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Article

Subjective Socioeconomic Status and Children’s Amygdala Volume: Minorities’ Diminish Returns

by
Shervin Assari
1,2,*,
Shanika Boyce
3 and
Mohsen Bazargan
1,4
1
Department of Family Medicine, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA
2
Department of Urban Public Health, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA
3
Department of Pediatrics, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA
4
Department of Family Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
NeuroSci 2020, 1(2), 59-74; https://doi.org/10.3390/neurosci1020006
Submission received: 16 August 2020 / Revised: 21 September 2020 / Accepted: 21 September 2020 / Published: 5 October 2020
Versions Notes

Abstract

Considerable research has suggested that low socioeconomic status (SES) negatively influences brain structure, including but not limited to decreased amygdala volume. Considering race and ethnicity as sociological rather than biological constructs, this study was built on minorities’ diminished returns (MDRs) to test if the effects of family SES on the total amygdala volume is weaker for black and Latino children than white and non-Latino children. We borrowed data from the Adolescent Brain Cognitive Development (ABCD) study, a national multi-center brain imaging investigation of childhood brain development in the US. The total sample was 9380 9–10-year-old children. The independent variables were subjective family SES and parental education. The primary outcome was total amygdala volume. High subjective SES and parental education were independently associated with larger total amygdala size. The association between high subjective SES and larger total amygdala volume was less pronounced for black and Latino children than white and non-Latino children. For American children, family SES has unequal effects on amygdala size and function, a pattern that is consistent with MDRs. This result suggests that SES loses some of its expected effects for racial and ethnic minority families.

1. Introduction

High socioeconomic status (SES) is associated with positive childhood emotions and behaviors [1]. For example, children from higher SES families are protected against school drop-out [2], depression [3], suicide [4,5], antisocial behaviors [6], aggression [7], and use of tobacco [8,9], alcohol [10,11], and drugs [12]. These behavioral effects of SES are mainly attributed to the protective effects of high SES on childhood development.
The scarcity hypothesis provides an explanation for why low SES deteriorates healthy children’s brain development. According to this hypothesis, low SES is a proxy of early adversity, stress, economic insecurity, and lack of resources, all operating as risk factors for poor child development. In this view, stress, adversity, and scarce resources explain the SES–brain development link [13]. Low family SES is a proxy of living in stressful environments, food insecurity, environmental toxins, and parental risk behaviors that can jeopardize healthy brain development in children [14,15,16]. As a result of unhealthy brain development, children from low SES are at an increased risk of various types of psychopathologies [17,18,19]. In contrast, children from high SES families experience lower levels of stress, have access to stimulating environments, receive better parenting, and have access to buffers when stress occurs [20,21,22].
According to the minorities’ diminished returns (MDRs) [23,24], however, racial groups may differ in the protective effect of high SES on brain development and function. Compared to white children, black American children show lower effects of family SES on developmental outcomes [25] such as school performance [25], mental health [26], emotion regulation [27,28], aggression [29], and substance use [26,30]. In a study, high-income black boys were more, not less, likely to be depressed [31]. Similar patterns are shown for black men [32]. In one study in blacks, the effect of discrimination on depression was larger at higher SES, suggesting that high SES may increase vulnerability to discrimination [33]. In many other studies among blacks, high SES was associated with increased proximity to whites [34,35], which increases their exposure to discrimination. Other studies have also shown a positive rather than inverse association between SES and discrimination, suggesting that high SES blacks experience more, not less, discrimination [32,34,35,36,37,38]. Similarly, the effects of neighborhood quality [39] and social contacts [40] are weaker for blacks than whites.
Research has established racial/ethnic differences in each SES indicator’s role in children’s brain development [20,41,42,43]. In several studies, the magnitude of the effects of family SES on a wide range of developmental and health outcomes is weaker for blacks and Latinos than whites and non-Latinos [32,36,44,45,46,47,48,49]. As a result of MDRs, middle-class ethnic minority children remain at risk for poor developmental and health outcomes [50,51,52,53,54]. For example, high SES black and Latino children remain at risk of anxiety [55], depression [31], poor health [45], poor school performance [56,57], as well as high-risk behaviors [54] such as aggression [54] and tobacco use [58,59]. Differential effects of SES across racial and ethnic groups of children are robust [44,45,46,60,61]. Data from the fragile families and child wellbeing study (FFCWS) shows that high parental education and family income is associated with better outcomes in impulsivity, school performance, school bonding, attention-deficit/hyperactivity disorder (ADHD), obesity, aggression, depression, and self-rated health for non-Latino and white children than Latino and black American children [52,61,62,63]. Subjective SES and parental education each impact brain imaging findings in a certain way [20,41,42,43]. Various SES indicators may also be the underlying mechanisms by which racial and ethnic disparities emerge in children’s development [44,45,64].
At least some of the effects of high SES on emotional and behavioral outcomes [1] can be attributed to the role of family SES on structure and function of the amygdala [20]. Across various brain structures, the amygdala has shown the most consistent pattern of association with SES [20,42,65]. While amygdala reactivity has a central role in emotion regulation, altered size and function of the amygdala is shown to predict high-risk behaviors such as aggression and substance use [66,67,68,69,70].
For at least three reasons, the intersections of race, SES, and amygdala structure was analyzed in this study. First, most of the literature is conducted mainly in white samples. For example, all studies by Javanbakht et al. were conducted exclusively or mainly in white individuals [20,42,65]. There is a need for brain imaging data from large, racially, and ethnically diverse samples to compare racial and ethnic groups. Most previous studies have used sample sizes of less than 100 or at most a couple of hundred participants [20,42,65]. Studies with large sample sizes are rare. As such, they are needed to compare diverse subsamples. The adolescents brain cognitive development (ABCD) study has a large sample size [71,72,73,74,75], which enhances the statistical power and generalizability of the results for comparison of black, white, Latino, and non-Latino groups for the effects of SES on children brain imaging. Thus, using large datasets, there is still a need for research on whether race and ethnicity change the functional and structural brain correlates of SES resources.
Second, there is a need to advance the current literature, where more information exists on the effects of family SES during childhood on brain structure in adulthood and less is known about the effects of childhood SES on childhood brain structure [20,41,42,43]. While we know that childhood poverty is a predictor of undesired brain changes during adulthood [20,41,42,43,76], less is known about the brain imaging finding of children while living under poverty. For example, all studies by Javanbakht investigated the effects of childhood family SES on adulthood brain function and structure [20,42,65]. Thus, we need to extend the research from the well demonstrated role of childhood SES in predicting adults’ neural circuits to the connection of childhood poverty to childhood brain development.
The third reason is a need for additional studies exploring the salience of family SES indicators other than income and poverty. There is particularly a need to study the effects of subjective SES and parental education on American children’s brain function [20,41,42,43,76]. Thus, we need more studies that compare the effects of subjective family SES on neural regulatory structures and functions across diverse groups [77].

Aims

To extend the existing knowledge on the complexities of social determinants of children’s brain development in the US, we explored racial and ethnic group variations in the effects of two family SES indicators, namely subjective family SES and parental education, on total amygdala volume among 9–10-year-old children. We expected to find racial and ethnic differences in the magnitude of the association between our family SES indicators and total amygdala volume, in line with the observed MDRs [23,24,54]. More specifically, we expected to find weaker SES effects on total amygdala volume for black and Latino than white and non-Latino children. This expectation is in line with the other research on a wide range of phenotypes and behaviors [23,24,54].

2. Materials and Methods

2.1. Design and Settings

We conducted a secondary analysis of the Adolescent Brain Cognitive Development (ABCD) study data [71,72,73,74,75]. With a cross-sectional design, we applied data from the baseline of the ABCD study. ABCD is a national, state-of-the-art brain imaging study of childhood brain development [71,78]. The advantages of the ABCD study include a national sample, a large sample size, a large sample of blacks and Latinos, available data, robust measures of brain development, and considerable socioeconomic factors [71,72,73,74,75].

