the discovery nature of the
present work and the lack of prior coordination among centers,
with caution until replicated in an independent sample.
Along with examining patterns of functional connectivity, we
measured the amplitude of low-frequency fluctuations at each
voxel using two common periodogram-based measures: ampli-
17, 43) and fractional ALFF (fALFF; total power <0.1 Hz/total
power in the measured spectrum) (44). Concordant with previous
work, the dominance of low-frequency fluctuations was con-
sistently noted within gray matter regions, but not white matter
(44). As with our analyses of functional connectivity, despite clear
evidence of center-related effects, we were again able to dem-
onstrate age- and sex-related differences in the magnitude of low-
frequency fluctuations in various regions, particularly medial wall
structures (Fig. 2 and Fig. S7).
Beyond data pooling for statistical analyses, we demonstrate
the potential to use high-throughput datasets to develop norma-
tive maps of functional systems in the brain, which is a pre-
requisite for clinical applications. Specifically, we exploit a key
property of functional connectivity maps, the presence of well-
differentiated borders between functionally distinct regions (45).
The voxelwise measures of coefficients of variation for each type
of functional connectivity map delineate putative functional
boundaries based on the presence of marked variability in func-
explained by age and sex (cluster-based Gaussian random-field corrected: Z > 2.3; P < 0.05). For each of three methods (seed-based, fALFF, and ICA), variance
in connectivity strength explained by age (Left) and sex (Right) is illustrated both anatomically and graphically. Age-related differences are represented as
scatterplots. Sex-related differences are represented as histograms depicting the distributions of resting-state functional connectivity (RSFC) values for males
and females separately. Vertical lines indicate peak values. Corresponding topographical brain areas are indicated with dots. “Male” refers to significantly
greater connectivity (or amplitude, i.e., fALFF) in males; similarly, “female” refers to significantly greater connectivity (or amplitude) in females. “Older”
refers to significantly increasing connectivity (or amplitude) with increasing age, whereas “younger” refers to significantly increasing connectivity (or
amplitude) with decreasing age.
Illustrative areas exhibiting age- and sex-related variation in R-fMRI properties. Significant group-level variance in functional connectivity maps was
| www.pnas.org/cgi/doi/10.1073/pnas.0911855107 Biswal et al.
tional connectivity across participants. The variation observed at
theseboundariesstands incontrast tothelow degreeofvariability
observed in regions exhibiting consistently positive or negative
connectivity (Fig. 3). In addition, examination of the coefficients
of variation for fALFF measures revealed sharp boundary zones
between white matter and gray matter. It also identified areas of
variability in the amplitude of spontaneous fluctuations that
coincided with anatomic areas of notable sulcal variability (e.g.,
cingulate and frontal opercular regions).
The present work represents a watershed event in functional
imaging: demonstration of the feasibility of sharing and pooling
functional data across multiple centers, alongside the establish-
the presence of a universal functional architecture, with remark-
ablestability in the functional connectome andits loci of variation
across participants and centers; (ii) evidence of systematic sex
differences in R-fMRI measures, as well as age-related gradients
even in middle adulthood; and (iii) a method for highlighting the
complex array of putative functional boundaries between net-
worksfrom which normative maps canbedeveloped. Futurework
should focus on using the functional connectome to catalog phe-
notypic diversity in brain–behavior relationships.
Functional connectivity is both related to and distinct from ana-
tomic connectivity. Specifically, a recent study reported that a
structural core appears to play “a central role in integrating infor-
mation across functionally segregated brain regions” (23). As such,
our finding of a universal functional architecture was not unex-
pected. But structure and function are not completely coupled, as
illustrated by the robust homotopic (i.e., contralateral) functional
connectivity for such regions as the primary visual cortex or the
amygdala, both of which lack direct callosal projections (24, 46).
Such findings imply that functional connectivity is subserved by
functional connectivity exhibits dynamic properties that are absent
in structural connectivity. For instance, functional connectivity is
modulated by cognitive (47) and emotional state (48), arousal, and
sleep (26), whereas structural connectivity is grossly unaffected by
such factors. In short, the presence of a demonstrable structural
does the demonstration of a functional connection imply the pres-
ence of a direct structural connection.
Task-based fMRI and R-fMRI approaches have comple-
mentary roles in the study of human brain function. Task-based
approaches require sufficient a priori knowledge to articulate
specific hypotheses, and they are invaluable in refining such
hypotheses. But when the knowledge base is insufficient, task-
based approaches may be compared to candidate gene studies,
which have had limited success when applied to complex genetic
disorders. In contrast, genome-wide association studies are
increasingly providing initial findings for complex traits (49) and
diseases that are subsequently validated through replication,
extension, and deep sequencing (50). Our demonstration that R-
fMRI data can be aggregated and pooled, and that variability
among individuals can be explained in terms of specific subject
variables (e.g., sex, age), suggests that this approach can provide
quantitative phenotypes to be integrated into molecular studies.
