Descriptive characteristics overall and by ethnicity (n = 10,840).
While studies have indicated an association between socioeconomic status (SES) and neuroimaging measures, weaker SES effects are shown for Blacks than Whites. This is, in part, due to processes such as stratification, racism, minoritization, and othering of Black people in the United States. However, less is known about Latino youth. This study had two aims: First, to test the association between parental education and the right and left nucleus accumbens (NAcc) resting-state functional connectivity with the frontoparietal network (FPN) in children; and second, to investigate ethnic heterogeneity in this association. This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study. We analyzed the resting-state functional connectivity data (rsFC) of 10,840 US preadolescents who were between 9 and 10 years old. The main outcomes were the NAcc resting-state functional connectivity with FPN separately calculated for right and left hemispheres. Parental education was our independent variable. Family structure, sex, and age were covariates. Furthermore, ethnicity (Latino vs. non-Latino) was regarded as the moderator. We used mixed-effects regression for data analysis with and without interaction terms between parental education and ethnicity. Most participants (n = 8690; 80.2%) were non-Latino and 2150 (19.8%) were Latino. Parental education was associated with higher right and left NAcc resting-state functional connectivity with FPN. Ethnicity showed statistically significant interactions with parental education, suggesting that the positive associations between parental education and right and left NAcc resting-state functional connectivity with FPN were different in non-Latino and Latino children. For right hemisphere, we found significantly stronger and for left hemisphere, we found significantly weaker association for Latino compared with non-Latino preadolescents. Preadolescents’ NAcc resting-state functional connectivity with FPN depends on the intersections of ethnicity, parental education, and laterality.
- ethnic groups
- nucleus accumbens (NAcc)
- reward system
- socioeconomic status
- parental education
- brain development
- functional MRI
- functional connectivity
The right and left nucleus accumbens (NAcc) are subcortical brain structures, located within the ventral striatum, that serve as a key limbic-motor interface, and have an important role in Pavlovian learning [1, 2]. This means that the right and left NAcc contribute to the regulation of emotional and motivation processing , incentive salience , pleasure, reward, and reinforcement . In addition, neural reactivity to food- and/or drug-related reward cues evokes robust dopamine responses in the right and left NAcc . This suggests that as parts of the brain reward system, the right and left NAcc function reflects how individuals respond to cues that signal a potential reward . The right and left NAcc have also been implicated in obesity [2, 6], food addiction , tobacco, alcohol, and drug-seeking behaviors [8, 9, 10], obsessive-compulsive disorder [11, 12], depression , and anxiety .
To fulfill their functions, the right and left NAcc communicate with a number of large-scale brain networks such as the frontoparietal network (FPN) [14, 15, 16]. Neuroimaging studies have revealed some alterations of the connectivity between the NAcc and FPN as an indicator of altered NAcc function . The FPN, also known as the central executive network (CEN), is a large-scale brain network that works with the NAcc, striatum, and basal ganglia . FPN is implicated in the cognitive control , attention , problem-solving , and working memory . Altered FPN function is linked to attention-deficit/hyperactivity disorder (ADHD) [18, 20], cocaine addiction , and several mental disorders  in children and adolescents. Disruption in the FPN during cognitive control tasks is a common element of schizophrenia, bipolar disorder, unipolar depression, anxiety, and substance use disorders .
Functional magnetic resonance imaging (fMRI) techniques have expanded what we know about functional connectivity across brain regions and networks. Resting-state functional connectivity (rsFC) investigates the temporal correlation of the spatially distributed brain regions’ activity at the resting state (i.e., when the participant has not engaged in an explicit task yet) . rsFC allows us to identify spontaneous brain activity patterns, which can provide insight into neural activity patterns . One advantage of rsFC is that it can explore networks not easily assessed during tasks and activities. Finally, rsFC tends to be free from bias in task selection and allows relatively easy data collection .
The frontoparietal-accumbal connectivity has a role in motivated behavior, food seeking, emotion regulation, food preference, obesity, eating disorders, and dopaminergic and reward systems of the brain [14, 15, 26, 27]. Decreased functional connectivity between the NAcc and the FPN is seen in depression . An increase in the functional connectivity between the FPN and the NAcc is seen following mindfulness training . Connectivity between the right NAcc and the FPN is also associated with substance use and cognitive control .