2.2. Participants and Sampling

Participants of the ABCD study were selected across multiple cities across states. This ABCD sample was primarily recruited through school systems with sampling (school selection) informed by race, ethnicity, sex, SES, and urbanicity. More details of ABCD sampling are published elsewhere [79]. Inclusion criteria were being an either non-Latino, Latino, black, or white child between ages 9 and 10, and having valid data on total amygdala volume. We excluded any child from any other race/ethnicity (Asian, mixed, other).

2.3. Study Process

Structural magnetic resonance imaging (sMRI) data were used to measure total amygdala volume. As described in detail by Casey et al. (2018), participants completed a high-resolution T1-weighted structural MRI scan (1-mm isotropic voxels) using scanners from Philips Healthcare (Philips, Andover, Massachusetts, USA), GE Healthcare (General Electrics, Waukesha, WI, USA), or Siemens Healthcare (Siemens, Erlangen, Germany) [72]. All the structural MRI data were processed using FreeSurfer version 5.3.0, available at http://surfer.nmr.mgh.harvard.edu/ [80,81], according to standard processing pipelines [72]. Processing included removal of nonbrain tissue, segmentation of gray and white matter structures [82], and cortical parcellation [83]. All scan sessions underwent radiological review whereby scans with incidental findings were identified. Quality control for the structural images comprised visual inspection of T1 images and FreeSurfer outputs for quality [84]. Quality review was conducted by the ABCD team. Subjects whose scans failed inspection (due to severe artifacts or irregularities) were excluded. The Desikan–Killiany atlas was used for cortical parcellation [84]. Regions of interest included caudal middle frontal, lateral orbitofrontal, medial orbitofrontal, rostral middle frontal, superior frontal, and frontal pole. In this analysis, we used the volumetric data provided by the ABCD data.

2.4. Study Variables

The study variables included demographic factors, family SES indicators, and total amygdala volume. A detailed explanation of the procedures and harmonization of the structural MRI in the ABCD study is available here [72].

2.4.1. Primary Outcome

The primary outcome was total amygdala volume, measured by structural MRI. Amygdala volume is shown to be under the influence of exposure to poverty, trauma, and adversity [65,85,86].

2.4.2. Moderator

Race. Race, a self-identified and a dichotomous variable, was coded as 1 for black and 0 for white (reference).
Ethnicity. Parents were asked if they are of Latino ethnic background. This variable was coded as Latino = 1 and non-Latino = 0.
Independent Variable
Subjective Family SES. Subjective family SES in this study was financial difficulties measured by the following seven items: “In the past 12 months, has there been a time when you and your immediate family experienced any of the following:” (1) “Needed food but could not afford to buy it or could not afford to go out to get it?”, (2) “Were without telephone service because you could not afford it?” (3) “Did not pay the full amount of the rent or mortgage because you could not afford it?”, (4) “Were evicted from your home for not paying the rent or mortgage?”, (5) “Had services turned off by the gas or electric company, or the oil company would not deliver oil because payments were not made?”, (6) “Had someone who needed to see a doctor or go to the hospital but did not go because you could not afford it?”, and (7) “Had someone who needed a dentist but could not go because you could not afford it?” Responses to each item were either 0 or 1. We calculated a mean score with a potential range between 0 and 1—a higher score indicating higher subjective family SES. Our variable was a continuous measure [33,87,88,89,90,91,92].
Parental Educational Attainment. Participants reported their years of schooling. This variable was operationalized as a continuous (interval) variable ranging from 0 for no formal education to 21 doctoral degrees.

2.4.3. Confounders

Age. Age was a dichotomous variable coded 1 or 0 for 10 years and 9 years of age. Parents reported the age of the children.
Sex. Sex was 1 for males and 0 for females.
Parental marital status. Parental marital status was 1 for married and 0 for any other condition (reference).
Parental employment status. Parental employment status was 1 for employed and 0 for unemployed (reference).

2.5. Data Analysis

We used SPSS 22.00 (IBM, NY, USA) for data analysis. Frequencies (n and %) and mean (standard deviations (SDs)) were reported for descriptive purposes. To estimate bivariate analyses between the study variables, we used the Pearson correlation test in the pooled sample. To perform our multivariable analyses, we performed multiple linear regressions. The independent variable was the family SES. The outcome was the total amygdala volume. All these models controlled for all confounders. All models were performed in the pooled sample. Model 1 did not have interaction effects. Model 2 was performed with two interaction terms. For sensitivity analysis, we also ran models that included total intracranial volume as a covariate. Adding this variable did not alter our results of interaction. Thus, we reported the most parsimonious model (models without total intracranial volume as a covariate). Unstandardized regression coefficient (b), SE, and p-values were reported for each model. A p-value of equal or less than 0.05 was significant.

2.6. Ethical Aspect

Our analysis was exempt from a full review. The ABCD study protocol, however, was approved by the University of California, San Diego (UCSD) Institutional Review Board (IRB) [78].

3. Results

3.1. Descriptives

The sample included 9380 9–10-year-old children. Table 1 presents the descriptive statistics of the pooled sample.

3.2. Unadjusted Bivariate Correlations

Table 2 presents the results of the unadjusted bivariate correlations based on the Spearman test. Family SES is positively correlated with the total amygdala volume.

3.3. Pooled-Sample Associations

Table 3 reports the results of two pooled sample regression models. Model 1, which only included the main effects, showed that high subjective family SES and parental education were associated with total amygdala volume. Model 2 showed that subjective family socioeconomic status (SES) and race and ethnicity interact, meaning that the effects were less pronounced for black and Latino than white and non-Latino children. These interactions were statistically significant. Parental education did not show a statistically significant interaction between race and ethnicity.

3.4. Stratified Associations

Table 4 reports the results of four regression models by race. Model 3, which was conducted in whites, showed that high subjective family SES and parental education were associated with total amygdala volume. Model 4, which was conducted in blacks, did not show an effect of subjective family SES or parental education on total amygdala volume.

3.5. Stratified Associations

Table 5 reports the results of four regression models by ethnicity. Model 5, which was conducted in non-Latinos, showed that high subjective family SES and parental education were associated with total amygdala volume. Model 6, which was conducted in Latinos, showed an effect of parental education, but not subjective family SES on total amygdala volume.

