What is a representative brain? Neuroscience meets population science.
ABSTRACT The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain-behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective-population neuroscience-that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas.
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ABSTRACT: The vast majority of mental illnesses can be conceptualized as developmental disorders of neural interactions within the connectome, or developmental miswiring. The recent maturation of pediatric in vivo brain imaging is bringing the identification of clinically meaningful brain-based biomarkers of developmental disorders within reach. Even more auspicious is the ability to study the evolving connectome throughout life, beginning in utero, which promises to move the field from topological phenomenology to etiological nosology. Here, we scope advances in pediatric imaging of the brain connectome as the field faces the challenge of unraveling developmental miswiring. We highlight promises while also providing a pragmatic review of the many obstacles ahead that must be overcome to significantly impact public health.Neuron 09/2014; 83(6):1335-1353. · 15.77 Impact Factor
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ABSTRACT: Growth in executive functioning (EF) skills play a role children's academic success, and the transition to elementary school is an important time for the development of these abilities. Despite this, evidence concerning the development of the ERP components linked to EF, including the error-related negativity (ERN) and the error positivity (Pe), over this period is inconclusive. Data were recorded in a school setting from 3- to 7-year-old children (N=96, mean age=5 years 11 months) as they performed a Go/No-Go task. Results revealed the presence of the ERN and Pe on error relative to correct trials at all age levels. Older children showed increased response inhibition as evidenced by faster, more accurate responses. Although developmental changes in the ERN were not identified, the Pe increased with age. In addition, girls made fewer mistakes and showed elevated Pe amplitudes relative to boys. Based on a representative school-based sample, findings indicate that the ERN is present in children as young as 3, and that development can be seen in the Pe between ages 3 and 7. Results varied as a function of gender, providing insight into the range of factors associated with developmental changes in the complex relations between behavioral and electrophysiological measures of error processing.Developmental cognitive neuroscience. 02/2014; 9C:93-105.
What is a representative brain? Neuroscience
meets population science
Emily B. Falka,b,c,1, Luke W. Hyded,e,f,1, Colter Mitchelle,g,1,2, Jessica Faule,3, Richard Gonzalezb,d,h,3,
Mary M. Heitzegi,3, Daniel P. Keatingd,e,i,j,3, Kenneth M. Langae,k,l,3, Meghan E. Martzd,3, Julie Maslowskym,3,
Frederick J. Morrisond,3, Douglas C. Nolln,3, Megan E. Patricke,3, Fabian T. Pfeffere,g,3, Patricia A. Reuter-Lorenzd,e,o,3,
Moriah E. Thomasonp,q,r,3, Pamela Davis-Keanb,d,e,f,4, Christopher S. Monkd,e,f,i,o,4, and John Schulenbergd,e,f,4
Departments ofaCommunication Studies,dPsychology,hStatistics,iPsychiatry,jPediatrics and Communicable Diseases,kInternal Medicine,
nBiomedical Engineering;oNeuroscience Graduate Program;bResearch Center for Group Dynamics,eSurvey Research Center, andgPopulation
Studies Center of the Institute for Social Research; andfCenter for Human Growth and Development, University of Michigan, Ann Arbor,
MI 48109;cAnnenberg School for Communication, University of Pennsylvania, Philadephia, PA, 19104;lVeterans Affairs Center for Clinical
Management Research, Ann Arbor, MI 48105,mRobert Wood Johnson Foundation Health and Society Scholars Program, Population Health
Sciences, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI 53706;pSchool of Medicine Pediatrics and
qMerrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, MI 48202; andrPerinatology Research
Branch, National Institutes of Health, Detroit, MI 48202
Edited by Mary C. Waters, Harvard University, Cambridge, MA, and approved September 11, 2013 (received for review May 31, 2013)
The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and
function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the
relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and
promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience.
Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding
brain–behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have
tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography,
sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain,
but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we
describe and promote a perspective—population neuroscience—that leverages interdisciplinary expertise to (i) emphasize the importance
of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address
such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural
mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population
neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this
approach across areas.
neuroimaging|life course|statistics|survey methodology|physics
Why Population Neuroscience?
How do biology, social situations, and the
broader environmental context interact to
guide behavior, health, and development? This
question is fundamental to most, if not all,
social and behavioral sciences. We argue that
to effectively address the many topics that stem
from this larger question across disciplines, it
is necessary to (i) bring a “population per-
spective” to neuroscience and (ii) leverage
neuroscience tools within population scien-
ces, which are subdisciplines of many fields,
areas, and departments focused on docu-
menting and understanding the dynamics
of human populations, including outcomes
such as health, well-being, behavior, etc.
Although recent advances in neuroscience
research, and neuroimaging in particular,
speak to how social, cognitive, and emotional
processes unfold (1–5), the extent to which
existing knowledge in human neuroscience
applies to broader, theoretically relevant pop-
ulations, and the ways that macrolevel
structures (e.g., social structure, neighborhood
safety, school quality, media exposure) in-
fluence neural processes is often unknown (6).