In comparison to peers with high parental education, children from low parental education have worse brain development . The effects of parental education are well described on brain reward system, inhibitory control, cognitive development , language , executive function , and school achievement . Low parental education is a risk factor for several mental, physical, and behavioral problems  including anxiety , depression , substance use problems [35, 36, 37], early initiation of sexual behavior , delinquency , obesity , and high blood pressure . Parental education reduces children’s antisocial behaviors , externalizing problems , anxiety and depression , behavioral problems , psychiatric disorders , mental health problems , tobacco dependence and aggression , and school problems  in children and adolescents. High parental education is linked to the size and function of the NAcc , thalamus , hippocampus , amygdala , and cerebral cortex.
According to the Minorities’ Diminished Returns (MDRs) framework, parental education produces unequal outcomes for subpopulations [51, 52]. Additionally, based on the MDRs, ethnic minority children are less likely to have equal opportunities to gain from their parents’ education to ensure health outcomes [53, 54]. Stratification, racism, segregation, and marginalization are shown to decrease parental education’s effects on developmental outcomes for ethnic minorities [55, 56]. However, most of the MDRs’ literature is on Black, rather than Latino, children [49, 57, 58]. While we know about the poor attention , low school performance , high reward dependence , impulsivity , suicide , aggression , depression , and problem behaviors  of Black children with highly educated parents, very limited knowledge exists on Latino children.
According to Harrist and Criss, influences of parental conditions such as parental education are not additive to the effects of other social and behavioral determinants. There are complex moderated mediational influences of parental conditions that are beyond additive effects and may be sub-additive, synergistic, or multiplicative. These effects also vary across diverse groups of families with different socioeconomic and demographic backgrounds . For example, parental education may have diminished influences on children brain development of Black than White families, in part because structural racism may reduce what parental education can do for a Black child . Thus, there is an interest to test heterogeneity of the effects of parental conditions and to investigate the multiplicative and non-additive effects of parental resources and other factors that impact child development . While these differential effects of parental education are shown for structure and function of some brain regions such as amygdala , thalamus , hippocampus , and cerebral cortex [68, 72, 73], less is known about heterogeneity of the effects of parental education on NAcc.
Previous neuroimaging studies have shown the association between parental education and children’s brain function and structure [56, 65]. Different from other socioeconomic status (SES) indicators such as income and poverty, parental education tends to represent an aspect of SES that is not represented by the presence of financial or material resources in the family . Still, there continues to be a lack of studies on the effects of parental education on brain functional connectivity of the NAcc and FPN in group differences at the resting state. Likewise, it is necessary to examine the connectivity between the right and left NAcc and FPN that may reflect reward salience, reward process, cognitive control [75, 76, 77], and various cognition, emotions, and psychological problems [75, 76].
Using a sample of 9/10-year-old preadolescents from the Adolescent Brain Cognitive Development research (ABCD) study [75, 78], the present study had two aims: first, to investigate the correlation between parental education and rsFC between the right and left Nacc and FPN; and second, to examine ethnic heterogeneity in this correlation. We hypothesized that parental education would be positively associated with the functional connectivity of the right and left NAcc and FPN, and that there would be a weaker effect of parental education on the right and left NAcc functional connectivity with FPN for Latino than non-Latino preadolescents.
2.1 Design and settings
Data for this secondary analysis came from baseline (wave 1) of the Adolescent Brain Cognitive Development (ABCD) study. The ABCD is an unprecedented study in the examination of children’s brain development [75, 79]. The ABCD study is a longitudinal study of a diverse sample of children from age 9 to 10 to their early adulthood . For more information regarding the ABCD sample, methods, measures, and imaging techniques, please see here .
2.2 Participants and sampling
The ABCD study is a multi-site longitudinal study that has recruited 11,875 children aged 9–10, from 21 cities across different states, to characterize their psychological and neurobiological development from early adolescence to early adulthood . Most of the participants were recruited through schools across the 21 study sites . Because of well-designed and performed sampling process, the ABCD study sample has generated a sample that although is not nationally representative, it is a balanced sample that has a strong proxy of US adolescents . Thus, the ABCD sample is a close approximation of US children in terms of distribution of age, SES, ethnicity, sex, and urbanicity .
