Socioeconomic health disparities: A health neuroscience and lifecourse perspective
Peter J. Gianaros is an Associate Professor of Psychiatry and Psychology at the University of Pittsburgh, where he also holds faculty appointments in the Center for the Neural Basis of Cognition and the Center for Neuroscience. Gianaros’ research focuses on human individual differences in brain functionality and morphology, particularly as they relate to biological and behavioral risk factors for cardiovascular disease. Gianaros completed his doctoral training in psychology at the Pennsylvania State University and his postdoctoral training at the University of Pittsburgh. He received the Herbert Weiner Early Career Award from the American Psychosomatic Society in 2008 and the Award for Distinguished Early Career Scientific Contributions to Psychology from the American Psychological Association in 2010.
Scope of the Problem
Socioeconomic disadvantage experienced early in life confers risk not only for a shortened lifespan, but also for chronic medical conditions that emerge later in adulthood. Compared to adults from more advantaged backgrounds, for example, those raised under disadvantaged socioeconomic circumstances have disproportionately higher adult mortality and morbidity rates that are due to cardiovascular and cerebrovascular diseases (Galobardes, Smith, & Lynch, 2006), as well as chronic obstructive pulmonary disease and lung cancer (Power, Hypponen, & Smith, 2005). Developing under disadvantaged circumstances also predicts the appearance of biological risk factors and health-impairing behaviors that contribute to many chronic medical conditions in adulthood. These include elevations in circulating inflammatory molecules that compromise immune function (Phillips et al., 2009), transcriptional changes in genes that favor inflammatory states that accompany increases in the stress hormone, cortisol (Miller et al., 2009), premature declines in respiratory function (Jackson, Kubzansky, Cohen, Weiss, & Wright, 2004), and adopting disadvantageous health habits, such as cigarette smoking and other lifestyle behaviors that lead to glucose and insulin dysregulation, high cholesterol, and obesity (Batty, Lewars, Emslie, Benzeval, & Hunt, 2008; Fergusson, Horwood, Boden, & Jenkin, 2007; Lynch, Kaplan, & Salonen, 1997; Melchior, Moffitt, Milne, Poulton, & Caspi, 2007; Poulton et al., 2002; Power, Graham et al., 2005).
Importantly, the relationships between early experiences engendered by socioeconomic disadvantage and adult disease and risk for ill health persist even after accounting for the influence of adult socioeconomic factors, such as education, occupation, and income (Shonkoff, Boyce, & McEwen, 2009). These kinds of persistent relationships support lifecourse theories of social health disparities, which consider the disadvantaged conditions of early life to be critical for influencing lasting trajectories of risk for diseases of adulthood (Ben-Shlomo & Kuh, 2002; Bradley & Corwyn, 2002; Matthews & Gallo, 2010; Pollitt, Rose, & Kaufman, 2005). Put differently, the disadvantaged circumstances of early life are believed to become developmentally ‘embedded’ or ‘entrenched’ in a person’s biology, laying down deep roots for adult disease risk (Chen, Miller, Kobor, & Cole, in press; Miller & Chen, 2010). Yet despite evidence supporting lifecourse theories of social health disparities, we still know very little about the manifold pathways and mechanisms by which the socioeconomic circumstances of human development come to affect health behaviors and biological processes contributing to disparities in chronic medical conditions among adults in the U.S. and other industrialized nations.
The brief summary below highlights some findings from a program of health neuroscience research aimed at better understanding how the brain might link early life socioeconomic factors to disparities in adult health behaviors and associated profiles of disease risk. Also summarized are open questions that, if answered, will lead to a more complete mechanistic picture of how socioeconomic factors become embodied by the brain to affect health throughout life. Research on these questions will inform multidisciplinary efforts to develop social policies and advance interventions and preventative strategies aimed at reducing social health disparities.
