Introduction

Human development does not occur within a vacuum. The environmental contexts and social connections a person experiences throughout his or her lifetime significantly impact the development of both cognitive and social skills. The incorporation of neuroscience into topics more commonly associated with the social sciences, such as culture or socioeconomic status (SES), has led to an increased understanding of the mechanisms that underlie development across the lifespan. However, more research is necessary to disentangle the complexities surrounding early environmental variation and neural development. This review highlights studies examining links between structural brain development and SES disparities of the magnitude typically found in developing countries. We do not include studies examining children who have experienced extreme forms of early adversity, such as institutionalization or severe abuse. We also limit this review to findings concerning socioeconomic disparities in brain structure, as opposed to brain function.

KEY CONCEPT 1. Socioeconomic status (SES) Refers to an individual's access to economic and social resources, as well as the benefits and social standing that come from these resources. Most often measured by educational attainment, income, or occupation.

SES is a multidimensional construct, combining objective factors such as an individual's (or parent's) education, occupation, and income (McLoyd, 1998). Neighborhood SES is also often considered (Leventhal and Brooks-Gunn, 2000), as are subjective measures of social status (Adler et al., 2000). In 2012, 46.5 million people in the United States (15%) lived below the official poverty line (United States Census Bureau, 2012) and numerous studies have reported socioeconomic disparities profoundly affecting physical health, mental well-being, and cognitive development (Anderson and Armstead, 1995; Brooks-Gunn and Duncan, 1997; McLoyd, 1998; Evans, 2006). In turn, SES accounts for approximately 20% of the variance in childhood IQ (Gottfried et al., 2003) and it has been estimated that by age five, chronic poverty is associated with a 6- to 13-point IQ reduction (Brooks-Gunn and Duncan, 1997; Smith et al., 1997). Disparities in cognitive development outweigh disparities in physical health, possibly contributing to the propagation of poverty across generations (Duncan et al., 1998).

KEY CONCEPT 2. Poverty Comparison of a household's income with a threshold level of income that varies with family size and inflation. Households below the poverty threshold are considered “poor.” Households above this threshold are considered “not poor” even if the amount of money between “poor” and “not poor” is diminutive. Poverty guideline for a family of four in 2014 is $23,850.

Evidence suggests multiple possible, and non-mutually-exclusive, explanations for these findings. Socioeconomically disadvantaged children tend to experience less linguistic, social, and cognitive stimulation from their caregivers and home environments than children from higher SES homes (Hart and Risley, 1995; Bradley et al., 2001; Bradley and Corwyn, 2002; Rowe and Goldin-Meadow, 2009). Additionally, individuals from lower SES homes report more stressful events during their lifetime, and the biological response to stressors has been hypothesized as one of the underlying mechanisms for health and cognitive disparities in relation to SES (Anderson and Armstead, 1995; Hackman and Farah, 2009; Noble et al., 2012a).

In turn, these experiential differences are likely to have relatively specific downstream effects on particular brain structures (see Figure 1 for one theoretical model). For example, disparities in the quantity and quality of linguistic stimulation in the home have been associated with developmental differences in language-supporting cortical regions in the left hemisphere (Kuhl et al., 2003; Conboy and Kuhl, 2007; Kuhl, 2007). In contrast, the experience of stress has important negative effects on the hippocampus (Buss et al., 2007; McEwen and Gianaros, 2010; Tottenham and Sheridan, 2010), the amygdala (McEwen and Gianaros, 2010; Tottenham and Sheridan, 2010), and areas of the prefrontal cortex (Liston et al., 2009; McEwen and Gianaros, 2010)—structures which are linked together anatomically and functionally (McEwen and Gianaros, 2010). As discussed below, different components of SES may differentially relate to these varying experiences, and thus may have varying associations with particular structures across the brain.

Figure 1. Hypothesized mechanisms by which SES operates to influence structural and functional brain development.

Measures of parental SES are often used as indicators of children's family or home conditions, but these distal measures may not fully account for children's experiences. For example, while a parent may be highly educated, unforeseen circumstances, such as a recession, may cause short- or long-term unemployment and inadequate income, leading to reduced resources and increased family stress experienced by the child. Studies examining an individual's own SES may more accurately represent the individual's current experience during adulthood, but may possibly discount the environmental experiences that shaped neural development as a child. Some studies have included measures of both childhood and adult SES (see Table 1), attempting to obtain a complete measure of SES development, but retrospective SES relies on the individual's memory of past events, and therefore may be biased. Overall, accurate and complete measures of SES are often difficult to obtain and these complications render it difficult to disentangle precise associations between specific socioeconomic indicators and outcomes of interest. Despite this, even approximate assessments of SES have, across multiple independent laboratories, been shown to predict clinically and statistically significant differences in brain structure and function, signifying the prominent association between environmental factors and brain development.

Table 1. Studies reporting on associations between SES and structural brain development.

SES Variables Reported in Structural Imaging Studies

Although many studies have reported a high degree of correlation between various components of SES, different socioeconomic factors reflect different aspects of experience and should not be used interchangeably (Duncan and Magnuson, 2012). For example, families with greater economic resources may be better able to purchase more nutritious foods, provide more enriched home learning environments, or afford higher-quality child care settings or safer neighborhoods. In contrast, parental education may influence children's development by shaping the quality of parent–child interactions (Duncan and Magnuson, 2012). The notion that these SES components might differentially influence development is supported by the neuroscience literature, in which whole-brain structural analyses (Lange et al., 2010; Jednoróg et al., 2012) and studies with a priori testing of regions of interest (Hanson et al., 2011; Noble et al., 2012a; Luby et al., 2013) have indicated that different SES components may be associated with different brain structural attributes. Additionally, SES disparities tend not to be global, but rather, are disproportionately associated with differences in the structures of the hippocampus, amygdala, and the prefrontal cortex (see Table 1).

Income

Household or family income is usually calculated as the sum of total income, typically measured monthly or annually. Although income can be considered a continuous variable, many studies ask participants to select what category of income they fall into. For example, a participant may indicate that they earn between $30,000 and $60,000 dollars per year, and researchers often take the midpoint of the participant's estimate (i.e., $45,000), thereby reducing variability between participants. Income is one of the more volatile of the SES markers, as family circumstances frequently fluctuate across time, resulting in varying levels of income throughout childhood and adolescence (Duncan, 1988; Duncan and Magnuson, 2012). Income-to-Needs (ITN) is a similar marker of SES, in which total family income is divided by the official poverty threshold for a family of that size. Hanson et al. (2011); Noble et al. (2012a) and Luby et al. (2013) all find significant positive correlations between income/ITN and hippocampal size, with children and adolescents from lower SES families having smaller hippocampal volumes. Examining income-related differences in amygdala volumes, we find some discrepancies across studies. While both Hanson et al. (2011) and Noble et al. (2012a) find no association between income/ITN and amygdala volume, Luby et al. (2013) report a significant positive correlation, where children from lower income homes also have smaller amygdala volumes. The families in the latter study reported lower family income than the families in the other two studies; thus it may be possible that, unlike the hippocampus, substantial income insufficiency is necessary to observe structural differences in amygdala volumes.

