Socioeconomic Status and the Brain: Mechanistic Insights from Human and Animal Research

Center for Cognitive Neuroscience, Center for Neuroscience and Society, Department of Psychology, University of Pennsylvania, 3720 Walnut Street, Room B51, Philadelphia, Pennsylvania 19104-6241, USA.
Nature Reviews Neuroscience (Impact Factor: 31.43). 09/2010; 11(9):651-9. DOI: 10.1038/nrn2897
Source: PubMed


Human brain development occurs within a socioeconomic context and childhood socioeconomic status (SES) influences neural development--particularly of the systems that subserve language and executive function. Research in humans and in animal models has implicated prenatal factors, parent-child interactions and cognitive stimulation in the home environment in the effects of SES on neural development. These findings provide a unique opportunity for understanding how environmental factors can lead to individual differences in brain development, and for improving the programmes and policies that are designed to alleviate SES-related disparities in mental health and academic achievement.

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    • "race and gender) of our youth participants, and aspects of their familial social capital (e.g. parental education) have statistical relationships with their neurocognitive performance (Hackman & Farah, 2009; Hackman et al. 2010). The importance of neighborhood-level demography and crime, to further characterize the environment around these youth at the time of entry into the cohort, has also been noted (Noble et al. 2007; McEwen & Gianaros, 2010). "
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    ABSTRACT: Background: The contribution of 'environment' has been investigated across diverse and multiple domains related to health. However, in the context of large-scale genomic studies the focus has been on obtaining individual-level endophenotypes with environment left for future decomposition. Geo-social research has indicated that environment-level variables can be reduced, and these composites can then be used with other variables as intuitive, precise representations of environment in research. Method: Using a large community sample (N = 9498) from the Philadelphia area, participant addresses were linked to 2010 census and crime data. These were then factor analyzed (exploratory factor analysis; EFA) to arrive at social and criminal dimensions of participants' environments. These were used to calculate environment-level scores, which were merged with individual-level variables. We estimated an exploratory multilevel structural equation model (MSEM) exploring associations among environment- and individual-level variables in diverse communities. Results: The EFAs revealed that census data was best represented by two factors, one socioeconomic status and one household/language. Crime data was best represented by a single crime factor. The MSEM variables had good fit (e.g. comparative fit index = 0.98), and revealed that environment had the largest association with neurocognitive performance (β = 0.41, p < 0.0005), followed by parent education (β = 0.23, p < 0.0005). Conclusions: Environment-level variables can be combined to create factor scores or composites for use in larger statistical models. Our results are consistent with literature indicating that individual-level socio-demographic characteristics (e.g. race and gender) and aspects of familial social capital (e.g. parental education) have statistical relationships with neurocognitive performance.
    Psychological Medicine 10/2015; DOI:10.1017/S0033291715002111 · 5.94 Impact Factor
    • "The consequences of this inequity are far-reaching. First, it is well established that child poverty has adverse longterm effects on the life chances of these children as well as on their opportunities to become future productive adults (Duncan et al. 1998; Hackman et al. 2010). Second, given the inheritance of social inequality, children growing up in poverty have a great chance of becoming poor parents themselves (Corak 2006). "
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    ABSTRACT: A large body of research has demonstrated that child benefit systems are of paramount importance in reducing child poverty, thus having an important vertical equity component. Although all child benefit systems embody in one way or the other such vertical equity objective, the primary objective of child benefit systems is to (at least partly) compensate for the costs associated with childrearing and to minimize the welfare loss relative to childless families, a horizontal equity objective. Most studies are concerned with vertical equity and child poverty reduction; here we also explicitly take the dimension of horizontal equity into account. In this paper, we propose and develop a two-dimensional framework for evaluating and classifying the outcomes of child benefit systems in terms of both vertical and horizontal equity. Treating these two objectives as analytically distinct permits the construction of a synthetic index of child benefit outcomes and allows for the explicit incorporation of a value judgement about the most important objective of child benefit systems. In doing so, we propose a novel measure for gauging horizontal equity based on the cost of children implicit in commonly used equivalence scales drawing on the public finance literature. We demonstrate the potential of our evaluative framework for policy purposes by means of an empirical application for 31 European welfare states. We contribute to the literature by highlighting the role of characteristics of benefit systems in achieving certain objectives regarding horizontal and/or vertical equity.
    Social Indicators Research 08/2015; DOI:10.1007/s11205-015-1080-9 · 1.40 Impact Factor
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    • "Moreover, broader studies should also investigate the effects of socioeconomic status and the impact that this may have on cognitive functioning impairments. This sample was homogenous on SES (see Table 1) and generalizability may be limited (Hackmanet al., 2010). Attaining earlier scans may also begin to unravel the direction of impairment (i.e., do brainbased deficits affect eating behaviors or vice versa). "
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    International journal of developmental neuroscience: the official journal of the International Society for Developmental Neuroscience 07/2015; 46. DOI:10.1016/j.ijdevneu.2015.07.003 · 2.58 Impact Factor
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