2.3 Analytical sample
For this analysis, we only used the ABCD baseline sample. We included the ABCD study regardless of their race, ethnicity, and psychopathologies . However, we limited the sample to those who had complete data on our variables and met satisfactory imaging quality. Our analytical n for the analyses *presented here is 7959.
2.4.1 Study variables
The study variables included parental education (independent variable), children’s ethnicity (moderator), ethnicity, age, race, sex, parental marital status (confounders), and NAcc functional connectivity with the FPN, separately calculated for the right and left (dependent variables).
18.104.22.168 Independent variables
22.214.171.124 Dependent variables
2.5 Data analysis
We used the Data Exploration and Analysis Portal (DEAP), a user-friendly online platform for multivariable analysis of the ABCD data. For multivariable analyses, two mixed-effects regression models were estimated (Supplementary Table).
2.6 Ethical aspect
The original ABCD research protocol received Institutional Review Board (IRB) approval in several institutions, including the University of California, San Diego (UCSD). Additionally, we received the ABCD data through an agreement between Charles R. Drew University and NIH/NDA. As the ABCD data were fully de-identified, our study was considered to be a nonhuman subject research. This exempted our study from a full review. Besides, all children in the ABCD study provided verbal assent to the protocol approved by the IRB, and all parents/caregivers signed the written informed consent form .
3.1 Sample descriptive data
The present study used data from a large sample of 10,840 preadolescents between 9 and 10 years old (
|Mean (SD)||Mean (SD)||Mean (SD)||Mean (SD)|
|Age (month)||119.06 (7.51)||119.18 (7.48)||118.61 (7.58)||0.002|
|Right NAcc functional connectivity with the FPN||−0.01 (0.15)||−0.01 (0.15)||−0.02 (0.15)||0.043|
|Left NAcc functional connectivity with the FPN||−0.06 (0.17)||−0.06 (0.17)||−0.06 (0.17)||0.421|
|<HS diploma||470 (4.3)||184 (2.1)||286 (13.3)||<0.001|
|HS diploma/GED||970 (8.9)||634 (7.3)||336 (15.6)|
|Some college||2815 (26.0)||2071 (23.8)||744 (34.6)|
|Bachelor||2791 (25.7)||2393 (27.5)||398 (18.5)|
|Postgraduate degree||3794 (35.0)||3408 (39.2)||386 (18.0)|
|White||7071 (65.2)||5798 (66.7)||1273 (59.2)||<0.001|
|Black||1654 (15.3)||1573 (18.1)||81 (3.8)|
|Asian||256 (2.4)||235 (2.7)||21 (1.0)|
|Other/mixed||1859 (17.1)||1084 (12.5)||775 (36.0)|
|Female||5194 (47.9)||4162 (47.9)||1032 (48.0)||0.949|
|Male||5646 (52.1)||4528 (52.1)||1118 (52.0)|
|No||3413 (31.5)||2532 (29.1)||881 (41.0)||<0.001|
|Yes||7427 (68.5)||6158 (70.9)||1269 (59.0)|
The fit of the mixed-effects regression model is summarized in Table 2. Models with the interaction effects between parental education and ethnicity showed a better fit when compared with main effect models that only included ethnicity and parental education. This shows that interaction between parental education and ethnicity contributes more to explaining the variance of the outcome for both the right and left NAcc with FPN connectivity.
|Main effect||Interaction effect||Main effect||Interaction effect|
3.2.1 Main effect model
As shown by Table 3 and Figure 1, parental education showed a positive association with the functional connectivity between the right and left NAcc with FPN. This positive correlation suggests that children with higher parental education have a stronger rsFC between the right NAcc and FPN.
|Parental education (HS Diploma/GED) × hispanic||0.01551||0.01763||0.379094|
|Parental education (some college) × hispanic||0.03629||0.01586||0.0221188||*|
|Parental education (bachelor) × hispanic||0.03788||0.01680||0.0241787||*|
|Parental education (postgraduate degree) × hispanic||0.01227||0.01677||0.4644527|
3.2.2 Interactive effects model
Table 4 and Figure 1 show that parental education had a stronger positive association between parental education and the right FPN resting-state functional connectivity in Hispanic children than non-Hispanic children.
|Parental education (HS diploma/GED) × Hispanic||−0.05046||0.01993||0.0113542||*|
|Parental education (some college) × Hispanic||−0.04662||0.01793||0.0093157||**|
|Parental education (bachelor) × Hispanic||−0.03491||0.01899||0.066009||#|
|Parental education (postgraduate degree) × Hispanic||−0.03745||0.01895||0.0482004||*|
3.3.1 Main effects model
As shown by Table 4 and Figure 2, parental education showed a positive association with the functional connectivity between the right and left NAcc with FPN. This positive correlation suggests that children with higher parental education have stronger rsFC between left NAcc and FPN.