KEY CONCEPT 3. Income-to-Needs The ratio of total family income divided by the federal poverty level for a family of that size, in the year data were collected. A family living at the poverty line would have an income-to-needs of ratio of 1. In 2012, 20.4 million people reported an income below 50% of their poverty threshold, including 7.1 million children under the age of 18.

Education

Parental education or educational attainment is usually measured by participants reporting their highest level (or their parents' highest levels) of education (e.g., college degree). While family income has been associated with resources available to the family and levels of environmental stress (Evans and English, 2002), parental education has been more closely linked to cognitive stimulation in the home (Hoff-Ginsberg and Tardif, 1995). Compared to parents with lower levels of education, parents with higher levels of education tend to spend more time with their children (Guryan et al., 2008), use more varied and complex language (Hart and Risley, 1995; Hoff, 2003), and engage in parenting practices that promote socioemotional development (Duncan et al., 1994; McLoyd, 1997; Bradley and Corwyn, 2002). Again, like income/ITN, we find some inconsistencies across studies when examining links between parental education and children's brain structure. Luby et al. (2013) and Noble et al. (2012a) find no significant correlations between parental education (measured as the average or highest level of education of any parents or guardians living in the home) and hippocampal volumes. Hanson et al. (2011) report a significant association between right hippocampal volumes and paternal, but not maternal, education levels. There are differences across studies in reported amygdala volumes as well. Whereas Noble et al. (2012a) find a negative correlation between parental education and amygdala volumes, Luby et al. (2013) and Hanson et al. (2011) find no association. These differences may be due in part to how parental education was measured (average parental education vs. separate indicators for mothers and fathers) and/or how parental education was coded (continuously vs. categorically).

Examining the relation between brain structure and one's own educational attainment in adulthood (as opposed to parental education), both Gianaros et al. (2012) and Piras et al. (2011) found positive associations between educational attainment and increases in white matter integrity using diffusion tensor imaging (indexed by increases in fractional anisotropy and decreases in mean diffusivity, respectively). Whereas Gianaros and colleagues found widespread associations, Piras and colleagues found that, once controlling for age, only microstructural changes in the hippocampi significantly correlated with educational attainment. Noble et al. (2012b) also found no simple correlation between reported educational attainment and either hippocampal or amygdala volumes in adulthood. Educational attainment did, however, moderate the association between age and hippocampal volume. Specifically, as has been reported previously, age was quadratically related to hippocampal volume, with the volume of this structure tending to increase until approximately the age of 30, at which point volume starts to decline (Grieve et al., 2011). Although this quadratic relation between hippocampal volume and age was present across the entire sample, the volumetric reduction seen at older ages was more pronounced among less educated individuals, and was buffered among more highly educated individuals. Differences in hippocampal structure between higher and lower educated individuals may therefore be most apparent in the later stages of the lifespan.

Occupation

Occupations generally reflect education, earnings, and prestige (Jencks et al., 1988), and have been extensively studied as an important aspect of SES as they are directly related to both education and income. Chiang et al. (2011) found that occupational status, measured using the Australian Socioeconomic Index (SEI), a 0–100 scale based on an individual's occupational category, was not related to white matter integrity. However, the authors did find an interaction between occupational status and white matter integrity, controlling for subjects' age and sex. Specifically, higher SEI was associated with higher heritability white matter integrity in the thalamus, left middle temporal gyrus, and callosal splenium.

SES Composite Measures

Some studies have combined different SES markers to create average or composite measures. Cavanagh et al. (2013) used indicators of early life SES (number of siblings, number of people per room, paternal social class, parental housing tenure, and use of car by family) and current SES (current income, current social class, and current housing tenure) to predict cerebellar gray matter volume. Both composite measures positively predicted cerebellar structure, where current SES explained significant additional variance to early life SES, but not vice-versa. Staff et al. (2012) also measured both childhood SES (indexed by paternal education and childhood home conditions) as well as adult SES (indexed by the individual's educational attainment, occupational status, and neighborhood deprivation). These authors reported a significant association between hippocampal volume and childhood SES, after adjusting for the individual's SES as an adult more than 50 years later. These results may suggest that early life conditions may have an effect on structural brain development over and above conditions later in life.

The Hollingshead scale (Hollingshead, 1975) is a commonly used measure of SES, which combines occupation and education (Two-Factor Index) or occupation, education, marital status, and employment status (Four-Factor Index). Duncan and Magnuson (2003) have argued that aggregating these SES measures is faulty as fluctuations within each measure of SES differentially affect parenting and child developmental outcomes. Imaging studies using these composite measures of SES have found significant correlations between composite scores and regions in the medial temporal lobe and frontal lobe (Raizada et al., 2008; Jednoróg et al., 2012), but without knowing associations to specific SES markers, it is difficult to compare these studies with other structural imaging studies.

Neighborhood SES

Of note, SES can describe a single participant, the participant's family or even the participant's neighborhood. The neighborhood context is associated with various health outcomes (Pickett and Pearl, 2001) as it is another source of potential exposure to stressors (e.g., violence) or protection from them (e.g., community resources, social support). Some studies have found correlations between neighborhood disadvantage and cognitive outcomes independent of individual level SES (Wight et al., 2006; Sampson et al., 2008), whereas others have not (Hackman et al., 2014). Studies examining neighborhood SES and brain structure have also had mixed findings. Gianaros et al. (2007, 2012) have used census tract level data (median household income, percentage of adults with college degrees or higher, proportion of households below federal poverty line, and single mother households) to create composite indicators of community SES. Although community SES was not associated with total brain volume or gray matter volumes in regions of interest (Gianaros et al., 2007), community SES was positively associated with white matter integrity independent of self-reported levels of stress and depressive symptoms (Gianaros et al., 2012). Similarly, Krishnadas et al. (2013) found that neighborhood SES, indexed using the Scottish Index of Multiple Deprivation, was related to cortical thickness, with men living in more disadvantaged areas demonstrating more cortical thinning in areas that support language function (bilateral perisylvian cortices) than men living in more advantaged areas.