Thus, in parallel with a broader social science
focus on the limitations of nonrepresentative
samples (7, 8), we are now at a critical juncture
for social and biological science. What would
a “representative group of brains” tell us about
the generalizability of current samples and
mechanisms? How do individual differences in
brain structure and function affect cognitive,
affective, and behavioral outcomes and how do
social situations and broader environmental
contexts interact with these processes? Current
methods in much of neuroscience research and
the absence of neural measures in most pop-
ulation-based research limit our ability to
answer these questions (9).
At the same time, most social scientists are
interested in thoughts and behaviors (e.g., de-
cision making, empathy, attitudes), which
must have some relationship to the brain.
As such, neural measures, especially neuro-
imaging, have become widely used in several
specific social science disciplines (e.g., psy-
chology, decision science) (1–5, 10–12).
Author contributions: P.D.-K., C.S.M. and J.S. formed and led group;
E.B.F., L.W.H., and C.M. wrote paper; and E.B.F., L.W.H., C.M., J.F.,
R.G., M.M.H., D.P.K., K.M.L., M.E.M., J.M., F.J.M., D.C.N., M.E.P., F.T.P.,
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1E.B.F., L.W.H., and C.M. contributed equally to this work.
2To whom correspondence should be addressed. E-mail: cmsm@
3J.F., R.G., M.M.H., D.P.K., K.M.L., M.E.M., J.M., F.J.M., D.C.N.,
M.E.P., F.T.P., P.A.R.-L., and M.E.T. contributed equally to this work.
4P.D.-K., C.S.M., and J.S. contributed equally to this work.
www.pnas.org/cgi/doi/10.1073/pnas.1310134110 PNAS Early Edition
| 1 of 8
However, this trend toward brain science has
not been as true for social sciences that deal in
large and representative samples (e.g., social
demography) or long-term development (e.g.,
the life course), leading to a view of the brain
as a black box in those disciplines. A more
recent focus within population sciences on
how the broader environment “gets under the
skin” suggests that this may be a key moment
to look to the brain. Health psychologists have
demonstrated that the broader environment
becomes biologically embedded in the brain
over the course of development (5, 13–17), but
how does this yield observed variations within
populations? Therefore, we argue that the crit-
ical juncture described for neuroscience re-
search also poses an opportunity for population
science research more broadly. Taken together,
how can neuroscience research usefully inform
broader understanding in the population sci-
ences and how can these sciences be brought to
bear on neuroscience research?
In the present article, we point to building
momentum of a new subfield—population
neuroscience (6, 18)—and the opportunities it
affords. A population neuroscience perspective
emphasizes an understanding of human be-
havior across multiple levels of influence (e.g.,
from culture to social structure, to experience,
to behavior, to genes, to neural connectivity
and function guided by a multilevel ecological
model (12, 19–21) (Fig. 1). We encourage
readers to read Paus’s initial treatment of
population neuroscience cited above. Although
our thesis focuses on the interchange between
population sciences and neuroscience, we
believe that this thesis fits within the larger
and emerging field Paus has described as
population neuroscience and thus we use his
term to characterize our goals for this emerg-
Below, we highlight work on predictors,
outcomes, moderators, and mediators of the
brain–behavior relationship in studies that
inform our understanding of broader popu-
lations (22). We place specific emphasis on
integrating perspectives on sampling meth-
odology from demography and survey re-
search (23, 24) into a population neuroscience
approach (6). We provide more concrete
examples to illustrate the necessity of a pop-
ulation neuroscience approach for certain
types of research questions and the benefits it
can afford to both neuroscientists and pop-
ulation scientists. We then outline specific
goals to make population neuroscience a re-
ality and discuss the need for theoretical and
applied advances from this approach.
Current Practices in Neuroimaging
Research: How Universal Is What We
Researchers in both social and biological sci-
ences have pointed to the negative con-
sequences of extrapolating from small,
nonrepresentative samples based on the sys-
tematic biases these samples can introduce (9).
For example, for years, research suggested that
IQ was highly heritable, but more recent re-
search using more representative samples
found that genetic heritability was decidedly
lower for the whole population (25). Previous
researchers had used samples primarily con-
sisting of high socioeconomic status (SES)
participants. SES, however, was shown to
moderate the genetic heritability such that for
high SES genetic heritability was above 70%,
but for low SES participants genetic heritability
was closer to 10% (7, 25). Similarly there are
many examples across other domains of re-
search where early nonrepresentative conve-
nience samples and/or small sample sizes led to
incorrect or inconsistent estimates of out-
comes, including errors such as miscon-
ceptions of age patterns on morbidity and
cognition (26, 27), the assumption that basic
tenants in social psychology (e.g., the funda-
mental attribution error) generalize to all
people (7, 28, 29), and relationships between
socioeconomic position, neuropathology, and
dementia (30). Beyond these consequences,
social scientists have long noted the constraints
and problems imposed by reliance on student
subject pools (8, 29) and Western, Educated,
Industrialized, Rich, and Democratic (WEIRD)
populations more broadly (7). These samples
differ in many concrete ways from broader
populations of interest, which has led to a
greater emphasis by the National Institutes of
Health on including women and minorities in
studies (31, 32). Additionally, even within
medical clinical trials there has been a call for
greater use of practical clinical trials to improve
the external validity of the results (33, 34).