3.3.2 Interactive effects model
Table 4 and Figure 2 show that parental education had a negative interaction with ethnicity on the functional connectivity between the FPN and the left NAcc. This interaction was indicative of a weaker positive association between parental education and the left FPN-NAcc resting-state functional connectivity in Hispanic children than non-Hispanic children.
Our first aim showed a positive correlation between parental education and the NAcc resting-state functional connectivity with the FPN. Our second aim showed ethnic variation in the association between parental education and the right and left NAcc resting-state functional connectivity with the FPN. That is laterality, ethnicity, and parental education all show multiplicative effects on NAcc resting-state functional connectivity with the FPN. While we found a stronger correlation between parental education and the resting-state FPN’s functional connectivity with the right NAcc in Latino than non-Latino children, parental education showed a weaker association with the same connectivity for the left NAcc. The finding on the right NAcc contrasts with the MDRs, but the finding on the left NAcc supports the MDRs’ theory, which shows a weaker association between SES and brain development for marginalized and minority children than White children.
Our first finding is in agreement with other work showing the effects of parental education on brain structure , performance in several cognitive domains , and mental health problems, such as anxiety and depression . However, most of what we know about SES effects are limited to specific brain regions [74, 83, 84], rather than rsFC. Past research has established a link between parental education and the size and activity of brain structures, such as the NAcc , amygdala , hippocampus , and thalamus . In a study of examining a sample of 283 children and adolescents aged 4–18, higher parental education significantly predicted greater cortical thickness in the right anterior cingulate and left superior frontal gyrus . Among 9475 children from the ABCD study, parental education was associated with reduced within and between sensorimotor network connectivity and increased sensorimotor network connectivity to frontal functional networks . Furthermore, in line with our finding, higher parental education is shown to be linked to the development of frontoparietal connectivity in children . Neurodevelopmental correlates of parental education may mediate why parental education is linked to behaviors , executive functions , reading ability , spatial skills, and inhibitory control . Importantly, however, no studies to our knowledge have examined the associations between parental education and rsFC within the NAcc and FPN.
The effect of parental education on brain function can be explained by underlying mechanisms , such as cognitive stimulation available at home, parent–child interactions, and home learning environment, which all predict brain development [33, 88]. For example, more educated parents dedicate more time for their children in ways that seem to improve their children’s development [89, 90]. Likewise, more educated parents appear to have higher expectations for their children, provide more stimulating learning materials, use more complex language and speech patterns, and engage more with their children’s learning [89, 91]. These can help promote children’s cognitive development . Furthermore, the skills obtained from formal education appear to enable parents to arrange their activities in ways that allow them to effectively accomplish their parenting goals .
The results of the right NAcc-FPN connectivity were not in line with what is shown from the comparison of Black and White children. According to the MDRs’ theory, parental education is more protective for White children than Black children. This finding was observed for the left NAcc-FPN connectivity. Similar to our finding on the left side, the effects of SES on attention , reward dependence , school performance , aggression , impulsivity , suicide , anxiety , and problem behaviors  are shown to be weaker in Black than White adolescents. This is the first study on the MDRs of parental education for NAcc functional connectivity with the FPN in Latino children. Even when MDRs exist, the right and left NAcc findings may vary largely.
Parental education has different and group-specific effects on children and youth brain development. This means that SES resources and ethnicity may have multiplicative, rather than additive, effects on the right NAcc resting-state functional connectivity with FPN. In this study and all the MDRs’ literature, ethnic variation in the SES effects is shaped by social rather than biological mechanisms. Thus, in our study, ethnicity is a social rather than biological factor. Consequently, the differential treatment of society, which is preventable, has resulted in the ethnic differences. Importantly, we consider race as a proxy of racism, such as labor market discrimination, low school quality, segregation, and differential policing, that results in reduced effects of parental education, even for more educated people .