Examples of Health Neuroscience Research on Socioeconomic Health Disparities
It is now broadly believed by researchers that the key pathways linking early life socioeconomic disadvantage to adult health involve complex interactions among genetic, environmental, psychosocial, and familial influences on the developmental assembly and long-term functioning of particular brain circuits. These circuits are important for cognitive and emotional control functions, as well as self-regulatory behaviors that impact peripheral physiology and health (Evans, 2004; McEwen & Gianaros, 2010; Power & Hertzman, 1997; Shonkoff et al., 2009; Shonkoff & Phillips, 2000; Taylor, 2010). Supporting this notion, a growing body of electrophysiological and brain imaging studies has shown that in children, markers of socioeconomic disadvantage (e.g., those linked to parental education, occupation, and income) are associated with markers of brain activity that reflect a delayed development of the prefrontal cortex, which can in turn relate to developmental delays in neurocognitive abilities that are themselves associated with early socioeconomic disadvantage, including delays in selective-attention, reading and language acquisition, and higher-order cognition (D'Angiulli, Herdman, Stapells, & Hertzman, 2008; Farah et al., 2008; Hackman, Farah, & Meaney, 2010; Kishiyama, Boyce, Jimenez, Perry, & Knight, 2009; Raizada & Kishiyama, 2010). Moreover, early socioeconomic disadvantage predicts later adult impairments in several cognitive functions and forms of impulsive decision-making that are assessed by neuropsychological tests, particularly tests on which performance depends on the functionality of the prefrontal cortex (Kaplan et al., 2001; Singh-Manoux, Richards, & Marmot, 2005; Sweitzer, Donny, Dierker, Flory, & Manuck, 2008).
In our research, we have tried to extend prior work by conducting brain imaging studies of young and midlife adults. In particular, we have examined whether markers of early socioeconomic disadvantage correlate with measures of the adult functioning of brain circuits—specifically, circuits that connect the prefrontal cortex with subcortical striatal and limbic regions. These striatal and limbic regions are targeted because they are thought to contribute to regulation of cognitive, emotional, and behavioral processes that increase risk for ill health in adulthood. We believe that evidence for these correlations will help characterize the lasting effects of early socioeconomic conditions on brain functions important for adult health.
One example is a recent study in which we examined the relationships between an indicator of early life socioeconomic disadvantage, lower parental educational attainment, and functional activity in cortical and striatal brain circuits during the processing of reward-related information. Several lines of research lay the foundation for this study. First, it has been shown that communication patterns between cortical and striatal brain circuits are important for motivation and learning, as well as impulse-control and goal-directed behaviors that can promote or undermine physical and mental health (Haber & Knutson, 2010). Specifically, the functional connections between cortical and striatal brain circuits are thought to support how we process and respond to rewarding and punishing environmental stimuli, as well as how we make and act on decisions that are adaptive under the current circumstances, while simultaneously suppressing decisions and actions that are disadvantageous (Bechara, 2005; Haber, 2003). In this sense, cortical and striatal brain circuits are important for how we represent the value of rewarding and punishing stimuli, which in turn relates to how we make decisions that can ultimately put the brakes on impulsive behaviors aimed at procuring immediate rewards (Haber, Kunishio, Mizobuchi, & Lynd-Balta, 1995; Rolls, 2000; Schultz, Tremblay, & Hollerman, 2000). Further, individual differences in the functioning of cortical and striatal circuits have been implicated in vulnerability to conditions that are linked to early socioeconomic disadvantage in lifecourse epidemiological studies, namely, addictive behaviors and impulsive decision-making (Bechara, 2005; Diekhof, Falkai, & Gruber, 2008; Jentsch & Taylor, 1999; Martin-Soelch et al., 2001; Volkow, Fowler, & Wang, 2004). In addition, several aspects of cortical and striatal activity patterns are being increasingly understood to stem not only from genetic factors (Dreher, Kohn, Kolachana, Weinberger, & Berman, 2009), but also from environmental influences over neurodevelopment. These neurodevelopmental effects include those that are important for assembling and sculpting brain circuits, such as maturational changes in cell proliferation, synapse formation, dendritic pruning and branching, and myelination (Barnea-Goraly et al., 2005; Kelly et al., 2009; Ostby et al., 2009). Finally, because of the long course of prefrontal cortical development from birth to early adulthood, a growing number of studies (e.g., Galvan et al., 2006; Van Leijenhorst et al., 2010) has focused on the notion that infancy, childhood and adolescence encompass vulnerable periods in which experience-dependent imbalances or disturbances in the interplay between prefrontal regulatory inputs to subcortical striatal regions may increase risk for impulsive decision-making, risk-taking, and adopting health-impairing lifestyle habits, like smoking and perhaps dietary preferences, that will have long-term health consequences over the lifespan (Chambers, Taylor, & Potenza, 2003; Ernst & Fudge, 2009; Fareri, Martin, & Delgado, 2008; Geier & Luna, 2009).