KEY CONCEPT 4. Cortical thickness Defined in neuroimaging studies as the shortest distance between the white matter surface and pial gray matter surface.

Subjective Social Status

Finally, subjective social status is another marker of SES used in some research. In these studies, participants are typically asked to indicate on a drawing of a ladder where they believe they rank in terms of social standing among a particular group. In past studies, lower social ladder standings have been correlated with negative physical and mental health outcomes (Adler et al., 2000; Kopp et al., 2004; Hu et al., 2005), even after accounting for objective measures of education, income, and potential reporting biases (Adler et al., 1994). Gianaros et al. (2007) found that subjective social status was not correlated with hippocampal or amygdala volumes, but was significantly associated with reduced gray matter volume in the perigenual area of the anterior cingulate cortex (pACC). This finding may be understood by recognizing that the pACC is a region in the brain involved in experiencing emotions and regulating behavioral and physiological reactivity to stress. Measures of subjective social status may not take into account objective measures of SES, but relate more to the individual's experience of disadvantage.

Words of Caution in Selecting SES Variables

Collecting and utilizing multiple independent measures of SES is necessary to accurately assess structural brain changes throughout development. SES is too complex to be captured by a single indicator or even a composite measure. Each measure of SES is its own distinct construct with varying associations with experience and cognitive development. However, while SES variables are not interchangeable, they are nonetheless highly correlated. It is therefore essential to avoid model multicollinearity in statistical analyses. This may be accomplished by first carefully considering which variables are most appropriate for testing particular hypotheses, and then confirming low variance inflation factors (VIF) within the model. Increasing sample size, centering variables, and utilizing residuals are additional methods to avoid inappropriate analysis and interpretation.

As a final word of caution, many of the SES indicators referenced above are based on studies completed in Western countries. Further work will be necessary to explore the generalizability of findings across different countries and cultures (Minujin et al., 2006; Lipina et al., 2011).

Covariates, Mediators, and Moderators

When examining SES disparities in brain structural development, additional demographic factors must be considered as well. First and foremost, the age of the participant must be taken into account, as brain structural volumes change significantly across childhood and adolescence (Paus et al., 1999; Lenroot and Giedd, 2006). Further, the timing of volumetric growth and reductions vary across different brain structures (Grieve et al., 2011). Inconsistencies in results across studies highlighted above may therefore be due to variability in the age ranges of the samples studied. Caution is advised when generalizing results reported within a narrow-age-range sample, as SES disparities in brain structure may vary substantially as a function of age.

Several studies include relatively wide age ranges, recruiting, for example, both children and adolescents in their imaging samples (Lange et al., 2010; Hanson et al., 2011; Noble et al., 2012a; Lawson et al., 2013). Two additional studies have taken a lifespan approach to examining SES and structural brain development (Piras et al., 2011; Noble et al., 2012b). Incorporating wide age ranges into a study allows researchers to consider whether results vary as a function of participant age. For example, both Noble et al. (2012b) and Piras et al. (2011) examine associations between subcortical structures and educational attainment in a wide age range of participants. Piras et al. (2011) found that microstructural changes in the hippocampus, but not changes in gross volume in this structure, were significantly predicted by education levels. However, due to a large negative correlation between education and age, the decreases in microstructure may have been more closely related to older age than greater education. As discussed above, Noble et al. (2012b) reported that higher levels of educational attainment buffered against age-related reductions in hippocampal volume, signifying that the association between age and hippocampal volume is not constant across all levels of education. Of course, distinctions between development and decline are, in some respects, arbitrary, and may be more appropriately classified according to functional rather than structural measures.

Sex is another important demographic characteristic to consider. Volumetric variation in brain structures increase within and between males and females during puberty (Sowell et al., 2003). Sex differences have been reported for cortical thickness. Using a longitudinal sample of participants ages 9–22 years, Raznahan et al. (2010) observed differences in cortical maturation, with males demonstrating a thicker cortex in frontopolar regions at younger ages and subsequent greater cortical thinning than females during adolescence. It has also been reported that females demonstrate more rapid cortical thinning than males in specific cortical areas (right temporal, left temporoparietal junction, and left orbitofrontal cortex) corresponding to the “social brain” (Mutlu et al., 2013). It will be important in future work to better understand how the links between SES variables and structural brain development may vary by sex, and/or a combination of sex and age.

In addition, studies have reported that families living in chronic poverty have differential outcomes based on when and for how long poverty was experienced (National Institute of Child Health and Human Development Early Child Care Research Network, 2005). While the brain is most malleable in early childhood, it nonetheless retains a substantial degree of plasticity throughout the lifespan, and the extent to which the timing and duration of socioeconomic disadvantage are associated with brain structural differences is virtually unexplored in the neuroscience literature to date.

Finally, it is important to consider environmental exposures and experiences that may account for links between distal socioeconomic factors and brain structural differences. For example, Luby et al. (2013) recently reported that links between income and hippocampal volume were mediated by caregiving support/hostility and stressful life events. Of course, there are many potential experiential correlates of SES that have not been well studied in the context of SES disparities in brain development, including nutrition, exposure to environmental toxins, safety of the play environment, or quality of the child's linguistic environment. In order to develop interventions that effectively target the SES gap in achievement, it will be essential to try to understand the particular component(s) of the environment that are most influential in explaining disparities.

Volume vs. Cortical Thickness/Surface Area

Differences in findings across studies may also be accounted for by the techniques used to measure morphometry. Most studies examining SES differences in brain structure have reported cortical volumes as their outcome of interest (but see Jednoróg et al., 2012; Liu et al., 2012; Krishnadas et al., 2013; Lawson et al., 2013). However, cortical volume is a composite measure that is determined by the product of surface area and cortical thickness, two genetically and phenotypically independent structures (Panizzon et al., 2009; Raznahan et al., 2011). Though the cellular mechanisms are not fully understood, it has been hypothesized that symmetrical cell division in the neural stem cell pool contribute to exponential increase in the number of radial columns that result in surface area, without changes to cortical thickness. In contrast, asymmetrical cell division in founder cells is independently responsible for a linear increase in the number of neurons in the radial column, leading to changes in cortical thickness but not surface area (Rakic, 2009). As such, these two properties of the cortical sheet develop differentially; cortical surface area tends to expand through childhood and early adolescence and decrease in adulthood, whereas cortical thickness tends to decrease rapidly in childhood and early adolescence, followed by a more gradual thinning and ultimately plateauing (Schnack et al., 2014). Cortical thinning is related to both synaptic pruning and increases in white matter myelination, resulting in a reduction of gray matter as measured on MRI (Sowell et al., 2003). These maturational changes occur concurrently and together contribute to the development of the mature human brain.