Learning from these examples, the need for
population neuroscience for certain types of
Fig. 1. This framework highlights the interplay between multiple levels of analysis from biology to macro level
environments across development. Adapted from Harrison et al. (125) and Antonucci et al. (126).
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research questions is clear. Population neuro-
science leverages well-known sampling tech-
niques that are routinely used in other fields
such as demography, epidemiology, and survey
research (11, 12) to strengthen the link be-
tween sample and target population and en-
hance the generalizability of results. The extent
to which neuroscience findings can inform and
be informed by disciplines that focus on macro
level structures (e.g., demography, sociology)
rests on our ability to maximize generalizabil-
ity to relevant populations. It should be noted
that what constitutes a relevant population is
often subjective and project specific. Re-
searchers may target different populations
(e.g., by disease status/risk, age, geographic
region, or SES level) based on their research
goals and substantive theoretical questions,
but a key aspect of moving research forward
is identifying and describing the relevant
population the research is intended to char-
acterize or address. Not explicating the popu-
lation could lead to problems of comparability,
replication, and inference.
This is not to say that all studies must use
samples that represent entire nations or that
treat variability across major cultural groups.
In fact, collecting larger representative data
without clear hypotheses or a target population
to generalize to may not yield helpful con-
clusions either. Rather, the goal is for neuro-
science studies to consider sampling as one
critical component of the design, just as
researchers would consider functional MRI
(fMRI) pulse sequences, task, stimuli, and/or
analysis strategy. In fact, many clinical studies
and high-risk population studies are excellent
examples where neuroimaging is used on
a carefully selected and characterized sample
that generalizes to a specific, relevant pop-
ulation (but see ref. 27). Ideally, in the study
design phase, researchers should consider to
whom their research question should ideally
apply and then take steps to recruit partic-
ipants who represent the target group and re-
port information relevant to this process.
Through a focus on theory-relevant sampling
and interdisciplinary discussion between
neuroscientists and population scientists,
population neuroscience can improve the ex-
ternal validity, replicability, and generalizability
of neuroscience findings to relevant pop-
ulations and clarify to which population(s) the
results can be generalized. In parallel, this
creates opportunities to make the results more
applicable to a wider range of social scientific
fields and to test key theoretical questions of
interest to population scientists. To achieve
these goals, however, requires a cross-discipline
emphasis on both the internal validity (a
greater focus in neuroscience) and the exter-
nal validity of studies (a greater focus within
population studies). It also requires multidis-
ciplinary communication and collaboration
(e.g., development of methodology and com-
mon language shared across areas), so that
critical methods and findings across the dis-
ciplines that study these phenomena can in-
form each other.
Getting Inside the Black Box: Adding
Neuroimaging and Other Neuroscience
Methods to Broader Population Science
Over the last several years, there has been
a rapid expansion of social and population
researchers using biomarkers (e.g., cortisol re-
sponse, cholesterol levels, epigenetics) to ex-
amine how the social environment gets under
the skin (17, 35). Examining these biological
mechanisms of social environment and health
has produced important justifications for
funding of continued work in the social and
population sciences (5, 36). However, very little
current population-focused work has exam-
ined the brain, which may be an optimal bio-
marker to examine the wide variety of variables
typically found in population-based studies
(e.g., health, decision-making, educational
achievement, acceptance of new ideas). Thus,
the current practice of many population re-
searchers using large, omnibus studies is well
suited to allow even a small group of popu-
lation neuroscience experts to make imme-
diate impacts on a wide variety of research
areas. With the help of neuroscientists, popu-
lation scientists can begin to open the black
box of the brain that has long been assumed,
but rarely examined, in most population-
For example, one set of topics of interest to
population scientists includes the effects of
neighborhood and family poverty and social
inequality on later outcomes such as family
instability, educational attainment, health, em-
ployment, crime, and psychiatric disorders.
Although population research has established
robust effects of poverty and inequality on
these outcomes (37–42), neural mechanisms of
these effects have not been a primary focus in
population approaches. However, within neu-
roscience and health psychology, research has
begun to show that early life experiences such
as parenting and SES have effects on brain
areas such as the amygdala and prefrontal
cortex (43–47), areas that have also been linked
to a variety of relevant outcomes such as crime
and violence (48, 49), depression (50), social
cognition (51, 52), drug use (53, 54), and
cognitive control (55). For example, a recent
study demonstrated that early life stress pre-
dicted stress responses in the hypothalamic-
pituitary-adrenal axis, which in turn predicted
connectivity between the amygdala and pre-
frontal cortex and later risk for depression (56).