In view of these lines of epidemiological and neurodevelopmental evidence, we speculated that if more advantaged socioeconomic circumstances of childhood and adolescence are associated with environmental, social, and familial influences on the maturation and long-term functioning of cortical brain regions important for self-regulatory behaviors, then adults from more advantaged socioeconomic backgrounds could be expected to show greater functional activity in prefrontal regions when processing rewarding stimuli—particularly in association with quantitative markers of a stronger cortical regulation over subcortical striatal regions. To test these hypotheses using functional MRI (fMRI), we examined whether lower parental education would specifically predict alterations in cortical and striatal brain activation to reward-related stimuli that signaled winning or losing money. We also tested whether parental education would predict alterations in the neural signaling from the prefrontal cortex to striatal regions. In 76 middle-aged adults without confounding psychological or physical health disorders, we found that after accounting for the participants’ own education and several other possible confounding factors, lower parental education predicted reduced activation in the prefrontal cortex during the processing of stimuli that signaled winning money. Adults from lower parental educational backgrounds also exhibited reduced functional connections between the prefrontal cortex and a striatal region implicated in impulse-control (Gianaros et al., 2010). Altogether, these new findings suggest that adult alterations in the functioning of cortical and striatal brain circuits may represent facets of a neurobiological pathway linking the socioeconomic conditions of early development to adult brain activity patterns associated with how we process and respond to rewarding stimuli in the environment.
In a different brain imaging study, we examined the relationship between another indicator of early life socioeconomic disadvantage, the subjective report of lower parental social standing, and functional activity in the amygdala. The amygdala is a brain region important for emotional processes. As above, this study was also motivated by lifecourse conceptual theories that posit that individuals who are raised in disadvantaged socioeconomic environments are more likely than their advantaged counterparts to develop a sensitivity to social threats, leading to dysregulated forms of emotional processing and biological stress responses that can increase risk for ill health in later life (Chen & Matthews, 2001; Chen, Matthews, & Boyce, 2002; Taylor, Lerner, Sage, Lehman, & Seeman, 2004). This lifecourse idea parallels the notion discussed above that disease trajectories may be developmentally ‘embedded’ in the brain by early disadvantaged socioeconomic circumstances (Hertzman, 1999; Miller & Chen, 2007; Shonkoff et al., 2009). Consistent with lifecourse perspectives, we found that low subjective parental social standing uniquely predicted larger amygdala responses to threatening (angry) facial expressions, but not to neutral or surprised facial expressions (Gianaros, Horenstein et al., 2008). Moreover, this relationship was observed among 33 healthy undergraduate students who had not yet reached their own adult socioeconomic position (i.e., they had not yet finished their schooling or entered a long-term occupation), and it was not explained by potentially confounding factors, including sex, ethnicity, personality traits, symptoms of depression or anxiety, and the participants’ own reports of their social standing. Because the amygdala is important for processing and responding to emotional information and social situations, and because it is developmentally sensitive to early life stress, increased amygdala responsivity to threatening environmental stimuli might reflect a neural ‘embedding’ of disadvantaged socioeconomic experiences. In other brain imaging research, we have found that amygdala responsivity is associated with cardiovascular reactions to stress (Gianaros, Sheu et al., 2008) and preclinical atherosclerosis (Gianaros, Hariri et al., 2009). Therefore, it is possible that increased amygdala responsivity (because of having developed in disadvantaged environments) may represent another neurobiological pathway linking early socioeconomic factors to adult health and disease risk, particularly cardiovascular risk.