KEY CONCEPT 5. Cortical volumes The most commonly used outcome in studies of socioeconomic disparities in brain structure. Cortical volume is actually a composite of cortical thickness and surface area, two genetically and phenotypically distinct morphometric properties of the brain.

KEY CONCEPT 6. Surface area The area of exposed cortical surface or convex hull area (CHA) and the area of cortex hidden in sulci.

Thus, studies in which the dependent measure is cortical volume may not adequately reflect the complexities of morphometric brain development. Indeed, cross-sectional comparisons of cortical volume are poor indicators of brain maturation (Giedd and Rapoport, 2010), whereas cortical thickness has been shown to be a more meaningful index of brain development (Sowell et al., 2004; Paus, 2005) and has been associated with both cognitive ability (Porter et al., 2011) and behavior (Shaw et al., 2011). For example, IQ has been correlated with the trajectory of cortical thickness, such that, during childhood, more intelligent children have thinner cortices than children with lower IQ, with this association strengthening through adolescence. In contrast, by middle adulthood, a thicker cortex is related to higher IQ (Schnack et al., 2014). Importantly, IQ has also been independently correlated with the trajectory of surface area development, such that more intelligent children exhibit greater surface area during childhood, though surface area expansion is completed earlier and then decreases more quickly in more intelligent adults (Schnack et al., 2014). Together, these findings suggest that both surface area and cortical thickness may be critical in accounting for individual differences in cognitive abilities, and that these factors must be considered independently rather than lumping them into a single composite measure of cortical volume.

In summary, when considering associations between experience and brain morphometry, cortical thickness and surface area should be assessed separately, rather than reporting on the composite metric of cortical volume (Winkler et al., 2010; Raznahan et al., 2011). Research investigating cortical complexity and its association with SES variables will be vital to further understanding how environmental influences over the life course influence structural brain development.

Conclusions

Children living in socioeconomic disadvantage are more likely to experience cognitive delays and emotional problems (Brooks-Gunn and Duncan, 1997), but the underlying causal pathways between disadvantage and developmental outcomes are not clear. The nascent field of socioeconomic disparities in brain structure is an exciting one, which holds promise in helping to understand this question. However, while progress has been made in understanding how socioeconomic disparities may affect brain development, there are many avenues for further research. Careful social science approaches to assessing individual socioeconomic factors must be combined with cutting-edge neuroscientific approaches to measuring precise aspects of brain morphometry. Consideration of how results interact with demographic factors such as age and sex are critical. Differences in exposures and experiences that may mediate socioeconomic disparities in brain development must be rigorously assessed to help identify or confirm underlying mechanisms.

Although this review has focused on SES disparities in brain structure as opposed to function, it is readily acknowledged that the two approaches are complementary. While a structural approach lends itself to greater spatial resolution as well as, arguably, more precision in understanding proximal experience-dependent mechanisms, it is limited in terms of functional interpretations. Ultimately, linking both structural and functional imaging to cognitive outcomes is essential for examining associations between anatomy, physiology, and behavior. Brain structural measures can be viewed as mediators between SES and cognition, or as outcome variables in their own right; having clear theoretical pathways ensures accurate interpretation of results and implications, and will help inform the design of effective policies, emphasizing early and targeted interventions.

Link to Article

Introduction

Human development is profoundly influenced by environmental contexts and social interactions. Integrating neuroscience with social science domains like culture and socioeconomic status (SES) has illuminated the mechanisms behind lifelong development. However, understanding the intricate interplay between early environmental variations and neural development requires further investigation. This review examines studies linking structural brain development to SES disparities commonly observed in developing countries. It focuses specifically on socioeconomic disparities in brain structure, excluding extreme early adversity and functional brain differences.

Key Concept 1. Socioeconomic Status (SES) SES encompasses an individual's access to economic and social resources and the associated benefits and social standing. It is frequently measured by educational attainment, income, and occupation. SES represents a multidimensional construct encompassing objective factors such as an individual's (or their parents') education, occupation, and income. Neighborhood SES and subjective measures of social status are also considered. In 2012, 15% of the US population lived below the poverty line. Numerous studies highlight the profound impact of socioeconomic disparities on physical health, mental well-being, and cognitive development. SES accounts for approximately 20% of the variance in childhood IQ, with chronic poverty potentially leading to a 6- to 13-point IQ reduction by age five. The significant impact of SES on cognitive development, surpassing even physical health disparities, may contribute to the intergenerational cycle of poverty.

Key Concept 2. Poverty Poverty is determined by comparing household income to a threshold that varies with family size and inflation. Households below this threshold are classified as "poor." Several non-mutually-exclusive explanations may account for these findings. Socioeconomically disadvantaged children often experience less linguistic, social, and cognitive stimulation than their higher-SES peers. Individuals from lower SES backgrounds report more stressful life events, with the biological stress response hypothesized as a key mechanism underlying health and cognitive disparities. These experiential differences likely exert specific effects on particular brain structures. For instance, variations in linguistic stimulation quality and quantity correlate with developmental differences in left-hemisphere language-related cortical regions. Conversely, stress negatively affects the hippocampus, amygdala, and prefrontal cortex—anatomically and functionally interconnected structures. Different SES components may relate to these experiences differently, leading to varying associations with specific brain structures.

Parental SES often serves as a proxy for children's family or home environments. However, these distal measures may not fully capture children's actual experiences. While a parent might be highly educated, unforeseen circumstances like economic downturns could lead to reduced resources and increased family stress. Studies using an individual's SES in adulthood may better reflect current experiences but might overlook early neural development influences. Some studies incorporate both childhood and adult SES measures, aiming for a comprehensive understanding. However, retrospective SES assessments rely on potentially biased memory. Obtaining accurate and complete SES measures remains challenging, making it difficult to isolate precise associations between specific socioeconomic indicators and outcomes. Despite this, even approximate SES assessments consistently predict significant differences in brain structure and function, highlighting the strong link between environment and brain development.

SES Variables Reported in Structural Imaging Studies

Despite their high correlation, different SES components reflect distinct experiences and should not be used interchangeably. For example, economic resources might enable access to nutritious food, enriched learning environments, quality childcare, and safer neighborhoods. Parental education, however, might influence child development through parent-child interactions. This distinction is supported by neuroscience research, indicating that various SES components associate with different brain structural attributes. Notably, SES disparities are not global but disproportionately affect the hippocampus, amygdala, and prefrontal cortex.