The present study had some limitations. Firstly, a cross-sectional design limits any inference of causal links between parental education, ethnicity, and NAcc functional connectivity with the FPN. Secondly, we only studied parental education; other SES indicators were not included. Moreover, we did not examine how other factors, such as neighborhood context, stress, and social adversities, mitigate these effects across groups. Thirdly, Latino people are highly diverse. Cuban, Mexican, and Puerto Rican families differ in their history, culture, neighborhoods, SES, and other factors that may alter SES effects.
Although high NAcc resting-state functional connectivity with FPN is under the influence of parental education, ethnicity, and laterality, these effects are multiplicative rather than additive. This means that, while the parental education gradient was stronger for the right NAcc in Latino than non-Latino American preadolescents, the opposite finding was observed for the left NAcc. Due to qualitative differences in the lived conditions of ethnic groups in the United States, various subgroups may show different SES effects on brain development.
|Model 1||rsfmri_cor_network.gordon_frontoparietal_subcort.aseg_accumbens.area.rh ∼ high.educ.bl + hisp + race.4level + age + sex + married.bl Random: ∼(1|rel_family_id)||rsfmri_cor_network.gordon_frontoparietal_subcort.aseg_accumbens.area.lh ∼ high.educ.bl + hisp + race.4level + age + sex + married.bl Random: ∼(1|rel_family_id)|
|Model 2||rsfmri_cor_network.gordon_frontoparietal_subcort.aseg_accumbens.area.rh ∼ high.educ.bl + hisp + race.4level + age + sex + married.bl + high.educ.bl * hisp Random: ∼(1|rel_family_id)||rsfmri_cor_network.gordon_frontoparietal_subcort.aseg_accumbens.area.lh ∼ high.educ.bl + hisp + race.4level + age + sex + married.bl + high.educ.bl * hispRandom: ∼(1|rel_family_id)|
Stelly CE, Girven KS, Lefner MJ, Fonzi KM, Wanat MJ. Dopamine release and its control over early Pavlovian learning differs between the NAc core and medial NAc shell. Neuropsychopharmacology. 2021:1-8
Ferrario CR. Why did I eat that? Contributions of individual differences in incentive motivation and nucleus accumbens plasticity to obesity. Physiology & Behavior. 2020; 227:113114
Salgado S, Kaplitt MG. The nucleus accumbens: A comprehensive review. Stereotactic and Functional Neurosurgery. 2015; 93(2):75-93
Olney JJ, Warlow SM, Naffziger EE, Berridge KC. Current perspectives on incentive salience and applications to clinical disorders. Current Opinion in Behavioral Sciences. 2018; 22:59-69
Boissoneault J, Stennett B, Robinson ME. Acute alcohol intake alters resting state functional connectivity of nucleus accumbens with pain-related corticolimbic structures. Drug and Alcohol Dependence. 2020; 207:107811
Rapuano KM, Laurent JS, Hagler DJ, et al. Nucleus accumbens cytoarchitecture predicts weight gain in children. Proceedings of the National Academy of Sciences. 2020; 117(43):26977-26984
Domingo-Rodriguez L, de Azua IR, Dominguez E, et al. A specific prelimbic-nucleus accumbens pathway controls resilience versus vulnerability to food addiction. Nature Communications. 2020; 11(1):1-16
Boissoneault J, Lewis B, Nixon SJ. Characterizing chronic pain and alcohol use trajectory among treatment-seeking alcoholics. Alcohol. 2019; 75:47-54
Sousa SS, Sampaio A, López-Caneda E, Bec C, Gonçalves ÓF, Crego A. Increased nucleus accumbens volume in college binge drinkers-preliminary evidence from manually segmented MRI analysis. Frontiers in Psychiatry. 2020; 10:1005
Buck SA, Torregrossa MM, Logan RW, Freyberg Z. Roles of dopamine and glutamate co-release in the nucleus accumbens in mediating the actions of drugs of abuse. The FEBS Journal. 2021; 288(5):1462-1474
Chen Y, Ou Y, Lv D, et al. Decreased nucleus accumbens connectivity at rest in medication-free patients with obsessive-compulsive disorder. Neural Plasticity. 2021; 2021
Zhang X-Y, Peng S-Y, Shen L-P, et al. Targeting presynaptic H3 heteroreceptor in nucleus accumbens to improve anxiety and obsessive-compulsive-like behaviors. Proceedings of the National Academy of Sciences. 2020; 117(50):32155-32164