A Closer Look at Early Life Experiences
In considering the studies above, one could ask: what are the specific early life experiences that may link multifaceted constructs like socioeconomic advantage and disadvantage to brain development and adult health? Broadly answered, these kinds of experiences are undoubtedly many and complex, but at minimum they can be grouped into those that are stressful and those that are enriching. From a stress perspective, for example, families of relative socioeconomic disadvantage are known to experience heightened levels of life stress that could be traced to economic and occupational insecurity, to a greater likelihood of residing in unsafe communities characterized by high levels of environmental toxin exposures, and to restricted access to educational, material, and other social resources (Elder, Nguyen, & Caspi, 1985; Evans, 2004; Kessler & Cleary, 1980; Matthews & Gallo, 2010; Taylor, 2010). In turn, these sources of life stress may play a role in disrupting family relationships, warm and consistent parenting styles, and stable family routines that may all affect brain development (Evans, 2004; Matthews & Gallo, 2010; Taylor, 2010). Chronically coping with stressful family experiences could also result in an increase in stress hormones that adversely affect the assembly and long-term functioning of brain circuits that are important for cognitive and emotional processes (Chen, Cohen, & Miller, 2010; Lupien, McEwen, Gunnar, & Heim, 2009).
From an enrichment perspective, socioeconomic differences in childrearing and educational repertoires could also affect brain development and later health. For example, Lareau (2003) found that parents from more advantaged socioeconomic backgrounds are more likely to have the resources available to engage in “concerted cultivation” child-rearing efforts. These efforts may involve more frequent and regular attempts to provide stimulating learning activities and social interactions that lead to more enriched and nurturing experiences in home and school environments that provide for optimal neurocognitive developmental trajectories in children and adolescents (Evans, 2004; Matthews & Gallo, 2010; Raizada & Kishiyama, 2010). From a health neuroscience perspective, it is very likely that socioeconomic differences in such child-rearing repertoires could affect brain development through experience-dependent processes that have been identified in animal models of early environmental enrichment (Hackman et al., 2010). These include positive effects on brain cell proliferation and density, the creation of connections between brain cells (synaptogenesis), and the branching patterns of cells in cortical and subcortical regions that are important for a range of cognitive, social, and emotional behavioral processes (Chapillon, Patin, Roy, Vincent, & Caston, 2002; DiPietro, 2000; Markham & Greenough, 2004; Sullivan et al., 2006).
Needs and Opportunities for New Multidisciplinary Work
Advances in brain imaging methods and other health neuroscience tools are contributing to a better understanding of how the socioeconomic conditions of early life come to affect adult social health disparities. Future work building on the studies described here will require more multidisciplinary research efforts and funding mechanisms that support collaborations among neuroscientists, behavioral geneticists, social and biological psychologists, environmental and social epidemiologists, and policy and intervention researchers who share an interest in the developmental pathways and mechanisms by which socioeconomic factors influence the brain and health throughout life (Shonkoff et al., 2009; Shonkoff & Phillips, 2000). In particular, we need to know much more about how specific risk and resiliency factors linked to the socioeconomic conditions of early life affect brain development and brain-based health behaviors that affect health and disease risk, particularly within the broader context of genetic influences and gene signaling patterns (Miller et al., 2009; Shonkoff & Phillips, 2000). In this way, novel targets for interventions based on solid empirical evidence and more mechanistic accounts of socioeconomic factors and health could be established.
At the time of this writing, the National Children’s Study (NCS) represents what is perhaps the most exciting and promising opportunity for achieving the kind of multidisciplinary science that is needed. The NCS is now in a phase of pilot testing and planning, and when launched in the coming years will be one of the most ambitious longitudinal research attempts in history to understand child and adolescent health and development. It is expected to provide an unparalleled basis of evidence for health recommendations, intervention and preventative efforts, and social policies for the future. The NCS will be a national multi-site study that will collect a comprehensive array of genetic measures, as well as measures of developmental exposures from the environment (e.g., from air, water, diet, family interactions, neighborhood and cultural sources). It will bring together scientists from many disciplines to link these measures to health outcomes in developing children throughout the U.S. And, most important to the present discussion: the NCS will track its nationally representative sample of children from before birth until they are 21 years of age. Critically, these children will span a diverse socioeconomic spectrum. These expansive efforts of the NCS are being made possible by several federal funding sources, including the National Institutes of Health (specifically the National Institute of Child Health and Human Development and National Institute of Environmental Health Sciences), Centers for Disease Control and Prevention, and Environmental Protection Agency. It goes without saying that funding should remain a high priority for this work if we are to fully address the complex lifecourse determinants of social health disparities in this country.