Therefore, emerging research showing that
early experience can affect the function,
structure, and connections within and be-
tween key brain areas may help explain why
experiences such as poverty lead to delete-
rious health and behavioral outcomes and
also why some individuals are more susceptible
to these experiences.
Although this research is beginning to elu-
cidate biological embedding of experience at
the neural level, it has not yet addressed a
second related key process linking experience
and behavior: the effect of culture in de-
veloping cognitive maps that allow us to un-
derstand and navigate the world (57). For
example, our brains help us to acquire and
then use complex information specific to our
culture(s), such as knowing the difference be-
tween breakfast and snack foods, how to re-
spond to authority, whether smoking is bad,
etc., but the content of these cognitive maps is
not currently accessible through neuroimaging,
suggesting the importance of interdisciplinary
partnerships (58–60). Furthermore, additional
work linking such processes with macrolevel
variables (e.g., social network structure) and
biological variables (e.g., neural responses to
cognitive tasks) also stands to advance both
population and neuroscientific theory.
Overall, by partnering with neuroscientists,
population scientists can specify new biological
processes such as brain structure and function,
which would shape cognition and perception
of experiences thereby influencing behavior.
This process leads to an interaction through
which experience and biology shape each other
over time (61, 62). Although neuroscience
cannot capture experience at all levels, it can
help to specify how some experience is bi-
ologically embedded and how experience and
biology interact over time to explain questions
of interest to population scientists.
How can these goals become reality? How can
we link multiple levels of analysis (i.e., move
from synapse to cell to brain to individual to
groups to regions to nations)? Below, we out-
line six concrete steps toward the big picture
goal of promoting generalizability in neuro-
science investigations and harnessing neuro-
science tools to understand processes of
interest to population scientists.
Goal 1: Integrate Brain Imaging into
Existing Representative (Sub)samples. By
using techniques such as sample stratifica-
tion, cluster sampling, subsampling, and
“planned missingness” (63), neuroscience
methods can be integrated with ongoing and
large-scale population-level studies without
needing to collect neural data on every sample
Falk et al.PNAS Early Edition
| 3 of 8
participant. This strategy can afford greater
generalizability of brain processes and insights
about underlying mechanisms that contribute
to macro level processes (10). Larger-scale
studies of the type typically conducted by
survey researchers and demographers often
contain rich longitudinal measures of behav-
ior and experience. Presently, it is rare for
these types of data to be examined in dialogue
with neuroimaging data [although a growing
number of studies have scanned subsamples
of participants within specific existing longi-
tudinal or archival studies (64–66)]. One
large-scale example that might be considered
a model for integration of brain imaging into
existing representative samples is the National
Institutes of Health Pediatric MRI Database
(NIH-PD) (6, 67), which used population-
based sampling at six sites, based on the 2000
census data. This study includes information
about social and environmental variables (e.g.,
socioeconomic position, prenatal exposure to
risk factors such as alcohol), cognitive tests,
and behavioral measures, including labora-
tory-based tests of executive function and
academic skills, and a range of structural
brain images. The key to this point is that
using these sampling techniques, along with
“piggybacking” on another study, means that
neuroimaging studies need not necessarily be
of large magnitude to yield large findings if
sampled thoughtfully within the context of
a larger study. Moreover, when piggybacked
on another study, the neuroimaging data are
enhanced through more precise (and often
longitudinal) behavioral phenotypes at both
the individual and macro level of behavioral
analysis (6, 64). Substantially more work is
needed to broaden this understanding and to
more fully integrate what is known about
large-scale social phenomena with the indi-
vidual level processes that yield these larger-
Goal 2: Development of Methods to Scale
Up Neuroimaging Studies to Larger and
More Representative Samples with Meth-
ods Allowing for Cross-Study, Cross-Age,
and Cross-Culture Comparisons. Recently,
several large-scale neuroimaging studies have
emerged by expertly piecing together smaller
convenience samples (68, 69), scanning larger
and larger samples of individuals (6, 70, 71),
using data sharing and open access data (68,
72–74), consortium models (75–77), and
neuroimaging meta-analysis (78, 79). These
models emphasize that neuroimaging ap-
proaches can be done on a larger scale and
across research teams. However, these studies
also highlight many of the current methodo-
logical challenges to going big with neuro-
imaging studies (68, 75, 78). For example,
neuroscience methods have lagged in terms of
addressing the use of multiple scanners (69,
80, 81), standardizing tasks used for func-
tional (and resting-state: ref. 82) MRI studies,
understanding the effect of different pulse
sequences on findings (83, 84), standardiza-
tion of single data processing streams (85, 86),
and statistical approaches for issues such as
multiple comparisons (68). Understanding
the extent to which different laboratories,
scanners, and methods can reliably collect
data (80) is also vital to population-based
studies because most large studies rely on
cluster sampling, which is conducive to using
multiple laboratories for imaging. Cross-team
data sharing also highlights the need for better
methods for secure data sharing and com-
putation across sites. Moreover, research is
needed to examine factors that affect MRI
results (e.g., head motion, ability to attend to a
task) that covary with health factors and be-
haviors (e.g., chronic hypertension, smoking),
which may be linked to environmental factors
being studied (e.g., SES, geography). We also
need designs and analytic tools that allow us
to combine variables and processes at different
time scales and levels of analysis. Finally, re-
search that examines translation from neuro-
imaging to neural methods or proxies including
self-report measures that are cost-effective and
can be implemented at a population level are
also critical in the translation of this research
from smaller samples to larger samples and to
wide-scale clinical relevance.