4. Discussion

This study had two primary findings. Although higher subjective family socioeconomic status (SES) and parental education predicted a larger volume of the amygdala (first finding), subjective family SES showed a stronger effect for white and non-Latino than black and Latino children (second finding).
Our first results can be compared with the literature on the effects of SES and adversity on amygdala volumes. These effects may be because stress, adversities, trauma, maternal depression, and SES impact children’s amygdala volume [65,85,86,93,94,95,96,97,98,99,100]. There are multiple studies that have documented enlarged amygdala volumes in children who have been raised in an orphanage [98], have had maternal depression [101]), or have had infant attachment insecurity [102]. However, in our study, high SES was associated with larger amygdala size. Our study finding is more in line with the results of studies showing smaller amygdala size in low SES children. For example, Merz et al. studied 1196 children and adolescents who were between ages of 3 and 21. These individuals were selected from the pediatric imaging, neurocognition, and genetics study and showed that lower family income and parental education are significantly associated with smaller amygdala volume in adolescents (13–21 years) but not in younger children (3–12 years) [86].
While our first finding documented a link between SES and total amygdala volume, most of the literature on the SES–amygdala relationship has focused on amygdala function rather than structure. For example, three studies by Javanbakht have linked high family SES to amygdala response to negative facial expressions. While social adversities show cumulative (additive) effects on amygdala structure and function (including amygdala response to threatening faces) [65], these effects may be more pronounced for females than males [42]. Childhood poverty is also linked to a reduced functional connectivity between the amygdala and the medial prefrontal cortex [20]. Brody et al. also documented a link between poverty and a reduced connectivity in neural networks involved in emotion regulation (e.g., amygdala) [103]. Barch et al. documented a link between childhood poverty and reduced connectivity between the amygdala and hippocampus with brain regions including the lingual gyrus, superior frontal cortex, posterior cingulate, as well as putamen [104]. The literature has also shown the effects of family SES indicators such as poverty and household income on the brain [77] and behavior [20,41,42,43,76].
Our second results can be explained through two mechanisms. First, there is high exposure to racial and ethnic discrimination in high SES black and Latino families. Racial and ethnic discrimination affect the amygdala’s structure and function [105,106,107,108,109,110]. In a study, discrimination was associated with increased connectivity with multiple brain regions. In the presence of racial discrimination, the amygdala shows a more robust connection with the thalamus (place reference here). Similarly, chronic discrimination increases connections between the amygdala and the putamen, anterior insula, the caudate, medial frontal gyrus, and anterior cingulate [105]. As high SES, particularly high subjective SES, is a proxy of high not low discrimination [32,33,34,35,36,37,38,111,112], high SES black American children still report lower than expected amygdala sizes, because of the effect of discrimination.
Our second finding can be also seen as a reflection of the MDRs. Many studies have shown more significant effects of SES on outcomes for white than black American children [44,45,113]. For example, family SES has shown larger effects on ADHD [63], anxiety [55], aggression [54], tobacco dependence [54], school bonding [114], school performance [56,57], obesity [52], and health [51] for white than black American children. Family SES has also shown a more salient role in shaping the impulsivity of white than black American children [50]. As a result of this pattern, higher than expected risk of poor self-rated health, obesity, poor mental health, chronic disease, impulsivity, aggression, smoking, and low school performance are observed in high SES black American children [52,61,63]. The higher than expected risks for high SES black American children seem to be robust as they hold across SES indicators, outcomes, population groups, birth cohorts, age groups, and settings [23,24].
As shown by this study and previous work [23], family SES may differently influence the outcomes of black and white children [60,115], adolescents [116], adults [117], and older adults [118,119]. In an unequal society, not only parental education [54] but also their own educational attainment [58,62,120], employment [111], marital status [46], and even coping style [121,122] generates unequal outcomes for blacks and whites. Regardless of their types, SES resources seem to always generate unequal effects for blacks and whites, a pattern which may be indicative of social stratification, segregation, and deeply rooted societal inequalities [123,124,125,126,127,128,129,130,131,132].
Differential effects of family SES indicators for black and white families contribute to the transgenerational transmission of inequalities [50,51,52,53,54]. Differential effects of SES mean that the same level of SES may generate unequal outcomes for the next generation, which results in the reproduction of inequalities across generations. However, most of the previous studies on MDRs have relied on self-reported outcomes. Thus, the evidence lacked biological studies that test the differential effects of SES on children’s brain imaging. This paper extends the existing literature by testing such patterns on brain development.
The observed MDRs suggest that Latino and black American children are at two jeopardies. The first risk is that they live in low SES families. The second risk is that their SES shows a weaker impact on their brain development. The weakened effect of high SES for black and Latino children may be due to other unique stressors in the lives of racial and ethnic minorities across all SES levels.
There is a need for an enhanced understanding of the role of the amygdala in explaining why high SES shows a more significant impact on children’s behavioral and emotional outcomes of white and non-Latino than black and Latino families. Without such knowledge, our policymakers may fall into a false thinking trap that suggests equalizing SES may be enough to equalize the brain function of racial and ethnic groups. However, past research has shown that high SES may still be associated with some residual adversities for non-white families. For example, high SES black families remain in poor neighborhoods, experience high levels of stress, have a poor diet, are more obese, have higher risk social networks, and are sent to high-risk schools (add references here). As a result, despite family-level SES, black and Latino children remain at risk of poor developmental outcomes across multiple emotional and behavioral domains.
It should be emphasized again that we see race and ethnicity as social factors (as proxy of social status, treatment by society, access to opportunity structure, interpersonal discrimination, environmental injustices, societal obstacles, and historical injustice) on how the individual is treated by society. Across various brain mechanisms, we focused on the amygdala, which is highly involved in emotion regulation, emotion expression, social relations, aggression, and impulsivity. An alteration of the amygdala volume and function is expected to be involved in many outcomes across various domains. As our results suggested, race and ethnicity alter the implications of family SES for amygdala structure and function, which are at the core of and salient to brain functions across domains.

5. Conclusions

In summary, in a large national sample of American children, high subjective family SES correlates with larger amygdala volume. However, this effect is unequal across racial and ethnic groups with the marginal return of SES being smaller for racial and ethnic minority families than non-Latino and white people. Policy solutions that wish to achieve equality should go beyond SES inequalities and be aware that SES generates unequal outcomes across racial and ethnic groups. More research is needed on implications of the observations that black, white, Latino, and non-Latino children may show different associations between SES and brain development. The influence of social determinants on children’s brain development is complex and multiplicative rather than simple and additive.

Author Contributions

Conceptualization, S.A. software, S.A.; formal analysis, S.A.; writing—original draft preparation, S.A.; writing—review and editing, S.A., M.B., S.B.; supervision, S.A. All authors have read and agreed to the published version of the manuscript.