Heshmati M, Christoffel DJ, LeClair K, et al. Depression and social defeat stress are associated with inhibitory synaptic changes in the nucleus accumbens. Journal of Neuroscience. 2020; 40(32):6228-6233
Ding Y, Ji G, Li G, et al. Altered interactions among resting-state networks in individuals with obesity. Obesity. 2020; 28(3):601-608
Cerit H, Davidson P, Hye T, et al. Resting-state brain connectivity predicts weight loss and cognitive control of eating behavior after vertical sleeve gastrectomy. Obesity. 2019; 27(11):1846-1855
Cooper JC, Knutson B. Valence and salience contribute to nucleus accumbens activation. Neuroimage. 2008; 39(1):538-547
Tan Y, Tan J, Deng J, et al. Alteration of basal ganglia and right frontoparietal network in early drug-naive Parkinson’s disease during heat pain stimuli and resting state. Frontiers in Human Neuroscience. 2015; 9:467
Cai W, Griffiths K, Korgaonkar MS, Williams LM, Menon V. Inhibition-related modulation of salience and frontoparietal networks predicts cognitive control ability and inattention symptoms in children with ADHD. Molecular Psychiatry. 2019:1-10
Markett S, Reuter M, Montag C, et al. Assessing the function of the fronto-parietal attention network: Insights from resting-state fMRI and the attentional network test. Human Brain Mapping. 2014; 35(4):1700-1709
Gao Y, Shuai D, Bu X, et al. Impairments of large-scale functional networks in attention-deficit/hyperactivity disorder: A meta-analysis of resting-state functional connectivity. Psychological Medicine. 2019; 49(15):2475-2485
Barrós-Loscertales A, Costumero V, Rosell-Negre P, Fuentes-Claramonte P, Llopis-Llacer JJ, Bustamante JC. Motivational factors modulate left frontoparietal network during cognitive control in cocaine addiction. Addiction Biology. 2020; 25(4):e12820
Lees B, Squeglia LM, McTeague LM, et al. Altered neurocognitive functional connectivity and activation patterns underlie psychopathology in preadolescence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2021; 6(4):387-398
McTeague LM, Huemer J, Carreon DM, Jiang Y, Eickhoff SB, Etkin A. Identification of common neural circuit disruptions in cognitive control across psychiatric disorders. American Journal of Psychiatry. 2017; 174(7):676-685
Smith SM, Fox PT, Miller KL, et al. Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences. 2009; 106(31):13040-13045
Hohenfeld C, Werner CJ, Reetz K. Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker? NeuroImage: Clinical. 2018; 18:849-870
Kim SH, Park BY, Byeon K, et al. The effects of high-frequency repetitive transcranial magnetic stimulation on resting-state functional connectivity in obese adults. Diabetes, Obesity and Metabolism. 2019; 21(8):1956-1966
Ma N, Liu Y, Li N, et al. Addiction related alteration in resting-state brain connectivity. Neuroimage. 2010; 49(1):738-744
Liu R, Wang Y, Chen X, Zhang Z, Xiao L, Zhou Y. Anhedonia correlates with functional connectivity of the nucleus accumbens subregions in patients with major depressive disorder. NeuroImage: Clinical. 2021; 30:102599
Smith JL, Allen JW, Haack C, et al. The impact of app-delivered mindfulness meditation on functional connectivity and self-reported mindfulness among health profession trainees. Mindfulness. 2021; 12(1):92-106
Weissman DG, Schriber RA, Fassbender C, et al. Earlier adolescent substance use onset predicts stronger connectivity between reward and cognitive control brain networks. Developmental Cognitive Neuroscience. 2015; 16:121-129
Jenkins LM, Chiang JJ, Vause K, et al. Subcortical structural variations associated with low socioeconomic status in adolescents. Human Brain Mapping. 2020; 41(1):162-171
McDermott CL, Seidlitz J, Nadig A, et al. Longitudinally mapping childhood socioeconomic status associations with cortical and subcortical morphology. Journal of Neuroscience. 2019; 39(8):1365-1373
Noble KG, Houston SM, Brito NH, et al. Family income, parental education and brain structure in children and adolescents. Nature Neuroscience. 2015; 18(5):773-778