Income Household or family income, usually calculated as the sum of total income, is often categorized into ranges. Income represents a volatile SES marker due to fluctuating family circumstances. Income-to-Needs (ITN) ratio, dividing total family income by the poverty threshold, is another similar marker. Studies consistently find positive correlations between income/ITN and hippocampal size, with lower-SES children exhibiting smaller hippocampal volumes. However, findings regarding amygdala volume are less consistent. While some studies report no association between income/ITN and amygdala volume, others show a positive correlation. These discrepancies might reflect differences in sample income levels, suggesting that significant income insufficiency might be necessary to observe amygdala volume differences.

Key Concept 3. Income-to-Needs Income-to-Needs is the ratio of total family income to the federal poverty level for a family of that size.

Education Parental education is typically categorized based on the highest level attained. While family income relates to available resources and environmental stress, parental education links more closely to cognitive stimulation. Compared to less educated parents, more educated parents tend to:

• Spend more time with their children

• Use more diverse and complex language

• Engage in parenting practices that foster socioemotional development

Like income/ITN, studies show inconsistencies in the relationship between parental education and children's brain structure. Some find no correlation between parental education and hippocampal volume, while others report an association specifically with paternal education and right hippocampal volume. Similar inconsistencies exist for amygdala volume, with one study reporting a negative correlation with parental education, while others find no association. These discrepancies could stem from variations in measuring (average vs. separate parental education) and coding (continuous vs. categorical) parental education.

Studies examining adult educational attainment, rather than parental education, reveal positive associations with white matter integrity, indexed by increased fractional anisotropy and decreased mean diffusivity. While one study found widespread associations, another observed that only microstructural changes in the hippocampus significantly correlated with education after controlling for age. Another study found that education moderated the quadratic relationship between age and hippocampal volume, with less educated individuals experiencing more pronounced age-related volume reductions. These findings suggest that education's impact on hippocampal structure might be most apparent later in life.

Occupation Occupations generally reflect education, earnings, and prestige, serving as a crucial aspect of SES due to their direct relationship with both education and income. However, one study found no direct association between occupational status, measured using the Australian Socioeconomic Index, and white matter integrity. Interestingly, the study did report an interaction between occupational status and white matter integrity in specific brain regions (thalamus, left middle temporal gyrus, callosal splenium), suggesting that higher occupational status might be associated with higher heritability of white matter integrity.

SES Composite Measures

Some studies employ composite SES measures combining different indicators. For instance, one study used early life and current SES indicators to predict cerebellar gray matter volume, finding that both composite measures positively predicted cerebellar structure. Notably, current SES explained additional variance beyond early life SES. Similarly, another study utilized childhood and adult SES indicators, reporting a significant association between childhood SES and hippocampal volume, even after adjusting for adult SES measured over 50 years later. These findings suggest that early life conditions might exert lasting effects on brain structure, independent of later life experiences.

The Hollingshead scale, a commonly used composite SES measure, combines occupation and education (Two-Factor Index) or occupation, education, marital status, and employment status (Four-Factor Index). However, aggregating SES measures has been criticized for masking the differential effects of fluctuations within each measure on parenting and child development. Studies using the Hollingshead scale report significant correlations with specific brain regions. However, the lack of information regarding associations with individual SES markers makes comparisons with other studies difficult.

Neighborhood SES SES can also describe neighborhood characteristics. Neighborhood disadvantage correlates with various health outcomes, potentially reflecting exposure to stressors (e.g., violence) or protective factors (e.g., community resources, social support). While some studies find associations between neighborhood disadvantage and cognitive outcomes independent of individual SES, others report no such association. Similarly, findings regarding neighborhood SES and brain structure are mixed. Some studies utilizing census-tract level data to create composite indicators of community SES find no association with brain volume but report a positive association with white matter integrity. Conversely, other studies find that neighborhood disadvantage correlates with reduced cortical thickness in specific brain areas.

Key Concept 4. Cortical Thickness Cortical thickness is defined as the shortest distance between the white matter and pial gray matter surfaces.

Subjective Social Status Subjective social status, measured using self-reported social standing on a ladder scale, is another SES marker. Lower self-reported social status has been linked to negative physical and mental health outcomes, even after controlling for objective SES measures and reporting biases. One study found no correlation between subjective social status and hippocampal or amygdala volume but reported a significant association with reduced gray matter volume in the perigenual anterior cingulate cortex (pACC). This finding aligns with the pACC's role in emotional experience and stress response regulation. While subjective social status might not fully capture objective SES measures, it reflects individuals' experiences of disadvantage.

Words of Caution in Selecting SES Variables Using multiple independent SES measures is crucial for accurately assessing structural brain changes. SES is too complex to be captured by a single indicator or composite measure. Each SES variable represents a distinct construct with varying associations with experience and cognitive development. However, the high correlation among SES variables necessitates avoiding multicollinearity in statistical analyses. This involves carefully selecting appropriate variables for testing specific hypotheses and confirming low variance inflation factors (VIFs) within the model. Increasing sample size, centering variables, and utilizing residuals can further mitigate inappropriate analysis and interpretation.

Importantly, many SES indicators stem from Western-centric studies. Further research is needed to explore the generalizability of findings across diverse cultures and countries.

Covariates, Mediators, and Moderators

When investigating SES disparities in brain structure, considering additional demographic factors is crucial.

Age Participant age is paramount, as brain structural volumes significantly change throughout childhood and adolescence, with varying growth and reduction timelines across different structures. Inconsistencies across studies might reflect variability in sample age ranges. Generalizing findings from narrow-age-range samples requires caution, as SES disparities in brain structure might fluctuate with age. Studies incorporating wide age ranges, including children and adolescents, or adopting a lifespan approach, allow for examining age-dependent variations in findings. For instance, both studies explored associations between subcortical structures and educational attainment across a wide age range. One found that education predicted hippocampal microstructural changes but not volume, the other reported that higher education buffered against age-related hippocampal volume reduction. These findings highlight the importance of disentangling developmental and aging effects when examining brain structure.

Sex Sex is another crucial demographic factor. Brain structural volumes significantly differ between males and females, particularly during puberty. Sex differences have also been reported for cortical thickness, with males exhibiting a thicker cortex in specific regions at younger ages and subsequent greater cortical thinning during adolescence. Conversely, females demonstrate faster cortical thinning in areas associated with the "social brain." Future research must elucidate how the relationship between SES and structural brain development might vary by sex and the interplay between sex and age.