Funding

The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/Consortium_Members.pdf. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The current paper used the Curated Annual Release 2.0, also defined in NDA Study 634 (doi:10.15154/1503209). Assari is supported by the following NIH grants: 2U54MD007598, U54 TR001627; CA201415-02, 5S21MD000103, R25 MD007610, 4P60MD006923, and 54MD008149.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Oshri, A.; Hallowell, E.; Liu, S.; MacKillop, J.; Galvan, A.; Kogan, S.M.; Sweet, L.H. Socioeconomic hardship and delayed reward discounting: Associations with working memory and emotional reactivity. Dev. Cogn. Neurosci. 2019, 37, 100642. [Google Scholar] [CrossRef] [PubMed]
  2. Sirin, S.R. Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Rev. Educ. Res. 2005, 75, 417–453. [Google Scholar] [CrossRef]
  3. Mendelson, T.; Kubzansky, L.D.; Datta, G.D.; Buka, S.L. Relation of female gender and low socioeconomic status to internalizing symptoms among adolescents: A case of double jeopardy? Soc. Sci. Med. 2008, 66, 1284–1296. [Google Scholar] [CrossRef] [PubMed]
  4. Yildiz, M.; Demirhan, E.; Gurbuz, S. Contextual Socioeconomic Disadvantage and Adolescent Suicide Attempts: A Multilevel Investigation. J. Youth Adolesc. 2018, 48, 802–814. [Google Scholar] [CrossRef]
  5. Eisenberg, D.; Gollust, S.E.; Golberstein, E.; Hefner, J. Prevalence and correlates of depression, anxiety, and suicidality among university students. Am. J. Orthopsychiatry 2007, 77, 534–542. [Google Scholar] [CrossRef]
  6. Palma-Coca, O.; Hernández-Serrato, M.I.; Villalobos-Hernández, A.; Unikel-Santoncini, C.; Olaiz-Fernández, G.; Bojorquez-Chapela, I. Association of Socioeconomic Status, Problem Behaviors, and Disordered Eating in Mexican Adolescents: Results of the Mexican National Health and Nutrition Survey 2006. J. Adolesc. Health 2011, 49, 400–406. [Google Scholar] [CrossRef]
  7. Heshmat, R.; Qorbani, M.; Ghoreshi, B.; Djalalinia, S.; Tabatabaie, O.R.; Safiri, S.; Noroozi, M.; Motlagh, M.E.; Ahadi, Z.; Asayesh, H.; et al. Association of socioeconomic status with psychiatric problems and violent behaviours in a nationally representative sample of Iranian children and adolescents: The CASPIAN-IV study. BMJ Open 2016, 6, e011615. [Google Scholar] [CrossRef]
  8. Kaleta, D.; Usidame, B.; Dziankowska-Zaborszczyk, E.; Makowiec-Dąbrowska, T. Socioeconomic Disparities in Age of Initiation and Ever Tobacco Smoking: Findings from Romania. Central Eur. J. Public Health 2015, 23, 299–305. [Google Scholar] [CrossRef]
  9. Barreto, S.M.; De Figueiredo, R.C.; Giatti, L. Socioeconomic inequalities in youth smoking in Brazil. BMJ Open 2013, 3, e003538. [Google Scholar] [CrossRef]
  10. Moore, L.; Littlecott, H.J. School- and Family-Level Socioeconomic Status and Health Behaviors: Multilevel Analysis of a National Survey in Wales, United Kingdom. J. Sch. Health 2015, 85, 267–275. [Google Scholar] [CrossRef]
  11. Silveira, C.M.; Siu, E.R.; Anthony, J.C.; Saito, L.P.; De Andrade, A.G.; Kutschenko, A.; Viana, M.C.; Wang, Y.-P.; Martins, S.S.; Andrade, L.H. Drinking Patterns and Alcohol Use Disorders in São Paulo, Brazil: The Role of Neighborhood Social Deprivation and Socioeconomic Status. PLoS ONE 2014, 9, e108355. [Google Scholar] [CrossRef] [PubMed]
  12. Gerra, G.; Benedetti, E.; Resce, G.; Potente, R.; Cutilli, A.; Molinaro, S. Socioeconomic Status, Parental Education, School Connectedness and Individual Socio-Cultural Resources in Vulnerability for Drug Use among Students. Int. J. Environ. Res. Public Health 2020, 17, 1306. [Google Scholar] [CrossRef] [PubMed]
  13. A Yaple, Z.; Yu, R. Functional and Structural Brain Correlates of Socioeconomic Status. Cereb. Cortex 2019, 30, 181–196. [Google Scholar] [CrossRef] [PubMed]
  14. Assari, S.; Bazargan, M. Unequal Associations between Educational Attainment and Occupational Stress across Racial and Ethnic Groups. Int. J. Environ. Res. Public Health 2019, 16, 3539. [Google Scholar] [CrossRef] [PubMed]
  15. Assari S, B.M. Second-hand exposure home Second-Hand Smoke Exposure at Home in the United States; Minorities’ Diminished Returns. Int J Travel Med Glob Health 2019, 7, 135–141. [Google Scholar] [CrossRef]
  16. Assari, S.; Bazargan, M. Unequal Effects of Educational Attainment on Workplace Exposure to Second-Hand Smoke by Race and Ethnicity; Minorities’ Diminished Returns in the National Health Interview Survey (NHIS). J. Med. Res. Innov. 2019, 3, e000179. [Google Scholar] [CrossRef]
  17. Chassin, L.; Presson, C.C.; Sherman, S.J.; Edwards, D.A. Parent educational attainment and adolescent cigarette smoking. J. Subst. Abus. 1992, 4, 219–234. [Google Scholar] [CrossRef]
  18. Kocaoglu, B.; Moschonis, G.; Dimitriou, M.; Kolotourou, M.; Keskin, Y.; Sur, H.; Hayran, O.; Manios, Y. Parental educational level and cardiovascular disease risk factors in schoolchildren in large urban areas of Turkey: Directions for public health policy. BMC Public Health 2005, 5, 13. [Google Scholar] [CrossRef]
  19. Padilla-Moledo, C.; Ruiz, J.R.; Castro-Piñero, J. Parental educational level and psychological positive health and health complaints in Spanish children and adolescents. Child Care Health Dev. 2016, 42, 534–543. [Google Scholar] [CrossRef]
  20. Javanbakht, A.; King, A.P.; Evans, G.W.; Swain, J.E.; Angstadt, M.; Phan, K.L.; Liberzon, I. Childhood Poverty Predicts Adult Amygdala and Frontal Activity and Connectivity in Response to Emotional Faces. Front. Behav. Neurosci. 2015, 9, 154. [Google Scholar] [CrossRef]
  21. Masten, C.L.; Telzer, E.H.; Eisenberger, N.I. An fMRI Investigation of Attributing Negative Social Treatment to Racial Discrimination. J. Cogn. Neurosci. 2011, 23, 1042–1051. [Google Scholar] [CrossRef] [PubMed]
  22. Wu, X.; Zou, Q.; Hu, J.; Tang, W.; Mao, Y.; Gao, L.; Zhu, J.; Jin, Y.; Wu, X.; Lu, L.; et al. Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury. J. Neurosci. 2015, 35, 12932–12946. [Google Scholar] [CrossRef] [PubMed]
  23. Assari, S. Unequal Gain of Equal Resources across Racial Groups. Int. J. Health Policy Manag. 2017, 7, 1–9. [Google Scholar] [CrossRef] [PubMed]
  24. Assari, S. Health Disparities due to Diminished Return among Black Americans: Public Policy Solutions. Soc. Issues Policy Rev. 2018, 12, 112–145. [Google Scholar] [CrossRef]
  25. Spera, C.; Wentzel, K.R.; Matto, H.C. Parental Aspirations for Their Children’s Educational Attainment: Relations to Ethnicity, Parental Education, Children’s Academic Performance, and Parental Perceptions of School Climate. J. Youth Adolesc. 2008, 38, 1140–1152. [Google Scholar] [CrossRef]
  26. Goodman, E.; Slap, G.B.; Huang, B. The Public Health Impact of Socioeconomic Status on Adolescent Depression and Obesity. Am. J. Public Health 2003, 93, 1844–1850. [Google Scholar] [CrossRef]
  27. Morris, A.S.; Silk, J.S.; Steinberg, L.; Myers, S.S.; Robinson, L.R. The Role of the Family Context in the Development of Emotion Regulation. Soc. Dev. 2007, 16, 361–388. [Google Scholar] [CrossRef]