In addition to prioritizing funding, there is a critical need to organize and coordinate efforts among non-primary NCS researchers from different disciplines to contribute their expertise in the design and analysis of NCS Adjunct Studies. These adjunct studies represent unique opportunities for researchers to get involved in the NCS by studying sub-samples of participants in the main NCS cohort. Such studies can involve an examination of available biological and environmental measures (e.g., of socioeconomic factors), and they can take place at one or more of the NCS study sites. They could also involve the recruitment and testing of NCS participants in ancillary research protocols (e.g., those integrating longitudinal NCS measures brain imaging variables) that can answer the kinds of questions that may be impractical for an independent research team working alone, or impractical to implement at all NCS sites. Finally, adjunct studies can be funded by non-NCS grants from federal or public or private agencies.
Finally, there are two exemplary models that illustrate how to effectively bring together the kinds of multidisciplinary researchers committed to addressing social health disparities, particularly researchers who could contribute appreciably to the NCS. The first is the MacArthur Research Network on Socioeconomic Status and Health, which recently published an essential collection of papers on social health disparities for students, researchers, and policy makers alike (Adler & Stewart, 2010). The second model is represented by the recent conference on "Child Health and Well-being: Socioeconomic Position, Biological Links, and Policy Solutions," held at the University of Wisconsin–Madison Institute for Research on Poverty. The complex and interdisciplinary issues surrounding the origins of social health disparities can only be addressed if these kinds of multidisciplinary efforts continue to be supported and pursued.
The research discussed here was done in close collaboration with Drs. Stephen B. Manuck and Karen A. Matthews at the University of Pittsburgh, and Dr. Sheldon Cohen at Carnegie Mellon University. Research funding was provided by grants from the National Heart Lung and Blood Institute of the National Institutes of Health.
Adler, N. E., & Stewart, J. (2010). The biology of disadvantage: Socioeconomic status and health (Vol. 1186). New York: The New York Academy of Sciences.
Barnea-Goraly, N., Menon, V., Eckert, M., Tamm, L., Bammer, R., Karchemskiy, A., et al. (2005). White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study. Cereb Cortex, 15, 1848-1854.
Batty, G. D., Lewars, H., Emslie, C., Benzeval, M., & Hunt, K. (2008). Problem drinking and exceeding guidelines for 'sensible' alcohol consumption in Scottish men: associations with life course socioeconomic disadvantage in a population-based cohort study. BMC Public Health, 8, 302.
Bechara, A. (2005). Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat Neurosci, 8, 1458-1463.
Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31, 285-293.
Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371-399.
Chambers, R. A., Taylor, J. R., & Potenza, M. N. (2003). Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. American Journal of Psychiatry, 160, 1041-1052.
Chapillon, P., Patin, V., Roy, V., Vincent, A., & Caston, J. (2002). Effects of pre- and postnatal stimulation on developmental, emotional, and cognitive aspects in rodents: a review. Devolpmental Psychobiology, 41, 373-387.
Chen, E., Cohen, S., & Miller, G. E. (2010). How low socioeconomic status affects 2-year hormonal trajectories in children. Psychological Science, 21, 31-37.
Chen, E., & Matthews, K. A. (2001). Cognitive appraisal biases: an approach to understanding the relation between socioeconomic status and cardiovascular reactivity in children. Annals of Behavioral Medicine, 23, 101-111.
Chen, E., Matthews, K. A., & Boyce, W. T. (2002). Socioeconomic differences in children's health: how and why do these relationships change with age? Psychological Bulletin, 128, 295-329.