Timing and Duration of Poverty Studies suggest that families experiencing chronic poverty exhibit differential outcomes based on poverty timing and duration. While the brain exhibits heightened plasticity in early childhood, it retains significant plasticity throughout life. However, research exploring the relationship between the timing and duration of socioeconomic disadvantage and brain structural differences remains limited.

Environmental Exposures and Experiences It is essential to consider environmental exposures and experiences that might mediate the link between SES and brain structure. For example, one study found that caregiving quality and stressful life events mediated the relationship between income and hippocampal volume. Further research is needed to investigate other potential mediators such as nutrition, exposure to environmental toxins, play environment safety, and linguistic environment quality. Understanding these mediating factors is crucial for developing effective interventions targeting the SES gap in achievement.

Volume vs. Cortical Thickness/Surface Area Discrepancies across studies might also arise from variations in morphometric measurement techniques. While most studies report cortical volumes, cortical volume is a composite measure influenced by surface area and cortical thickness—two genetically and phenotypically independent structures. While the cellular mechanisms remain unclear, symmetrical cell division in the neural stem cell pool is hypothesized to contribute to surface area expansion, while asymmetrical division in founder cells is thought to drive changes in cortical thickness. Consequently, these two properties develop differentially. Cortical surface area typically expands through childhood and early adolescence before declining in adulthood, whereas cortical thickness decreases rapidly in early development before plateauing. Cortical thinning relates to synaptic pruning and increased white matter myelination, resulting in reduced gray matter on MRI. These concurrent maturational changes contribute to the development of the mature brain.

Key Concept 5. Cortical Volumes Cortical volume, a commonly used outcome in SES disparity research, is a composite measure of cortical thickness and surface area.

Key Concept 6. Surface Area Surface area encompasses both the exposed cortical surface and the area hidden within sulci.

Therefore, studies relying solely on cortical volume might not fully capture the complexities of brain development. Cross-sectional cortical volume comparisons are poor indicators of brain maturation, whereas cortical thickness provides a more meaningful index, correlating with cognitive ability and behavior. For instance, IQ correlates with cortical thickness trajectories, with more intelligent children exhibiting thinner cortices during childhood, an association that strengthens through adolescence. Conversely, thicker cortices relate to higher IQ in middle adulthood. Importantly, IQ also independently correlates with surface area development, with more intelligent children showing greater surface area during childhood, followed by earlier completion of surface area expansion and faster decline in adulthood. These findings suggest that both surface area and cortical thickness are crucial for understanding individual differences in cognitive abilities and should be examined independently.

In conclusion, when investigating the relationship between experience and brain morphometry, assessing cortical thickness and surface area separately, rather than relying on cortical volume alone, is crucial. Future research should focus on cortical complexity and its association with SES to better understand how environmental influences shape brain development.

Conclusions

Socioeconomic disadvantage increases the likelihood of cognitive delays and emotional problems in children. The emerging field of socioeconomic disparities in brain structure holds promise for unraveling the underlying causal pathways. However, despite significant progress, many research avenues remain unexplored. Rigorous social science approaches for assessing individual socioeconomic factors must be combined with advanced neuroimaging techniques for precise brain morphometry measurement. Considering interactions with demographic factors like age and sex is crucial. Identifying and rigorously assessing potential mediators of SES disparities in brain development, such as environmental exposures and experiences, is essential. Integrating structural and functional imaging with cognitive outcome measures is crucial for examining the interplay between anatomy, physiology, and behavior.

Brain structural measures can serve as mediators between SES and cognition or as independent outcome variables. Establishing clear theoretical pathways ensures accurate interpretation of findings and implications, informing effective policy development and targeted interventions emphasizing early intervention strategies. By understanding the complex interplay between SES and brain development, we can strive to create a more equitable society that supports the cognitive potential of all individuals.

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How Socioeconomic Status Shapes the Developing Brain: Insights from Structural Imaging

We all know that people are shaped by their environment. But did you know that the social and economic conditions we experience throughout our lives can actually impact the way our brains develop, influencing both our thinking and social skills? By combining neuroscience with social science, researchers are beginning to understand how these factors, like socioeconomic status (SES), leave their mark on our brains. This review dives into what we've learned from brain imaging studies about the connection between differences in brain structure and SES disparities, particularly those found in developing countries. We'll be focusing on studies that look at typical variations in early childhood environments, rather than extreme cases like institutionalization or severe abuse. And to keep things focused, we'll be sticking to how SES affects brain structure rather than brain function.

KEY CONCEPT 1. Socioeconomic Status (SES) What it means: SES is a measure of a person's access to money, resources, and social standing. How it's measured: Typically, researchers look at education level, income, and job type to determine someone's SES. SES is a complex concept that goes beyond just how much money you make. It also includes factors like your parents' education and job, the neighborhood you live in, and even your own perception of your social standing. Unfortunately, millions of people face socioeconomic disadvantages that can have a huge impact on their physical health, mental well-being, and even how well they do cognitively. Research has shown that SES can explain about 20% of the differences in IQ scores among children. Sadly, children living in poverty often score 6-13 points lower on IQ tests by the age of five compared to their peers from wealthier backgrounds. These cognitive disparities can create a cycle of poverty that's hard to break, impacting future generations.

KEY CONCEPT 2. Poverty What it means: Poverty is determined by comparing a household's income to a set poverty line, which changes based on family size and inflation. Example: In 2014, a family of four in the U.S. with an income below $23,850 was considered below the poverty line. Importantly, even families slightly above the line can struggle financially.

So, why do these disparities exist? There's no single answer, but research points to several key factors. Children from disadvantaged backgrounds often receive less language stimulation, have fewer opportunities for social interaction, and experience less cognitive enrichment at home compared to their wealthier peers. They are also more likely to encounter stressful life events, and the body's biological response to stress is thought to play a role in these health and cognitive disparities.

These differences in experience can impact specific parts of the brain. For instance, variations in language exposure have been linked to developmental differences in brain regions responsible for language processing, mainly in the left hemisphere. On the other hand, exposure to stress can negatively affect areas like the hippocampus, amygdala, and prefrontal cortex, all of which are interconnected both structurally and functionally. As we explore further, you'll see how different aspects of SES, such as income versus education, can lead to distinct experiences and, therefore, have different effects on specific brain regions.

While parental SES is often used as a proxy for a child's home environment, it doesn't capture the whole picture. A parent might be highly educated but face unexpected unemployment, leading to financial strain and stress for the entire family. Similarly, studying an adult's SES might not fully reflect the environmental influences that shaped their brain during childhood. Ideally, researchers would track SES throughout a person's life to gain a more accurate understanding, but this can be challenging. Despite these limitations, research consistently shows that even rough estimates of SES can predict significant differences in brain structure and function. This highlights the profound impact of environmental factors on brain development.