Ultimately, methodological advances (in-
cluding sampling approaches) will be in-
herently intertwined with theory and the
hypotheses being tested in these studies.
Although sampling for large-scale represen-
tativeness may not be appropriate for all
research questions, goal 2 emphasizes the
need for methods in cases where the scien-
tific question calls for this type of general-
izability and/or where theoretical questions
cannot be adequately addressed using data
from one level of analysis alone.
Goal 3: Use Strategic Sampling When
Recruiting for Stand-Alone fMRI Studies.
demographers can partner with neuroscientists
to improve the inferential and statistical quality
of smaller studies by helping neuroscientists
clarify to whom it is important for their results
to generalize and then to sample accordingly.
Population scientists have extensive experience
using a variety of methods to reduce bias, in-
crease power, and improve causal inference in
smaller sample studies (87). These methods
include knowledge of matching sampling
frames and target populations to reduce cov-
erage error, reducing sampling costs by using
cluster samples and subsampling within clus-
ters, and improving representativeness through
stratified sampling (23, 88–91). For example,
developmental scientists interested in the role
of psychological resources on limbic system
reactivity during adolescence might benefit
from input from population scientists in
selecting specific subgroups of adolescents who
represent the demographic profile of high- and
low-risk adolescents within the United States.
The process of more precisely specifying the
target population can help clarify theoretical
predictions and advance understanding of
boundary conditions for effects observed. In
considering the demographic profile of teens,
ulation scientists might both benefit from
selecting adolescents who vary along some
key dimension (e.g., SES, risk-taking status)
and examining the interplay between the
target macro level processes and the brain.
Further, there may be great interest in ex-
amining the predictors of participation and
nonresponse in brain imagining studies with
the goal of improving and adjusting for non-
response bias in our statistical models. Com-
bined with goal 1 of scanning existing
participants of current population-based stud-
ies, a particularly useful concept may be using
a large population-based study as the control
for several smaller case-control neuroimaging
studies. This way the controls can be used to
provide generalizability, whereas the case
studies provide the necessary power for disease
or behavior conditions that may be too rare to
power a study from a standard population-
based study (e.g., see the Welcome Trust Case
Control Consortium in genetic research as an
exemplar; ref. 92).
Goal 4: Explore Moderators of Brain–
Behavior Links and Neural Predictors of
Relevant Outcomes. A growing body of
research has demonstrated that environmental
factors influence brain development. For ex-
ample, childhood SES predicts brain structure
(46) and function (45). Likewise, the size of our
social networks relates to brain structure (93,
94). Given that the social environment is
known to affect a wide array of biological
responses (5, 17, 19, 95), a next important goal
for neuroscience will be to further understand
how experience at multiple levels (e.g., culture,
family, social networks, SES) affects neural
structure and function (46, 96–101). In paral-
lel, it is certain that social and environmental
variables moderate the link between brain and
behavior (12), but further research is needed to
examine such interactions. For example a re-
cent study has demonstrated that level of
perceived social support moderates the pre-
viously much replicated relationship between
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amygdala reactivity and trait anxiety, indi-
cating that many brain–behavior relationships
may vary by environment and experience
(102). However, if research does not explore
these moderators, then these relationships may
be assumed to be invariant across people and
To fully leverage the fruits of the method-
ological goals outlined above, neuroscientists
and population scientists might also reconsider
the ways that neural variables are conceptual-
ized (103). Traditional neuroimaging research
has focused on the brain as a dependent
measure (e.g., Where do certain processes take
place in the brain? What structures support
those processes?). Decades of neuroimaging
literature have now characterized several pro-
cesses that may be able to predict outcomes of
interest to population scientists [e.g., neural
activity in response to health communications
predicts large-scale effects of media campaigns
(104)]. With this in mind, neural variables
(e.g., structure, function, connectivity) can be
hypothesized in advance and treated as pre-
dictor variables of relevant population level
outcomes (103). This use of neuroimaging
methods may contribute explanatory power
that is not readily available from other sources
and may be a source of convergent validity for
identifying the best measures of behavior (103–
105). Moreover, increasingly, there has been a
shift from the assumption that the brain is only
a dependent or independent variable but also
can be a moderator and mediator of paths from
experience to behavior or between genes, brain,
and behavior (56, 61, 102, 103, 106). Moreover,
evidence is mounting that that brain–behavior
links may be powerfully moderated by ex-
perience, context, and culture (102, 107).