Chen, E., Miller, G. E., Kobor, M. S., & Cole, S. W. (in press). Maternal warmth buffers the effects of low early-life socioeconomic status on pro-inflammatory signaling in adulthood. Molecular Psychiatry.
D'Angiulli, A., Herdman, A., Stapells, D., & Hertzman, C. (2008). Children's event-related potentials of auditory selective attention vary with their socioeconomic status. Neuropsychology, 22, 293-300.
Diekhof, E. K., Falkai, P., & Gruber, O. (2008). Functional neuroimaging of reward processing and decision-making: a review of aberrant motivational and affective processing in addiction and mood disorders. Brain Research Review, 59, 164-184.
DiPietro, J. A. (2000). Baby and the brain: advances in child development. Annual Review of Public Health, 21, 455-471.
Dreher, J. C., Kohn, P., Kolachana, B., Weinberger, D. R., & Berman, K. F. (2009). Variation in dopamine genes influences responsivity of the human reward system. Proceedings of the National Academy of Science USA, 106, 617-622.
Elder, G. H., Jr., Nguyen, T. V., & Caspi, A. (1985). Linking family hardship to children's lives. Child Development, 56, 361-375.
Ernst, M., & Fudge, J. L. (2009). A developmental neurobiological model of motivated behavior: anatomy, connectivity and ontogeny of the triadic nodes. Neuroscience and Biobehavioral Reviews, 33, 367-382.
Evans, G. W. (2004). The environment of childhood poverty. American Psychologist, 59, 77-92.
Farah, M. J., Betancourt, L., Shera, D. M., Savage, J. H., Giannetta, J. M., Brodsky, N. L., et al. (2008). Environmental stimulation, parental nurturance and cognitive development in humans. Developmental Science, 11, 793-801.
Fareri, D. S., Martin, L. N., & Delgado, M. R. (2008). Reward-related processing in the human brain: developmental considerations. Developental Psychopathology, 20, 1191-1211.
Fergusson, D. M., Horwood, L. J., Boden, J. M., & Jenkin, G. (2007). Childhood social disadvantage and smoking in adulthood: results of a 25-year longitudinal study. Addiction, 102, 475-482.
Galobardes, B., Smith, G. D., & Lynch, J. W. (2006). Systematic review of the influence of childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood. Annals of Epidemiology, 16, 91-104.
Galvan, A., Hare, T. A., Parra, C. E., Penn, J., Voss, H., Glover, G., et al. (2006). Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents. Journal of Neuroscience, 26, 6885-6892.
Geier, C., & Luna, B. (2009). The maturation of incentive processing and cognitive control. Pharmacology, Biochemistry, and Behavior, 93, 212-221.
Gianaros, P. J., Hariri, A. R., Sheu, L. K., Muldoon, M. F., Sutton-Tyrrell, K., & Manuck, S. B. (2009). Preclinical atherosclerosis covaries with Individual differences in reactivity and functional connectivity of the amygdala. Biological Psychiatry, 65, 943-950.
Gianaros, P. J., Horenstein, J. A., Hariri, A. R., Sheu, L. K., Manuck, S. B., Matthews, K. A., et al. (2008). Potential neural embedding of parental social standing. Social Cognitive and Affective Neuroscience, 3, 91-96.
Gianaros, P. J., Manuck, S. B., Sheu, L. K., Kuan, D. C., Votruba-Drzal, E., Craig, A. E., et al. (2010, in press). Parental education predicts corticostriatal functionality in adulthood. Cerebral Cortex.
Gianaros, P. J., Sheu, L. K., Matthews, K. A., Jennings, J. R., Manuck, S. B., & Hariri, A. R. (2008). Individual differences in stressor-evoked blood pressure reactivity vary with activation, volume, and functional connectivity of the amygdala. Journal of Neuroscience, 28, 990-999.
Haber, S. N. (2003). The primate basal ganglia: parallel and integrative networks. Journal of Chemical Neuroanatomy, 26(4), 317-330.
Haber, S. N., & Knutson, B. (2010). The reward circuit: Linking primate anatomy and human imaging. Neuropsychopharmacology, 35, 4-26.