Breaking Down SES: Different Variables, Different Effects on the Brain

While various aspects of SES are often correlated, it's important to remember they represent different facets of a person's experience and shouldn't be used interchangeably. Let's take a closer look at how specific SES variables relate to differences in brain structure.

Income

Household income, the combined income of all members, is a key indicator of SES. While income can be measured continuously, many studies simplify by asking participants to select an income bracket. For example, a participant might report earning between $30,000 and $60,000 per year. Researchers often use the midpoint of this range ($45,000) to reduce variability. Income is one of the more fluid SES markers, as families experience financial ups and downs throughout their lives, especially during childhood and adolescence. A similar indicator is the Income-to-Needs (ITN) ratio, which compares total family income to the poverty threshold for their family size. Several studies have found a link between lower income/ITN and smaller hippocampal volume in children and adolescents. The findings on amygdala volume are less clear-cut. While some studies report no association between income/ITN and amygdala size, others show a positive correlation, with smaller amygdala volumes found in children from lower-income homes. These inconsistencies might be because more severe income insufficiency is needed to see an effect on the amygdala compared to the hippocampus.

KEY CONCEPT 3. Income-to-Needs Ratio (ITN) What it means: The ITN ratio compares a family's total income to the federal poverty level for a family of their size. Example: A family earning exactly the poverty level would have an ITN ratio of 1. In 2012, a staggering 20.4 million Americans, including 7.1 million children, reported living on less than half of their poverty threshold.

Education

Parental education, usually measured by the highest level of education achieved by either parent, is another important SES factor. While income is linked to access to resources and environmental stressors, parental education is more closely tied to the quality of cognitive stimulation a child receives at home. Parents with higher education levels tend to engage in richer language use with their children, spend more time with them, and adopt parenting practices that nurture their socioemotional development.

However, similar to findings on income, the relationship between parental education and children's brain structure is complex. Studies show mixed results regarding parental education and hippocampal volume. Some report no correlation, while others find a connection between higher paternal education and larger right hippocampal volume. Findings on amygdala volume are similarly inconsistent, with some studies reporting no association and others showing a negative correlation. These inconsistencies might stem from differences in how parental education was measured and analyzed across studies.

Interestingly, a person's own educational attainment in adulthood has also been linked to brain structure. Studies using Diffusion Tensor Imaging (DTI), a technique that examines the brain's white matter, found that higher educational attainment is associated with greater white matter integrity in adulthood. White matter integrity reflects how well different brain regions are connected, which is crucial for cognitive function. However, when controlling for age, the link between education and white matter was primarily observed in the hippocampus. This suggests that the hippocampus might be particularly sensitive to the cognitive benefits of education. Another study found that while education wasn't directly related to hippocampal or amygdala volume, it did moderate the relationship between age and hippocampal volume. Specifically, higher education seemed to protect against the age-related decline in hippocampal volume typically observed as people get older. This highlights the importance of considering age when studying the impact of SES on the brain.

Occupation

Occupation is another important dimension of SES. It often reflects a person's education level, income, and social standing. One study using the Australian Socioeconomic Index (SEI), a measure based on occupational category, found that while occupation itself wasn't directly related to white matter integrity, it did interact with it. People with higher SEI scores showed a stronger link between their genes and the integrity of white matter in specific brain areas like the thalamus, which relays sensory information, and the left middle temporal gyrus, involved in language processing.

Combining SES Markers: Composite Measures

Some studies create composite measures by combining different SES markers. For example, Cavanagh et al. used a combination of early life SES indicators (like parental social class and housing) and current SES markers (like current income and housing) to predict cerebellar gray matter volume, a brain region involved in motor control and coordination. They found that both early and current SES positively predicted cerebellar structure, suggesting that both childhood and adulthood experiences contribute to its development. Similarly, Staff et al. combined childhood and adult SES measures and found a long-lasting association between childhood SES and hippocampal volume, even after accounting for the participants' SES in adulthood, which was measured more than 50 years later. This suggests that early life experiences can have a profound and enduring impact on the brain.

The Hollingshead scale is another common composite measure that combines education and occupation (Two-Factor Index) or adds marital and employment status (Four-Factor Index). However, some argue that simply combining these measures might not accurately reflect the complex and dynamic nature of SES, as fluctuations in each component can differently impact parenting and child development.

Neighborhood: SES on a Larger Scale

Remember, SES isn't just about an individual; it also extends to their neighborhood. The neighborhood environment can influence health outcomes. Living in a disadvantaged neighborhood can expose individuals to more stressors like violence, while access to community resources and support can buffer against these negative effects. While some studies have found a link between neighborhood disadvantage and cognitive outcomes, even after accounting for individual SES, others haven't. Similarly, the link between neighborhood SES and brain structure is complex. Some studies, using census data to measure neighborhood SES, found a positive correlation between higher neighborhood SES and greater white matter integrity in the brain, while others reported an association between neighborhood disadvantage and cortical thinning in areas supporting language function.

KEY CONCEPT 4. Cortical Thickness. What it means: In brain imaging, cortical thickness refers to the distance between the brain's surface and the underlying white matter. It's like measuring how thick the "bark" of the brain is.

Subjective Social Status: How We Perceive Ourselves

Subjective social status captures how people perceive their own social standing. In studies, participants are often asked to place themselves on a symbolic ladder representing social hierarchy. This measure reflects their personal sense of social rank. Interestingly, studies have shown that a lower subjective social status is associated with negative health outcomes, both physical and mental, even after accounting for objective SES measures and potential biases. One study found that lower subjective social status was associated with reduced gray matter volume in the perigenual anterior cingulate cortex (pACC), a brain area involved in processing emotions and regulating stress responses. This suggests that our perception of our social standing, regardless of objective measures, can influence brain structure.

Choosing the Right SES Variables: A Note of Caution

When studying the impact of SES on the brain, it's essential to carefully select and utilize multiple independent SES measures to avoid oversimplification. Each variable provides a unique piece of the puzzle. However, researchers must be mindful of multicollinearity—a statistical phenomenon that can occur when variables are highly correlated. By carefully selecting variables, increasing sample size, and using appropriate statistical techniques, researchers can minimize the risk of misinterpreting their findings.

Finally, it's worth noting that many SES indicators used in research are based on studies conducted in Western, educated, industrialized, rich and democratic (WEIRD) countries. More research is needed to explore how these findings translate to other cultures and contexts.