  28. Park, S.; Holloway, S.D. No Parent Left Behind: Predicting Parental Involvement in Adolescents’ Education Within a Sociodemographically Diverse Population. J. Educ. Res. 2013, 106, 105–119. [Google Scholar] [CrossRef]
  29. Pabayo, R.; Molnar, B.E.; Kawachi, I. The Role of Neighborhood Income Inequality in Adolescent Aggression and Violence. J. Adolesc. Health 2014, 55, 571–579. [Google Scholar] [CrossRef]
  30. Wills, T.A.; McNamara, G.; Vaccaro, D. Parental education related to adolescent stress-coping and substance use: Development of a mediational model. Health Psychol. 1995, 14, 464–478. [Google Scholar] [CrossRef]
  31. Assari, S.; Caldwell, C.H. High Risk of Depression in High-Income African American Boys. J. Racial Ethn. Health Disparities 2017, 5, 808–819. [Google Scholar] [CrossRef] [PubMed]
  32. Assari, S.; Lankarani, M.M.; Caldwell, C.H. Does Discrimination Explain High Risk of Depression among High-Income African American Men? Behav. Sci. 2018, 8, 40. [Google Scholar] [CrossRef] [PubMed]
  33. Assari, S.; Preiser, B.; Lankarani, M.M.; Caldwell, C.H. Subjective Socioeconomic Status Moderates the Association between Discrimination and Depression in African American Youth. Brain Sci. 2018, 8, 71. [Google Scholar] [CrossRef] [PubMed]
  34. Assari, S. Does School Racial Composition Explain Why High Income Black Youth Perceive More Discrimination? A Gender Analysis. Brain Sci. 2018, 8, 140. [Google Scholar] [CrossRef]
  35. Assari, S.; Lankarani, M.M. Workplace Racial Composition Explains High Perceived Discrimination of High Socioeconomic Status African American Men. Brain Sci. 2018, 8, 139. [Google Scholar] [CrossRef]
  36. Hudson, D.; Bullard, K.M.; Neighbors, H.W.; Geronimus, A.T.; Yang, J.; Jackson, J.S. Are benefits conferred with greater socioeconomic position undermined by racial discrimination among African American men? J. Men’s Health 2012, 9, 127–136. [Google Scholar] [CrossRef]
  37. Hudson, D.; Puterman, E.; Bibbins-Domingo, K.; Matthews, K.A.; Adler, N. Race, life course socioeconomic position, racial discrimination, depressive symptoms and self-rated health. Soc. Sci. Med. 2013, 97, 7–14. [Google Scholar] [CrossRef]
  38. Hudson, D.; Neighbors, H.W.; Geronimus, A.T.; Jackson, J.S. Racial Discrimination, John Henryism, and Depression Among African Americans. J. Black Psychol. 2015, 42, 221–243. [Google Scholar] [CrossRef]
  39. Assari, S. Perceived Neighborhood Safety Better Predicts Risk of Mortality for Whites than Blacks. J. Racial Ethn. Health Disparities 2016, 4, 937–948. [Google Scholar] [CrossRef]
  40. Assari, S. Whites but Not Blacks Gain Life Expectancy from Social Contacts. Behav. Sci. 2017, 7, 68. [Google Scholar] [CrossRef]
  41. D’Angiulli, A.; Lipina, S.J.; Olesinska, A. Explicit and implicit issues in the developmental cognitive neuroscience of social inequality. Front. Hum. Neurosci. 2012, 6, 254. [Google Scholar] [CrossRef] [PubMed]
  42. Javanbakht, A.; Kim, P.; Swain, J.E.; Evans, G.W.; Phan, K.L.; Liberzon, I. Sex-Specific Effects of Childhood Poverty on Neurocircuitry of Processing of Emotional Cues: A Neuroimaging Study. Behav. Sci. 2016, 6, 28. [Google Scholar] [CrossRef] [PubMed]
  43. Kim, P.; Evans, G.W.; Angstadt, M.; Ho, S.S.; Sripada, C.S.; Swain, J.E.; Liberzon, I.; Phan, K.L. Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proc. Natl. Acad. Sci. USA 2013, 110, 18442–18447. [Google Scholar] [CrossRef] [PubMed]
  44. Assari, S. Parental Education Attainment and Educational Upward Mobility; Role of Race and Gender. Behav. Sci. 2018, 8, 107. [Google Scholar] [CrossRef] [PubMed]
  45. Assari, S. Parental Educational Attainment and Mental Well-Being of College Students: Diminished Returns of Blacks. Brain Sci. 2018, 8, 193. [Google Scholar] [CrossRef]
  46. Assari, S. Race, Intergenerational Social Mobility and Stressful Life Events. Behav. Sci. 2018, 8, 86. [Google Scholar] [CrossRef]
  47. Fuller-Rowell, T.E.; Doan, S.N. The social costs of academic success across ethnic groups. Child Dev. 2010, 81, 1696–1713. [Google Scholar] [CrossRef]
  48. Fuller-Rowell, T.E.; Curtis, D.S.; Doan, S.N.; Coe, C.L. Racial Disparities in the Health Benefits of Educational Attainment. Psychosom. Med. 2015, 77, 33–40. [Google Scholar] [CrossRef]
  49. Hudson, D.; Neighbors, H.W.; Geronimus, A.T.; Jackson, J.S. The relationship between socioeconomic position and depression among a US nationally representative sample of African Americans. Soc. Psychiatry Psychiatr. Epidemiol. 2011, 47, 373–381. [Google Scholar] [CrossRef]
  50. Assari, S.; Caldwell, C.H.; Mincy, R.B. Family Socioeconomic Status at Birth and Youth Impulsivity at Age 15; Blacks’ Diminished Return. Children 2018, 5, 58. [Google Scholar] [CrossRef]
  51. Assari, S.; Caldwell, C.H.; Mincy, R.B. Maternal Educational Attainment at Birth Promotes Future Self-Rated Health of White but Not Black Youth: A 15-Year Cohort of a National Sample. J. Clin. Med. 2018, 7, 93. [Google Scholar] [CrossRef] [PubMed]
  52. Assari, S.; Thomas, A.; Caldwell, C.H.; Mincy, R.B. Blacks’ Diminished Health Return of Family Structure and Socioeconomic Status; 15 Years of Follow-up of a National Urban Sample of Youth. J. Hered. 2017, 95, 21–35. [Google Scholar] [CrossRef] [PubMed]
  53. Assari, S.; Bazargan, M.; Bazargan, M.; Mincy, R.B.; Caldwell, C.H. Unequal Protective Effects of Parental Educational Attainment on the Body Mass Index of Black and White Youth. Int. J. Environ. Res. Public Health 2019, 16, 3641. [Google Scholar] [CrossRef] [PubMed]
  54. Assari, S.; Caldwell, C.H.; Bazargan, M. Association Between Parental Educational Attainment and Youth Outcomes and Role of Race/Ethnicity. JAMA Netw. Open 2019, 2, e1916018. [Google Scholar] [CrossRef]
  55. Assari, S.; Caldwell, C.H.; Zimmerman, M.A. Family Structure and Subsequent Anxiety Symptoms; Minorities’ Diminished Return. Brain Sci. 2018, 8, 97. [Google Scholar] [CrossRef]
  56. Assari, S. Parental Educational Attainment and Academic Performance of American College Students; Blacks’ Diminished Returns. J. Health Econ. Dev. 2019, 1, 21–31. [Google Scholar]
  57. Assari, S.; Caldwell, C.H. Parental Educational Attainment Differentially Boosts School Performance of American Adolescents; Minorities’ Diminished Returns. J. Fam. Reprod. Health 2019, 13, 7–13. [Google Scholar] [CrossRef]
  58. Assari, S.; Mistry, R. Educational Attainment and Smoking Status in a National Sample of American Adults; Evidence for the Blacks’ Diminished Return. Int. J. Environ. Res. Public Health 2018, 15, 763. [Google Scholar] [CrossRef]
  59. Assari, S.; Mistry, R.; Bazargan, M. Race, Educational Attainment, and E-Cigarette Use. J. Med. Res. Innov. 2019, 4, e000185. [Google Scholar] [CrossRef]
  60. Assari, S.; Lankarani, M.M. Poverty Status and Childhood Asthma in White and Black Families: National Survey of Children’s Health. Health 2018, 6, 62. [Google Scholar] [CrossRef]
  61. Assari, S. Multiplicative Effects of Social and Psychological Risk Factors on College Students’ Suicidal Behaviors. Brain Sci. 2018, 8, 91. [Google Scholar] [CrossRef] [PubMed]