Haber, S. N., Kunishio, K., Mizobuchi, M., & Lynd-Balta, E. (1995). The orbital and medial prefrontal circuit through the primate basal ganglia. Journal of Neuroscience, 15, 4851-4867.
Hackman, D. A., Farah, M. J., & Meaney, M. J. (2010). Socioeconomic status and the brain: mechanistic insights from human and animal research. Nature Reviews Neuroscience, 11, 651-659.
Hertzman, C. (1999). The biological embedding of early experience and its effects on health in adulthood. Ann NY Acad Sci, 896, 85-95.
Jackson, B., Kubzansky, L. D., Cohen, S., Weiss, S., & Wright, R. J. (2004). A matter of life and breath: childhood socioeconomic status is related to young adult pulmonary function in the CARDIA study. International Journal of Epidemiology, 33, 271-278.
Jentsch, J. D., & Taylor, J. R. (1999). Impulsivity resulting from frontostriatal dysfunction in drug abuse: implications for the control of behavior by reward-related stimuli. Psychopharmacology (Berl), 146, 373-390.
Kaplan, G. A., Turrell, G., Lynch, J. W., Everson, S. A., Helkala, E. L., & Salonen, J. T. (2001). Childhood socioeconomic position and cognitive function in adulthood. International Journal of Epidemiology, 30, 256-263.
Kelly, A. M., Di Martino, A., Uddin, L. Q., Shehzad, Z., Gee, D. G., Reiss, P. T., et al. (2009). Development of anterior cingulate functional connectivity from late childhood to early adulthood. Cerebral Cortex, 19, 640-657.
Kessler, R. C., & Cleary, P. D. (1980). Social class and psychological distress. American Sociological Review, 45, 463-478.
Kishiyama, M. M., Boyce, W. T., Jimenez, A. M., Perry, L. M., & Knight, R. T. (2009). Socioeconomic disparities affect prefrontal function in children. Journal of Cognitive Neuroscience, 21, 1106-1115.
Lareau, A. (2003). Unequal childhoods: class, race, and family life. University of California Press, Berkeley, CA.
Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10, 434-445.
Lynch, J. W., Kaplan, G. A., & Salonen, J. T. (1997). Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse. Social Science Medicine, 44, 809-819.
Markham, J. A., & Greenough, W. T. (2004). Experience-driven brain plasticity: beyond the synapse. Neuron Glia Biology, 1, 351-363.
Martin-Soelch, C., Leenders, K. L., Chevalley, A. F., Missimer, J., Kunig, G., Magyar, S., et al. (2001). Reward mechanisms in the brain and their role in dependence: evidence from neurophysiological and neuroimaging studies. Brain Research Review, 36, 139-149.
Matthews, K. A., & Gallo, L. C. (2010). Psychological perspectives on pathways linking socioeconomic status and physical health. Annual Review of Psychology, 72, 742-747.
McEwen, B. S., & Gianaros, P. J. (2010). Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and disease. Annals of the New York Academy of Science, 1186, 190-222.
Melchior, M., Moffitt, T. E., Milne, B. J., Poulton, R., & Caspi, A. (2007). Why do children from socioeconomically disadvantaged families suffer from poor health when they reach adulthood? A life-course study. American Journal of Epidemiology, 166, 966-974.
Miller, G. E., & Chen, E. (2007). Unfavorable socioeconomic conditions in early life presage expression of proinflammatory phenotype in adolescence. Psychosomatic Medicine, 69, 402-409.
Miller, G. E., & Chen, E. (2010). Harsh family climate in early life presages the emergence of a proinflammatory phenotype in adolescence. Psychological Science, 21, 848-856.
Miller, G. E., Chen, E., Fok, A. K., Walker, H., Lim, A., Nicholls, E. F., et al. (2009). Low early-life social class leaves a biological residue manifested by decreased glucocorticoid and increased proinflammatory signaling. Proceedings of the National Academy of Sciences USA, 106, 14716-14721.