Goal 5: Changing of the Cultures in
Neuroscience and Population Research.
As population research has recently begun to
acknowledge the usefulness and importance of
including genetic and other biomarker data
(108–113), scholars note a lack of brain re-
search in the examination of the influence of
macrolevel processes on health and behavior
(111, 114). Moreover, we argue that even
though most population researchers may not
use MRI data, a better general understanding
of the behaviorally relevant elements of basic
brain research will be important for the prog-
ress of the field, especially as cross-discipline
studies are becoming the norm rather than
exception. Likewise, for many neuroscience
questions, smaller targeted samples make
sense, and many findings within the social,
affective, and cognitive neurosciences have
replicated well across laboratories, across geo-
graphical locations, and across time. However,
neuroscience has compelling insight to add to
population level investigations and will benefit
from increased focus on who the relevant
sample is. Both goals will be advanced with
minimal burden by researchers providing
more basic data on the sample and how the
sample relates to larger populations that might
be of interest in both publications and grant
applications. Building on the excellent checklist
developed by Poldrack and colleagues (115), we
propose the addition of some basic sampling
strategy information, as well as the facts that
would typically be included in a Consolidated
Standards of Reporting Trials (CONSORT)
flow diagram (www.consort-statement.org/
consort-statement/) to the checklist (Table 1).
Funding for interdisciplinary training
and interaction, support for interdisciplinary
working groups, and consultation across dis-
ciplines will promote true cross-pollination.
For example, conferences that bring together
scientists across these disciplines can lead to
more explicit collaboration and a better un-
derstanding of methods and the benefits of
each discipline’s area of expertise. Funding that
focuses explicitly on these specific aims may
yield studies that have big impacts, not only on
the study’s specific question but more broadly
on how we interpret and understand neuro-
imaging and population science. Although
these studies may seem risky to funding
agencies, these are the types of studies that can
have big rewards.
Goal 6: Emphasis on Development and
Ecological and Interactional Models. One
other major theme population science brings
to a population neuroscience approach is an
appreciation of development and multilevel
ecological models (18, 19). Many fundamental
neuroscience questions require longitudinal
data and a developmental perspective (17, 61,
79). Social ecological models of development
and life course will be fundamental to the
Table 1. Guidelines for presentation of neuroimaging studies with a focus on population neuroscience areas of emphasis
Specific subject and recruitment details to report Advantages of reporting this information regularly
Target population: Author note about who the sample may
generalize to in the larger population
Draws attention to the author specified relevant population and draws attention
to when samples may be limited in generalizing to other populations
Sample design: Sampling strategy used to select potential
participants from a larger population pool
Allows readers to understand the strengths of the strategy used, as well as
possible sampling error and bias within the study, and what methods
will be needed to appropriately account for the strategy
Recruitment strategy and response rate: Techniques used to
find, contact and encourage study participation
Allows for assessment of selectivity of sample by learning the extent to which
nonresponse bias may influence the findings
Analysis exclusion criteria: Measures used to distinguish analysis sample Delimits the sample and population of who and who is not included
Attrition bias: (longitudinal studies) Rates of continued participation Allows for the assessment of selectivity of a longitudinal sample due various
types of attrition
Demographics: age, race, and ethnicity, SES at each step from
recruitment to scanning to those with usable data
Draws attention to the diversity of characteristics of the sample and potential
bias in who is retained at any step along the way
Efforts to standardize across multiple scanners if the study includes
such data: If multiple scanners or scan sites were used in data collection,
what specific steps were taken to standardize pulse sequences, protocols,
and other factors that might affect the imaging data? What steps were
taken to adjust for scanner variability without removing variability that is
due to differing demographics or participant characteristics across sites?
Draws attention to portions of the protocol that are standard across sites,
and elements that may introduce variability
A complement to the guidelines suggested by Poldrack et al. (115), which include suggestions for reporting design specification, task specification, planned comparisons,
details of the subject sample (e.g., inclusions/exclusion criteria), ethics approval, behavioral performance, image properties, preprocessing, first level modeling, group level
modeling, inferences related to statistical images, ROI analysis, and figures/tables.