  62. Assari, S. Socioeconomic Status and Self-Rated Oral Health; Diminished Return among Hispanic Whites. Dent. J. 2018, 6, 11. [Google Scholar] [CrossRef] [PubMed]
  63. Assari, S.; Caldwell, C.H. Family Income at Birth and Risk of Attention Deficit Hyperactivity Disorder at Age 15: Racial Differences. Children 2019, 6, 10. [Google Scholar] [CrossRef] [PubMed]
  64. Assari, S. Parental Education Better Helps White than Black Families Escape Poverty: National Survey of Children’s Health. Economies 2018, 6, 30. [Google Scholar] [CrossRef]
  65. Evans, G.W.; Swain, J.E.; King, A.P.; Wang, X.; Javanbakht, A.; Ho, S.S.; Angstadt, M.; Phan, K.L.; Xie, H.; Liberzon, I. Childhood Cumulative Risk Exposure and Adult Amygdala Volume and Function. J. Neurosci. Res. 2015, 94, 535–543. [Google Scholar] [CrossRef] [PubMed]
  66. Dotterer, H.L.; Hyde, L.W.; Swartz, J.R.; Hariri, A.R.; Williamson, U.E. Amygdala reactivity predicts adolescent antisocial behavior but not callous-unemotional traits. Dev. Cogn. Neurosci. 2017, 24, 84–92. [Google Scholar] [CrossRef] [PubMed]
  67. Gard, A.M.; Waller, R.; Swartz, J.R.; Shaw, D.S.; Forbes, E.E.; Hyde, L.W. Amygdala functional connectivity during socioemotional processing prospectively predicts increases in internalizing symptoms in a sample of low-income, urban, young men. NeuroImage 2018, 178, 562–573. [Google Scholar] [CrossRef]
  68. Morawetz, C.; Bode, S.; Baudewig, J.; Heekeren, H.R. Effective amygdala-prefrontal connectivity predicts individual differences in successful emotion regulation. Soc. Cogn. Affect. Neurosci. 2016, 12, 569–585. [Google Scholar] [CrossRef]
  69. Szczepanik, J.E.; Nugent, A.C.; Drevets, W.C.; Khanna, A.; Zarate, C.A.; Furey, M.L. Amygdala response to explicit sad face stimuli at baseline predicts antidepressant treatment response to scopolamine in major depressive disorder. Psychiatry Res. Neuroimaging 2016, 254, 67–73. [Google Scholar] [CrossRef]
  70. Venta, A.; Sharp, C.; Patriquin, M.; Salas, R.; Newlin, E.; Curtis, K.; Baldwin, P.; Fowler, C.; Frueh, B.C. Amygdala-frontal connectivity predicts internalizing symptom recovery among inpatient adolescents. J. Affect. Disord. 2018, 225, 453–459. [Google Scholar] [CrossRef]
  71. NIH’s Adolescent Brain Cognitive Development (ABCD) Study; National Institute on Alcohol Abuse and Alcoholism, Alcohol Research: Current Reviews: Bethseda, MD, USA, 2018; Volume 39, p. 97.
  72. Casey, B.; Cannonier, T.; Conley, M.I.; Cohen, A.O.; Barch, D.M.; Heitzeg, M.M.; Soules, M.E.; Teslovich, T.; Dellarco, D.V.; Garavan, H.; et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 2018, 32, 43–54. [Google Scholar] [CrossRef] [PubMed]
  73. Karcher, N.R.; O’Brien, K.J.; Kandala, S.; Barch, D.M. Resting-State Functional Connectivity and Psychotic-like Experiences in Childhood: Results From the Adolescent Brain Cognitive Development Study. Biol. Psychiatry 2019, 86, 7–15. [Google Scholar] [CrossRef] [PubMed]
  74. Lisdahl, K.M.; Sher, K.J.; Conway, K.P.; Gonzalez, R.; Ewing, S.W.F.; Nixon, S.J.; Tapert, S.; Bartsch, H.; Goldstein, R.Z.; Heitzeg, M. Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods. Dev. Cogn. Neurosci. 2018, 32, 80–96. [Google Scholar] [CrossRef] [PubMed]
  75. Luciana, M.; Bjork, J.M.; Nagel, B.J.; Barch, D.; González, R.; Nixon, S.J.; Banich, M.T. Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery. Dev. Cogn. Neurosci. 2018, 32, 67–79. [Google Scholar] [CrossRef]
  76. Silverman, M.E.; Muennig, P.; Liu, X.; Rosen, Z.; A Goldstein, M. The Impact of Socioeconomic Status on the Neural Substrates Associated with Pleasure. Open Neuroimaging J. 2009, 3, 58–63. [Google Scholar] [CrossRef]
  77. Finn, A.S.; Minas, J.E.; Leonard, J.A.; Mackey, A.P.; Salvatore, J.; Goetz, C.; West, M.R.; Gabrieli, C.F.; Gabrieli, J.D. Functional brain organization of working memory in adolescents varies in relation to family income and academic achievement. Dev. Sci. 2016, 20, e12450. [Google Scholar] [CrossRef]
  78. Auchter, A.M.; Mejia, M.H.; Heyser, C.J.; Shilling, P.D.; Jernigan, T.L.; Brown, S.A.; Tapert, S.F.; Dowling, G.J. A description of the ABCD organizational structure and communication framework. Dev. Cogn. Neurosci. 2018, 32, 8–15. [Google Scholar] [CrossRef]
  79. Garavan, H.; Bartsch, H.; Conway, K.; DeCastro, A.; Goldstein, R.; Heeringa, S.; Jernigan, T.; Potter, A.; Thompson, W.; Zahs, D. Recruiting the ABCD sample: Design considerations and procedures. Dev. Cogn. Neurosci. 2018, 32, 16–22. [Google Scholar] [CrossRef]
  80. Vargas, T.; Damme, K.S.F.; Mittal, V.A. Neighborhood deprivation, prefrontal morphology and neurocognition in late childhood to early adolescence. NeuroImage 2020, 220, 117086. [Google Scholar] [CrossRef]
  81. Fischl, B.; Sereno, M.I.; Dale, A.M. Cortical Surface-Based Analysis. NeuroImage 1999, 9, 195–207. [Google Scholar] [CrossRef]
  82. Fischl, B.; Salat, D.H.; Busa, E.; Albert, M.; Dieterich, M.; Haselgrove, C.; Van Der Kouwe, A.; Killiany, R.; Kennedy, D.; Klaveness, S.; et al. Whole Brain Segmentation. Neuron 2002, 33, 341–355. [Google Scholar] [CrossRef]
  83. Fischl, B.; Van Der Kouwe, A.; Destrieux, C.; Halgren, E.; Ségonne, F.; Salat, D.H.; Busa, E.; Seidman, L.J.; Goldstein, J.; Kennedy, D.; et al. Automatically parcellating the human cerebral cortex. Cereb. Cortex 2004, 14, 11–22. [Google Scholar] [CrossRef] [PubMed]
  84. Hagler, D.J.; Hatton, S.; Cornejo, M.D.; Makowski, C.; Fair, D.A.; Dick, A.S.; Sutherland, M.T.; Casey, B.; Barch, D.M.; Harms, M.P.; et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage 2019, 202, 116091. [Google Scholar] [CrossRef] [PubMed]
  85. Calem, M.; Bromis, K.; McGuire, P.; Morgan, C.; Kempton, M.J. Meta-analysis of associations between childhood adversity and hippocampus and amygdala volume in non-clinical and general population samples. NeuroImage Clin. 2017, 14, 471–479. [Google Scholar] [CrossRef]
  86. Merz, E.C.; Tottenham, N.; Noble, K.G. Socioeconomic Status, Amygdala Volume, and Internalizing Symptoms in Children and Adolescents. J. Clin. Child Adolesc. Psychol. 2017, 47, 312–323. [Google Scholar] [CrossRef]
  87. Assari, S.; Smith, J.; Mistry, R.; Farokhnia, M.; Bazargan, M. Substance Use among Economically Disadvantaged African American Older Adults; Objective and Subjective Socioeconomic Status. Int. J. Environ. Res. Public Health 2019, 16, 1826. [Google Scholar] [CrossRef]
  88. Chen, E.; Paterson, L.Q. Neighborhood, family, and subjective socioeconomic status: How do they relate to adolescent health? Health Psychol. 2006, 25, 704–714. [Google Scholar] [CrossRef]
  89. Moon, C. Subjective economic status, sex role attitudes, fertility, and mother’s work. Ingu Pogon Nonjip J. Popul. Heal. Stud. 1987, 7, 177–196. [Google Scholar]
  90. Bøe, T.; Petrie, K.J.; Sivertsen, B.; Hysing, M. Interplay of subjective and objective economic well-being on the mental health of Norwegian adolescents. SSM Popul. Health 2019, 9, 100471. [Google Scholar] [CrossRef]
  91. Wright, C.E.; Steptoe, A. Subjective socioeconomic position, gender and cortisol responses to waking in an elderly population. Psychoneuroendocrinology 2005, 30, 582–590. [Google Scholar] [CrossRef]