Ostby, Y., Tamnes, C. K., Fjell, A. M., Westlye, L. T., Due-Tonnessen, P., & Walhovd, K. B. (2009). Heterogeneity in subcortical brain development: A structural magnetic resonance imaging study of brain maturation from 8 to 30 years. Journal of Neuroscience, 29, 11772-11782.
Phillips, J. E., Marsland, A. L., Flory, J. D., Muldoon, M. F., Cohen, S., & Manuck, S. B. (2009). Parental education is related to C-reactive protein among female middle-aged community volunteers. Brain Behavior and Immunity, 23, 677-683.
Pollitt, R. A., Rose, K. M., & Kaufman, J. S. (2005). Evaluating the evidence for models of life course socioeconomic factors and cardiovascular outcomes: a systematic review. BMC Public Health, 5, 7.
Poulton, R., Caspi, A., Milne, B. J., Thomson, W. M., Taylor, A., Sears, M. R., et al. (2002). Association between children's experience of socioeconomic disadvantage and adult health: a life-course study. Lancet, 360, 1640-1645.
Power, C., Graham, H., Due, P., Hallqvist, J., Joung, I., Kuh, D., et al. (2005). The contribution of childhood and adult socioeconomic position to adult obesity and smoking behaviour: an international comparison. International Journal of Epidemiology, 34, 335-344.
Power, C., & Hertzman, C. (1997). Social and biological pathways linking early life and adult disease. British Medical Bulletin, 53, 210-221.
Power, C., Hypponen, E., & Smith, G. D. (2005). Socioeconomic position in childhood and early adult life and risk of mortality: a prospective study of the mothers of the 1958 British birth cohort. American Journal of Public Health, 95, 1396-1402.
Raizada, R. D., & Kishiyama, M. M. (2010). Effects of socioeconomic status on brain development, and how cognitive neuroscience may contribute to levelling the playing field. Frontiers in Human Neuroscience, 4, 3.
Rolls, E. T. (2000). The orbitofrontal cortex and reward. Cerebral Cortex, 10, 284-294.
Schultz, W., Tremblay, L., & Hollerman, J. R. (2000). Reward processing in primate orbitofrontal cortex and basal ganglia. Cerebral Cortex, 10, 272-284.
Shonkoff, J. P., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. Journal of the American Medical Association, 301, 2252-2259.
Shonkoff, J. P., & Phillips, D. A. (Eds.). (2000). From neurons to neighborhoods: The science of early childhood development. Washington, D.C.: National Academy Press.
Singh-Manoux, A., Richards, M., & Marmot, M. (2005). Socioeconomic position across the lifecourse: how does it relate to cognitive function in mid-life? Annals of Epidemiology, 15, 572-578.
Sullivan, R., Wilson, D. A., Feldon, J., Yee, B. K., Meyer, U., Richter-Levin, G., et al. (2006). The International Society for Developmental Psychobiology annual meeting symposium: Impact of early life experiences on brain and behavioral development. Developmental Psychobiology, 48, 583-602.
Sweitzer, M. M., Donny, E. C., Dierker, L. C., Flory, J. D., & Manuck, S. B. (2008). Delay discounting and smoking: association with the Fagerstrom Test for Nicotine Dependence but not cigarettes smoked per day. Nicotine Tobacco Research, 10, 1571-1575.
Taylor, S. E. (2010). Inaugural article: mechanisms linking early life stress to adult health outcomes. Proceedings of the National Academy of Sciences USA, 107, 8507-8512.
Taylor, S. E., Lerner, J. S., Sage, R. M., Lehman, B. J., & Seeman, T. E. (2004). Early environment, emotions, responses to stress, and health. Journal of Personality, 72, 1376-1393.
Van Leijenhorst, L., Moor, B. G., Op de Macks, Z. A., Rombouts, S. A., Westenberg, P. M., & Crone, E. A. (2010). Adolescent risky decision-making: neurocognitive development of reward and control regions. NeuroImage, 51, 345-355.
Volkow, N. D., Fowler, J. S., & Wang, G. J. (2004). The addicted human brain viewed in the light of imaging studies: brain circuits and treatment strategies. Neuropharmacology, 47 Suppl 1, 3-13.
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