Falk et al.PNAS Early Edition
| 5 of 8
organization of population neuroscience
through their emphasis on the multiple layers
of influence on behavior and cognition. Life
course and development frameworks imply
that the relationships between social context
and the brain may not be constant across an
individual’s life, may vary as a function of the
context in which individuals operate, and may
not be constant over cohorts or historical
periods (116). Developmental theory has as its
primary focus the interaction of person, pro-
cess, and context, as studied with regard to age
and age-graded transitions in processes and
relationships (20, 62, 117). Life course theories
focus on context as well (cohort, period, and
historical contexts) and will be relevant here.
Thus, these theories highlight the (individual,
societal, and historical) timing of transitions
and adaptation to various transitions, which
are likely to influence and qualify brain–
behavior relationships in powerful ways that
are yet to be examined. Beyond thinking of
these contexts as predictors and moderators of
brain processes, ecological models emphasize
that brain processes are nested and embedded
within larger social contexts at multiple levels
and are likely to be influenced by and influence
these contexts (Fig. 1). Thus, partnerships
across these disciplines may bring a more
complex and interactional framework to our
understanding of neuroscience (20, 61, 62).
Benefits to Neuroscience, Population
Science, and Broader Social Sciences
Implementation of the six steps outlined above
would promote advances in the integration of
knowledge across levels of analysis (Table 2).
In turn, neuroscience, population sciences,
and related social and biological sciences all
stand to benefit: Population neuroscience
complements other movements toward larger
team-based science (118–121), which have ac-
complished goals that could not be achieved
with a single principal investigator (e.g., recent
work in high energy physics on the Higgs
Boson; the human genome project). Recent
advances in handling “Big Data” (e.g., new
methods for secure data sharing) should also
inform this work. Most centrally, neurosci-
ence and population science will benefit from
knowing how brain structure and function
varies across groups and what can be gener-
alized, and population-based social sciences
like demography and sociology will benefit by
looking into the black box that has previously
been a stand-in for the brain.
In addition, the mechanisms uncovered by
neuroscience research can be incorporated
within larger-scale models of human behav-
ior. For example, longitudinal neuroimaging
of representative samples, with repeat scans
starting early and continuing across devel-
opment, will advance developmental science,
as well as our broad understanding of brain
plasticity and relationship to experience (12,
18, 61, 122). Sociologists and social psychol-
ogists will also have a major stake in this re-
search and will be particularly important in
helping to formulate the possible chronic
influences on brain structure and function,
such as discrimination, poverty, SES, and social
support (5, 17, 43, 44). As well, these influences
of brain function and structure may act as
moderators of brain–behavior relationships,
thus leading to a more dynamic model of
social context, brain function, and behavior.
Communication scholars are also increasingly
interested in how neuroscience methods may
inform our understanding of media effects
(e.g., effects of violent media), as well as ways
to predict individual differences in response to
health communication, political communica-
tion, and inform media campaigns (123).
However, a full understanding of how larger-
scale mediated variables interact with individ-
ual level processes requires a more sophisticated
set of methods for linking micro and macro
level processes. Similarly, neuroimaging tools
can be more efficiently applied within psychi-
atry, medicine, and clinical psychology with
advances that allow tighter linkage between the
samples under study and the broader popula-
tions that require treatment.
It should be noted that several relevant fields
already have models for success: research in
demography has transitioned from relatively
coarse measures from the census to an em-
phasis on mechanisms and processes, statisti-
cal methods, and recent integration of
biomarkers to the field (90, 110, 111). Methods
from demography might also be harnessed as
best practices for cross-cultural/cross-national
analysis and work across broader sets of mul-
tilevel problems (i.e., international, national,
regional). Likewise, in human genetics, genetic
information is used as a predictor of behavior
and moderator of the social environment. As
illustrated by imaging genetics approaches, one
Table 2.Areas of emphasis within a population neuroscience framework
Areas of emphasis Why?How?
Increase the representativeness of
samples using neuroimaging
• Neuroimaging studies based on convenience samples
may not optimally address target research
questions or may come to erroneous conclusions
• All brains are not the same
• Increased emphasis on sampling approaches (goal 5)
• Use of sophisticated sampling and analytic techniques
to decrease N needed in samples (goal 3)
Increased collection of larger,
samples at multiple points across
the life span
• Understand developmental trajectories of brain
• Increase replicability and generalizability of results
• Merging existing data sets and meta-analysis (goal 2)
• Large-scale collaborative studies
• Piggybacking neuroimaging on existing behavioral studies
• Increased work on cross-site imaging and standardization
of protocols to allow for combining samples (goal 1)
• Longitudinal imaging (goal 6)
Increase the emphasis on larger social
context and experience as a predictor
and moderator of brain-behavior links
• Evidence in social sciences emphasizes the importance
of broader context and culture on behavior
• Ignoring these variables assumes uniform
brain-behavior relationships which is unlikely
• Examination of moderators and collection of data from
diverse groups (both cross- and within-culture) (goal 4)
• Examination of ecological and interactional models
Increased training and collaboration
between neural and social scientists
• Neural science can gain from increased focus on
samples and on contextual effects
• Population science can gain from increased understanding
of the brain as a mediator of context-behavior links
• Funding focused on this “high-risk, high-reward,”
• Conferences and national meetings for collaboration
• Emphasis on making each discipline’s methods
accessible (goal 5)
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| www.pnas.org/cgi/doi/10.1073/pnas.1310134110Falk et al.