  92. Ye, Z.; Wen, M.; Wang, W.; Lin, D. Subjective family socio-economic status, school social capital, and positive youth development among young adolescents in China: A multiple mediation model. Int. J. Psychol. 2019, 55, 173–181. [Google Scholar] [CrossRef] [PubMed]
  93. Gianaros, P.J.; Sheu, L.K.; Matthews, K.A.; Jennings, J.R.; Manuck, S.B.; Hariri, A.R. Individual differences in stressor-evoked blood pressure reactivity vary with activation, volume, and functional connectivity of the amygdala. J. Neurosci. 2008, 28, 990–999. [Google Scholar] [CrossRef] [PubMed]
  94. Gilliam, M.; Forbes, E.E.; Gianaros, P.J.; Erickson, K.I.; Brennan, L.M.; Shaw, D.S. Maternal depression in childhood and aggression in young adulthood: Evidence for mediation by offspring amygdala-hippocampal volume ratio. J. Child Psychol. Psychiatry 2014, 56, 1083–1091. [Google Scholar] [CrossRef] [PubMed]
  95. A Morey, R.; Haswell, C.C.; Hooper, S.R.; De Bellis, M.D. Amygdala, Hippocampus, and Ventral Medial Prefrontal Cortex Volumes Differ in Maltreated Youth with and without Chronic Posttraumatic Stress Disorder. Neuropsychopharmacology 2015, 41, 791–801. [Google Scholar] [CrossRef] [PubMed]
  96. Rojas, D.C.; Smith, J.A.; Benkers, T.L.; Camou, S.L.; Reite, M.; Rogers, S.J. Hippocampus and Amygdala Volumes in Parents of Children With Autistic Disorder. Am. J. Psychiatry 2004, 161, 2038–2044. [Google Scholar] [CrossRef] [PubMed]
  97. Taren, A.A.; Creswell, J.D.; Gianaros, P.J. Dispositional Mindfulness Co-Varies with Smaller Amygdala and Caudate Volumes in Community Adults. PLoS ONE 2013, 8, e64574. [Google Scholar] [CrossRef] [PubMed]
  98. Tottenham, N.; Hare, T.A.; Quinn, B.T.; McCarry, T.W.; Nurse, M.; Gilhooly, T.; Millner, A.; Galvan, A.; Davidson, M.C.; Eigsti, I.-M.; et al. Prolonged institutional rearing is associated with atypically large amygdala volume and difficulties in emotion regulation. Dev. Sci. 2010, 13, 46–61. [Google Scholar] [CrossRef]
  99. Trotman, G.P.; Gianaros, P.J.; Van Zanten, J.J.C.S.V.; Williams, S.E.; Ginty, A.T. Increased stressor-evoked cardiovascular reactivity is associated with reduced amygdala and hippocampus volume. Psychophysiology 2018, 56, e13277. [Google Scholar] [CrossRef]
  100. Walton, E.; Cecil, C.A.; Suderman, M.; Liu, J.; Turner, J.A.; Calhoun, V.; Ehrlich, S.; Relton, C.C.; Barker, E.D. Longitudinal epigenetic predictors of amygdala:hippocampus volume ratio. J. Child Psychol. Psychiatry 2017, 58, 1341–1350. [Google Scholar] [CrossRef]
  101. Lupien, S.J.; Parent, S.; Evans, A.C.; Tremblay, R.E.; Zelazo, P.D.; Corbo, V.; Pruessner, J.C.; Séguin, J.R. Larger amygdala but no change in hippocampal volume in 10-year-old children exposed to maternal depressive symptomatology since birth. Proc. Natl. Acad. Sci. USA 2011, 108, 14324–14329. [Google Scholar] [CrossRef]
  102. Moutsiana, C.; Johnstone, T.; Murray, L.; Fearon, R.M.P.; Cooper, P.J.; Pliatsikas, C.; Goodyer, I.; Halligan, S.L. Insecure attachment during infancy predicts greater amygdala volumes in early adulthood. J. Child Psychol. Psychiatry 2014, 56, 540–548. [Google Scholar] [CrossRef] [PubMed]
  103. Brody, G.H.; Yu, T.; Nusslock, R.; Barton, A.W.; Miller, G.E.; Chen, E.; Holmes, C.; McCormick, M.; Sweet, L.H. The Protective Effects of Supportive Parenting on the Relationship Between Adolescent Poverty and Resting-State Functional Brain Connectivity During Adulthood. Psychol. Sci. 2019, 30, 1040–1049. [Google Scholar] [CrossRef] [PubMed]
  104. Barch, D.; Pagliaccio, D.; Belden, A.; Harms, M.P.; Gaffrey, M.; Sylvester, C.M.; Tillman, R.; Luby, J. Effect of Hippocampal and Amygdala Connectivity on the Relationship Between Preschool Poverty and School-Age Depression. Am. J. Psychiatry 2016, 173, 625–634. [Google Scholar] [CrossRef] [PubMed]
  105. Clark, U.S.; Miller, E.R.; Hegde, R.R. Experiences of Discrimination Are Associated With Greater Resting Amygdala Activity and Functional Connectivity. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2018, 3, 367–378. [Google Scholar] [CrossRef] [PubMed]
  106. Fourie, M.M.; Stein, D.J.; Solms, M.; Gobodo-Madikizela, P.; Decety, J. Effects of early adversity and social discrimination on empathy for complex mental states: An fMRI investigation. Sci. Rep. 2019, 9, 1–14. [Google Scholar] [CrossRef]
  107. Han, S.D.; Lamar, M.; Fleischman, D.; Kim, N.; Bennett, D.A.; Lewis, T.T.; Arfanakis, K.; Barnes, L.L. Self-reported experiences of discrimination in older black adults are associated with insula functional connectivity. Brain Imaging Behav. 2020, 1–10. [Google Scholar] [CrossRef]
  108. Phelps, E.; O’Connor, K.J.; A Cunningham, W.; Funayama, E.S.; Gatenby, J.C.; Gore, J.C.; Banaji, M.R. Performance on Indirect Measures of Race Evaluation Predicts Amygdala Activation. J. Cogn. Neurosci. 2000, 12, 729–738. [Google Scholar] [CrossRef]
  109. Wheeler, M.E.; Fiske, S.T. Controlling Racial Prejudice. Psychol. Sci. 2005, 16, 56–63. [Google Scholar] [CrossRef]
  110. Chekroud, A.M.; Everett, J.A.C.; Bridge, H.; Hewstone, M. A review of neuroimaging studies of race-related prejudice: Does amygdala response reflect threat? Front. Hum. Neurosci. 2014, 8, 179. [Google Scholar] [CrossRef]
  111. Assari, S.; Caldwell, C.H. Social Determinants of Perceived Discrimination among Black Youth: Intersection of Ethnicity and Gender. Children 2018, 5, 24. [Google Scholar] [CrossRef]
  112. Assari, S.; Gibbons, F.X.; Simons, R.L. Perceived Discrimination among Black Youth: An 18-Year Longitudinal Study. Behav. Sci. 2018, 8, 44. [Google Scholar] [CrossRef] [PubMed]
  113. Assari, S. Educational Attainment Better Protects African American Women than African American Men Against Depressive Symptoms and Psychological Distress. Brain Sci. 2018, 8, 182. [Google Scholar] [CrossRef] [PubMed]
  114. Assari, S. Family Socioeconomic Position at Birth and School Bonding at Age 15; Blacks’ Diminished Returns. Behav. Sci. 2019, 9, 26. [Google Scholar] [CrossRef] [PubMed]
  115. Assari, S. Family Income Reduces Risk of Obesity for White but Not Black Children. Children 2018, 5, 73. [Google Scholar] [CrossRef] [PubMed]
  116. Assari, S.; Gibbons, F.X.; Simons, R.L. Depression among Black Youth; Interaction of Class and Place. Brain Sci. 2018, 8, 108. [Google Scholar] [CrossRef]
  117. Assari, S. Blacks’ Diminished Return of Education Attainment on Subjective Health; Mediating Effect of Income. Brain Sci. 2018, 8, 176. [Google Scholar] [CrossRef]
  118. Assari, S.; Lankarani, M.M. Education and Alcohol Consumption among Older Americans; Black–White Differences. Front. Public Health 2016, 4, 67. [Google Scholar] [CrossRef]
  119. Assari, S.; Lankarani, M.M.; Caldwell, C.H.; A Zimmerman, M. Fear of Neighborhood Violence During Adolescence Predicts Development of Obesity a Decade Later: Gender Differences Among African Americans. Arch. Trauma Res. 2016, 5. [Google Scholar] [CrossRef]