Beyond SES: Other Factors That Matter

When studying the relationship between SES and brain structure, it's crucial to account for other important factors that can influence brain development.

Age: Our brains change dramatically throughout our lives, especially during childhood and adolescence. Different brain structures grow and mature at different rates. Therefore, comparing brain structure across different age groups without considering these developmental changes can lead to misleading conclusions. Studies that include a wide range of ages or take a lifespan approach are better equipped to capture these dynamic changes.

Sex: Significant differences in brain volume between males and females occur during puberty, and these differences also extend to cortical thickness. For instance, males tend to have a thicker cortex in certain frontal brain regions at younger ages, followed by greater cortical thinning during adolescence compared to females. These findings highlight the importance of considering sex as a factor when studying SES and brain development.

Timing and Duration of Disadvantage: Studies have shown that the timing and duration of poverty experienced by families can have varying effects on child development. While the brain is most adaptable during early childhood, it maintains plasticity throughout life. More research is needed to fully understand how the timing and duration of socioeconomic disadvantage influence brain development.

Mediating Factors: It's important to remember that SES is not a direct cause of brain differences. Instead, SES influences a range of experiences and environmental exposures that shape brain development. These mediating factors might include nutrition, exposure to environmental toxins, access to quality education, and the quality of parent-child interactions. For example, one study found that the relationship between income and hippocampal volume was mediated by factors like parenting quality and stressful life events. By identifying these mediating factors, researchers can develop more targeted interventions to address SES-related disparities in brain development.

Beyond Volume: Understanding Cortical Thickness and Surface Area

Many studies investigating SES and brain structure have focused on cortical volume, a measure of the overall size of different brain regions. However, cortical volume is a composite measure that combines cortical thickness and surface area, two distinct properties of the brain that develop independently and are influenced by different genetic and environmental factors.

KEY CONCEPT 5. Cortical Volume. What it means: Cortical volume is often used as a measure of brain structure. It represents the total size of a particular brain region. Importantly, it's a combination of two separate components: cortical thickness and surface area.

KEY CONCEPT 6. Surface Area. What it means: This refers to the total area of the brain's outer surface, including the visible folds (gyri) and grooves (sulci). Think of it as the total area of a crumpled-up piece of paper, rather than just its length and width.

Cortical surface area expands rapidly during childhood and early adolescence, while cortical thickness decreases during this time, reflecting processes like synaptic pruning. These changes are crucial for healthy brain development and cognitive function. Interestingly, research suggests that both cortical thickness and surface area are independently associated with intelligence. This highlights the importance of studying these two measures separately, rather than relying solely on cortical volume.

To gain a more complete understanding of how SES impacts brain development, future research should move beyond simply measuring cortical volume and instead investigate how SES relates to both cortical thickness and surface area.

Conclusion: Connecting the Dots Between SES and Brain Development

Children growing up in socioeconomic disadvantage often face an uphill battle, experiencing cognitive delays and emotional challenges.

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How Does Growing Up Poor Affect the Brain?

Growing up in different environments can affect how we learn and interact with others. Scientists have discovered that these differences can actually change the physical structure of the brain. By studying the brain, we can better understand how growing up in poverty impacts a child's development. This article explores the link between brain structure differences and poverty, focusing mainly on developing countries and avoiding extreme cases like severe abuse.

Key Concept 1: Socioeconomic Status (SES)

SES describes a person's access to money, resources, and opportunities, as well as their social standing. It's determined by factors like education, job, and income, and affects physical health, mental well-being, and cognitive development. Studies show that SES can explain about 20% of the differences in IQ scores among children, with children in chronic poverty potentially having IQ scores 6 to 13 points lower than their peers.

Key Concept 2: Poverty

Poverty is determined by comparing a household's income to a set level that changes based on family size and inflation. Children from disadvantaged backgrounds often receive less language exposure, social interaction, and cognitive stimulation. They also experience more stress, which can affect brain regions like the hippocampus, amygdala, and prefrontal cortex.

SES and Brain Development

SES influences brain development in various ways. For instance, higher income correlates with larger hippocampal volumes in children, while parental education levels impact the amount of cognitive stimulation a child receives at home. These differences in experiences affect brain areas associated with language and stress response.

Key Concept 3: Income-to-Needs

The Income-to-Needs ratio compares a family's total income to the poverty line for their family size. Higher income/ITN is linked to larger hippocampal volumes, while severe income lack might affect amygdala size.

Key Concept 4: Cortical Thickness

Cortical thickness refers to the distance between the brain's outer surface (gray matter) and inner white matter. It's an important measure in understanding brain development and cognitive abilities.

Conclusion

Growing up in poverty can lead to developmental delays and emotional difficulties. Understanding how socioeconomic factors affect brain development is crucial for developing effective interventions. Combining structural and functional brain imaging with cognitive assessments can help us better understand the complex relationships between brain anatomy, physiology, and behavior, ultimately informing policies to reduce socioeconomic disparities in child development.

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How Money and School Affect Your Brain

Things like a family's money situation and education can actually change how the brain grows. Scientists are studying this fascinating topic to understand the impact better.

IMPORTANT IDEA 1: Socioeconomic Status (SES)

Imagine a big box full of things that help people in life, like money, good jobs, and nice houses. That box represents socioeconomic status, or SES. People with higher SES have more of these helpful resources, while those with lower SES have less.

Scientists have found that kids from families with lower SES often struggle more in school. They might not learn as quickly or perform as well on tests. This could be due to fewer books at home or parents having less time to help with homework. These factors can influence how their brains develop, especially in areas crucial for learning and memory.

IMPORTANT IDEA 2: Poverty

When families lack sufficient money to buy essentials like food, clothing, or a safe place to live, it is called poverty. Living in poverty can be very stressful for children, and this stress can impact brain development.

Scientists are working hard to understand exactly how this happens. They use special machines to take pictures of children's brains and observe how they change over time. This research is vital because it can help find ways to give all kids the best chance to learn and grow.

How Scientists Study This

Scientists use various methods to study how SES affects the brain. They consider factors like family income, parents' education levels, and types of jobs they have.

Research has shown that different aspects of SES can impact different brain areas. For instance, a family's income might be linked to the size of the hippocampus, which is essential for memory. A parent's education, on the other hand, might be connected to the size of the amygdala, which helps in understanding and controlling emotions.

Conclusion

SES isn't the only factor affecting brain development. Other elements such as age, gender, and even geographic location can also play significant roles.

Scientists are still piecing together this complex puzzle. However, one thing is clear: the more we learn about how our brains grow and change, the better we can support all children in reaching their full potential.

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