meeting place of genetics and society is in the
brain (61, 124). Therefore, neuroscience (e.g.,
MRI, psychophysiology) data could easily be
seen as an outcome of great interest to popu-
lation sciences, a moderator of environmental
influences, a mediator of gene × environment
interactions (61), or at least, an important
confounder to account for in their models.
We outlined a framework to better understand
influences and mechanisms of behavior from
culture to experience to brain structure and
function, which would also improve confi-
dence in the generalizability of neuroimaging
findings. To take action on this framework,
collaboration is needed between neurosci-
entists, survey methodologists, biophysicists,
biostatisticians, and representatives from across
social sciences, and population-based sciences
in particular. These stakeholders include
members of multiple social and behavioral
sciences (psychology, sociology, economics,
epidemiology, medicine, education, commu-
nication). Research across each of these dis-
ciplines will benefit from the resulting
knowledge. However, our point is not simply
that researchers should collaborate more
across disciplines, rather it is more pressing:
social and neural sciences are building huge
literatures that could be more efficient and
informative; however, at the present, “we
don’t know what we don’t know”: human
neuroimaging studies are limited in the extent
to which results might generalize based on
relatively less sophisticated sampling methods,
whereas social science disciplines that ignore
brain science may be missing a critical piece to
understanding behavior phenomena even at
macro levels. Thus, this is a critical moment
for these disciplines. Collaboration can and
should happen through funding opportunities,
summer institutes, cross-disciplinary training
of future scientists, graduate and postdoctoral
training opportunities across areas, and with
each area making their methods accessible to
others. This framework is meant to be dy-
namic and will be refined as members of each
of these groups agree on principles for the
collection and analysis of representative brain
imaging data. Although this goal is ambitious,
the groundwork is in place, and several large-
scale neuroimaging studies and existing na-
tionally representative surveys with interest in
adding neuroscience data provide jumping off
points (6). Accomplishment of this overarch-
ing goal will provide deeper insights about how
biology, social situations, and broader envi-
ronmental context interact to guide behavior
and development. In turn, this will advance
basic science and provide concrete insight for
the design of better interventions and policies.
ACKNOWLEDGMENTS. This paper was made possible
by the collective efforts of the Social Environment and
Neural Development (SEND) working group within the
Survey Research Center (SRC) at the University of Michi-
gan. We gratefully acknowledge the SRC for support of
this group, as well as funding supporting group mem-
bers: National Institutes of Health (NIH)-1 Grants DP2
DA035156-01 (to E.B.F.), U01AG009740 (to. J.F.), R01
DA027261 (to M.M.H.), R01 AA12217 (to M.M.H.), and
U01 AG09740 (to. K.L.), and the Robert Wood Johnson
Foundation Health and Society Scholars program (J.M.).
As we advocate cross-disciplinary collaboration, we de-
scribe how (i) our group has come together representing
many disciplines and (ii) how this paper was written as an
example of the potential of this type of group. (i) In
2010, the University of Michigan challenged social sci-
ence researchers to cross the traditional bounds of their
disciplines to think of emerging cross-disciplinary work
that would inform the science in the future. P.D.-K. and
F.J.M. received a grant from this initiative centered on
documenting important changes in the brain related to
socioeconomic differences of children and families. This
research, however, was based on small sample sizes
and a fairly basic understanding of indicators of socio-
economic differences.Thus, a conference was assembled
to bring together researchers across the social sciences
and neuroscience to discuss the state of this research
and ways to improve and validate findings. One of out-
come of this conference was that investigators across
the University began to meet and identify important
synergies across broad areas of social and neural scien-
ces. With support from the Institute for Social Research,
the senior authors began hosting monthly meetings for
these discussions. The group continues to grow and repre-
sent multiple disciplines and career stages, often with junior
members contributing “cutting edge” new approaches. (ii)
This manuscript was the result of discussions that the group
has had from 2012–2013 and emerged as a way to organize
our collective vision. Key to the production of the paper was
that the three first authors were junior investigators with
three very different backgrounds (e.g., demography,
social neuroscience, and developmental neurogenetics)
interested in collaborating and synthesizing interests
from across our fields. As the three first authors were
somewhat representative of the larger group, we were
able to structure a paper and receive feedback from the
larger group, especially in parts of the manuscript core
to each member’s expertise. We also received excellent
feedback from two reviewers: their thoughtful input
significantly strengthened the manuscript.
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