  120. Assari, S. Education Attainment and Obesity: Differential Returns Based on Sexual Orientation. Behav. Sci. 2019, 9, 16. [Google Scholar] [CrossRef]
  121. Assari, S.; Lankarani, M.M. Reciprocal Associations between Depressive Symptoms and Mastery among Older Adults; Black-White Differences. Front. Aging Neurosci. 2017, 8, 279. [Google Scholar] [CrossRef]
  122. Assari, S. General Self-Efficacy and Mortality in the USA; Racial Differences. J. Racial Ethn. Health Disparities 2016, 4, 746–757. [Google Scholar] [CrossRef] [PubMed]
  123. Krieger, N.; Williams, D.; Zierler, S. “Whiting out” white privilege will not advance the study of how racism harms health. Am. J. Public Health 1999, 89, 782–785. [Google Scholar] [CrossRef] [PubMed]
  124. Krieger, N. Epidemiology, Racism, and Health: The Case of Low Birth Weight. Epidemiology 2000, 11, 237. [Google Scholar] [CrossRef] [PubMed]
  125. Rich-Edwards, J.; Krieger, N.; Majzoub, J.; Zierler, S.; Lieberman, E.; Gillman, M. Maternal experiences of racism and violence as predictors of preterm birth: Rationale and study design. Paediatr. Périnat. Epidemiol. 2001, 15, 124–135. [Google Scholar] [CrossRef]
  126. Krieger, N. Does Racism Harm Health? Did Child Abuse Exist Before 1962? On Explicit Questions, Critical Science, and Current Controversies: An Ecosocial Perspective. Am. J. Public Health 2003, 93, 194–199. [Google Scholar] [CrossRef]
  127. Parrott, R.L.; Silk, K.J.; Dillow, M.R.; Krieger, J.L.; Harris, T.M.; Condit, C.M. Development and validation of tools to assess genetic discrimination and genetically based racism. J. Natl. Med. Assoc. 2005, 97, 980–990. [Google Scholar]
  128. Krieger, N.; Smith, K.; Naishadham, D.; Hartman, C.; Barbeau, E.M. Experiences of discrimination: Validity and reliability of a self-report measure for population health research on racism and health. Soc. Sci. Med. 2005, 61, 1576–1596. [Google Scholar] [CrossRef]
  129. Krieger, N. Does Racism Harm Health? Did Child Abuse Exist Before 1962? On Explicit Questions, Critical Science, and Current Controversies: An Ecosocial Perspective. Am. J. Public Health 2008, 98, S20–S25. [Google Scholar] [CrossRef]
  130. Krieger, N. Living and Dying at the Crossroads: Racism, Embodiment, and Why Theory Is Essential for a Public Health of Consequence. Am. J. Public Health 2016, 106, 832–833. [Google Scholar] [CrossRef]
  131. Bassett, M.T.; Krieger, N.; Bailey, Z. Charlottesville: Blatant racism, not grievances, on display. Lancet 2017, 390, 2243. [Google Scholar] [CrossRef][Green Version]
  132. Bailey, Z.; Krieger, N.; Agénor, M.; Graves, J.; Linos, N.; Bassett, M.T. Structural racism and health inequities in the USA: Evidence and interventions. Lancet 2017, 389, 1453–1463. [Google Scholar] [CrossRef]
Table 1. Descriptive data overall (n = 9380).
Table 1. Descriptive data overall (n = 9380).
Characteristics
n%
Race
White718176.6
Black219923.4
Ethnicity
Non-Latino777982.9
Latino160117.1
Sex
Male444347.4
Female493752.6
Age (year)
9505253.9
10432846.1
Parental employment status
Employed290631.0
Unemployed647469.0
Parental marital status
Not married304132.4
Married633967.6
MeanSD
Parental education (years)16.712.63
Subjective family SES0.930.16
Amygdala volume1563.26231.67
Table 2. Bivariate associations (n = 9380).
Table 2. Bivariate associations (n = 9380).
123456789
1 Race (black)1.00−0.12 **−0.020.00−0.41 **−0.05 **−0.30 **−0.27 **−0.18 **
2 Ethnicity (Latino) 1.00−0.01−0.02−0.08 **−0.04 **−0.19 **−0.07 **−0.05 **
3 Sex (male) 1.000.020.01−0.01−0.01−0.010.32 **
4 Age (10) 1.000.000.010.000.020.07 **
5 Parents married 1.000.03 **0.36 **0.30 **0.12 **
6 Parents employed 1.000.25 **0.13 **0.04 **
7 Parental education (years) 1.000.34 **0.11 **
8 Subjective family SES 1.000.10 **
9 Total amygdala volume 1.00
** p < 0.01.
Table 3. Linear regressions in the pooled sample (n = 9380).
Table 3. Linear regressions in the pooled sample (n = 9380).
Model 1Model 2
bSE95% CItpbSE95% CItp
Race (black)−78.525.99−90.27−66.78−13.11<0.00135.4940.78−44.46115.430.870.384
Ethnicity (Hispanic)−27.136.19−39.26−14.99−4.38<0.00176.0743.12−8.46160.601.760.078
Sex (male)145.284.44136.58153.9732.75<0.001145.404.43136.71154.1032.79<0.001
Age (10)28.594.4419.8837.306.44<0.00128.624.4419.9237.336.45<0.001
Married household16.925.466.2227.633.100.00216.435.485.6827.173.000.003
Parents employed10.514.970.7720.252.110.03411.054.971.3020.802.220.026
Parental education (years)3.990.982.065.914.050.0004.751.441.937.583.300.001
Subjective family SES44.1914.6615.4472.943.010.00395.6823.1450.32141.044.14<0.001
Parental education (years) x race −3.472.20−7.780.84−1.580.114
Parental education (years) x ethnicity 0.092.09−4.024.190.040.968
Subjective family SES x race −63.8630.46−123.56−4.16−2.100.036
Subjective family SES x ethnicity −110.8237.28−183.89−37.76−2.970.003
Table 4. Linear regressions by race (n = 9380).
Table 4. Linear regressions by race (n = 9380).
Model 3Model 4
bSE95% CItpbSE95% CItp
Ethnicity (Hispanic)−32.206.94−45.81−18.60−4.64<0.00114.8915.43−15.3645.150.970.335
Sex (male)150.345.13140.28160.4129.28<0.001130.118.75112.95147.2714.87<0.001
Age (10)31.345.1421.2641.426.10<0.00119.138.771.9436.332.180.029
Married household11.856.55−0.9924.691.810.07028.109.888.7347.472.850.004
Parents employed7.875.78−3.4719.211.360.17419.739.660.7838.672.040.041
Parental education (years)4.731.172.447.034.05<0.0010.221.86−3.423.860.120.907
Subjective family SES63.4020.2023.81103.003.140.00217.1420.69−23.4457.710.830.408
Table 5. Linear regressions by ethnicity (n = 9380).
Table 5. Linear regressions by ethnicity (n = 9380).
Model 5Model 6
bSE95% CItpbSE95% CItp
Race (black)−82.216.67−95.28−69.14−12.33<0.001−41.3215.89−72.48−10.16−2.600.009
Sex (male)143.354.92133.71152.9929.15<0.001155.2610.21135.24175.2815.21<0.001
Age (10)28.064.9218.4237.715.70<0.00130.3010.2610.1750.432.950.003
Married household20.136.327.7532.513.190.0011.9110.79−19.2623.090.180.859
Parents employed11.865.540.9922.722.140.0326.9611.18−14.9828.890.620.534
Parental education (years)3.141.230.735.542.560.0114.981.631.798.183.060.002
Subjective family SES58.0616.6425.4390.683.49<0.001−14.3730.71−74.6045.86−0.470.640

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Assari, S.; Boyce, S.; Bazargan, M. Subjective Socioeconomic Status and Children’s Amygdala Volume: Minorities’ Diminish Returns. NeuroSci 2020, 1, 59-74. https://doi.org/10.3390/neurosci1020006

AMA Style

Assari S, Boyce S, Bazargan M. Subjective Socioeconomic Status and Children’s Amygdala Volume: Minorities’ Diminish Returns. NeuroSci. 2020; 1(2):59-74. https://doi.org/10.3390/neurosci1020006

Chicago/Turabian Style

Assari, Shervin, Shanika Boyce, and Mohsen Bazargan. 2020. "Subjective Socioeconomic Status and Children’s Amygdala Volume: Minorities’ Diminish Returns" NeuroSci 1, no. 2: 59-74. https://doi.org/10.3390/neurosci1020006

APA Style

Assari, S., Boyce, S., & Bazargan, M. (2020). Subjective Socioeconomic Status and Children’s Amygdala Volume: Minorities’ Diminish Returns. NeuroSci, 1(2), 59-74. https://doi.org/10.3390/neurosci1020006

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