Our results must be considered in light of several limitations of
the present study. First, we used a convenience sample com-
prising previously collected data from an array of centers, with-
out prior coordination of acquisition parameters or scanning
conditions. Although the robustness of our results attests to the
consistency of intrinsic brain activity, it still represents a potential
underestimate of the true across-center consistency. Our dem-
ographic data warrant caution, because centers were heteroge-
neous with respect to male:female ratio, mean age, and age
range. Our findings should motivate more systematic exploration
of these variables, because future high-throughput imaging
studies will need to take such factors into account.
Despite thepromise of R-fMRI, some theoreticaland pragmatic
by well-demarcated boundaries for an individual (45). As such, variability in boundary areas is detectable across participants. Here we detect functional
boundaries via examination of voxelwise coefficients of variation (absolute value) for fALFF and selected seed-based [intraparietal sulcus (IPS), posterior
cingulate/precuneus (PCC)] and ICA-based (IC13) functional connectivity maps. For the purpose of visualization, coefficients of variation were rank-ordered,
whereby the relative degree of variation across participants at a given voxel, rather than the actual value, was plotted to better contrast brain regions.
Ranking coefficients of variation efficiently identified regions of greatest interindividual variability, thus delineating putative functional boundaries.
Variation across individuals reveals functional boundaries. Previous work has noted that functionally segregated regions are frequently characterized
Biswal et al.PNAS
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fluctuations of neuronal and hemodynamic activity, the impact of
means of acquiring, processing, and analyzing R-fMRI data. Nev-
ertheless, the potential of discovery science is vast, from the devel-
opment of objective measures of brain functional integrity to help
response and assessing the efficacy of treatment interventions.
Finally, whereas the presentwork examines functional connectivity
g., EEG, magnetoencephalography, diffusion-tensor imaging,
volumetrics) and genetics to achieve a more complete under-
standing of the human brain.
All data and analytic tools used in the present work will be made
available at www.nitrc.org/projects/fcon_1000/. We anticipate that
community necessary for successful implementation of discovery-
based science of human brain function. In addition, we hope that it
will further advance the ethos of data sharing and collaboration ini-
tiated by such efforts as fMRIDC (www.fmridc.org), FBIRN (www.
birncommunity.org), OASIS (www.oasis-brains.org), BrainScape
(www.brainscape.org), and BrainMap (www.brainmap.org).
(n = 1,414). The present analysis was restricted to 24 centers (n = 1,093; 21
published, 3 unpublished; mean age <60 years; only participants over age 18;
one scan per participant; duration: 2.2–20 min; n = 970 at 3 T, n = 123 at 1.5 T;
voxel size, 1.5–5mm within plane; slice thickness, 3–8 mm). Each contributor’s
respective ethics committee approved submission of deidentified data. The
institutional review boards of NYU Langone Medical Center and New Jersey
Medical School approved the receipt and dissemination of the data.
six previously identified seed regions (31), and model-free ICA, using temporal
concatenation to generate group-level components and dual regression to
used the FFT-based ALFF (2, 17, 43) and its normalized variant, fALFF (44).
Standard image preprocessing was performed (i.e., motion correction,
spatial filtering with FWHM = 6 mm, 12-dof affine transformation to MNI152
stereotactic space). For seed-based correlation approaches and dual regres-
sion following ICA analysis, nuisance signals (e.g., global signal, WM, CSF,
motion parameters) were regressed out. Temporal filtering was tailored for
each analytic approach (29, 31, 32, 44).
ICA components for dual regression analyses were determined by (i) low-
(each with 18 participants randomly selected from each of 17 centers with
minimum of 165 time points) and (ii) low-dimensional (20 components) meta-
participant, dual regression (32–34) was performed using the 20 components
identified by the meta-ICA (Fig. S3), yielding a connectivity map for each
a generalized linear model implementation of one-way ANOVA (factor:
center; covariates: age and sex). To identify functional boundaries, we cal-
culated voxelwise coefficients of variation across all 1,093 participants, and
ranked each voxel based on the absolute value of its coefficient of variation.
ACKNOWLEDGMENTS. We thank David Kennedy and www.nitrc.org for sup-
porting the 1000Functional Connectomes Project data release, Avi Snyder for
providing helpful insights and advice concerning project goals, and Cameron
Craddock for helpful advice on this study. Financial support for the 1000
Functional Connectomes project was provided by grants from the National
Institutes of Mental Health (R01MH083246 and R01MH081218 to F.X.C. and
M.P.M.), National Institute on Drug Abuse (R03DA024775, to C.K.;
R01DA016979, to F.X.C.), Autism Speaks, National Institute of Neurological
Institute (to J.S.A. and R.L.B.), as well as gifts to the NYU Child Study Center
from the Stavros Niarchos Foundation, Leon Levy Foundation, Joseph P.
Healy, Linda and Richard Schaps, and Jill and Bob Smith and an endowment
provided by Phyllis Green and Randolph Cōwen. NITRC is funded by the
National Institutes of Health’s Blueprint for Neurosciences Research (neuro-
scienceblueprint.nih.gov) (Contract N02-EB-6-4281, to TCG, Inc.).
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