Merz EC, Tottenham N, Noble KG. Socioeconomic status, amygdala volume, and internalizing symptoms in children and adolescents. Journal of Clinical Child & Adolescent Psychology. 2018; 47(2):312-323
Barr PB, Silberg J, Dick DM, Maes HH. Childhood socioeconomic status and longitudinal patterns of alcohol problems: Variation across etiological pathways in genetic risk. Social Science & Medicine. 2018; 209:51-58
Hiscock R, Dobbie F, Bauld L. Smoking cessation and socioeconomic status: An update of existing evidence from a national evaluation of English stop smoking services. BioMed Research International. 2015; 2015. DOI: 10.1155/2015/274056
Martinez SA, Beebe LA, Thompson DM, Wagener TL, Terrell DR, Campbell JE. A structural equation modeling approach to understanding pathways that connect socioeconomic status and smoking. PLoS One. 2018; 13(2):e0192451
Valencia MLC, Tran BT, Lim MK, Choi KS, Oh J-K. Association between socioeconomic status and early initiation of smoking, alcohol drinking, and sexual behavior among Korean adolescents. Asia Pacific Journal of Public Health. 2019; 31(5):443-453
Rekker R, Pardini D, Keijsers L, Branje S, Loeber R, Meeus W. Moving in and out of poverty: The within-individual association between socioeconomic status and juvenile delinquency. PLoS one. 2015; 10(11):e0136461
Shaikh RA, Siahpush M, Singh GK, Tibbits M. Socioeconomic status, smoking, alcohol use, physical activity, and dietary behavior as determinants of obesity and body mass index in the United States: Findings from the National Health Interview Survey. International Journal of MCH and AIDS. 2015; 4(1):22
Kaczmarek M, Stawińska-Witoszyńska B, Krzyżaniak A, Krzywińska-Wiewiorowska M, Siwińska A. Who is at higher risk of hypertension? Socioeconomic status differences in blood pressure among Polish adolescents: A population-based ADOPOLNOR study. European Journal of Pediatrics. 2015; 174(11):1461-1473
Piotrowska PJ, Stride CB, Croft SE, Rowe R. Socioeconomic status and antisocial behaviour among children and adolescents: A systematic review and meta-analysis. Clinical Psychology Review. 2015; 35:47-55
Bøe T, Øverland S, Lundervold AJ, Hysing M. Socioeconomic status and children’s mental health: Results from the Bergen Child Study. Social Psychiatry and Psychiatric Epidemiology. 2012; 47(10):1557-1566
Hosokawa R, Katsura T. Effect of socioeconomic status on behavioral problems from preschool to early elementary school–A Japanese longitudinal study. PLoS One. 2018; 13(5):e0197961
Reiss F. Socioeconomic inequalities and mental health problems in children and adolescents: A systematic review. Social Science & Medicine. 2013; 90:24-31
Assari S, Caldwell CH, Bazargan M. Association between parental educational attainment and youth outcomes and role of race/ethnicity. JAMA Network Open. 2019; 2(11):e1916018-e1916018
Long H, Pang W. Family socioeconomic status, parental expectations, and adolescents’ academic achievements: A case of China. Educational Research and Evaluation. 2016; 22(5-6):283-304
Assari S. Parental education and nucleus accumbens response to reward anticipation: Minorities’ diminished returns. Advances in Social Science and Culture. 2020; 2(4):132
Assari S, Curry TJ. Parental education ain’t enough: A study of race (racism), parental education, and children’s thalamus volume. Journal of Education and Culture Studies. 2021; 5(1):1
Assari S, Boyce S, Bazargan M, Caldwell CH. Family income mediates the effect of parental education on adolescents’ hippocampus activation during an n-back memory task. Brain Sciences. 2020; 10(8):520
Assari S. Mental rotation in american children: Diminished returns of parental education in black families. Pediatric Reports. 2020; 12(3):130-141
Assari S. Cingulo-opercular and cingulo-parietal brain networks functional connectivity in pre-adolescents: Multiplicative effects of race, ethnicity, and parental education. Research in Health Science. 2021; 6(2)
Assari S, Boyce S, Akhlaghipour G, Bazargan M, Caldwell CH. Reward responsiveness in the Adolescent Brain Cognitive Development (ABCD) study: African Americans’ diminished returns of parental education. Brain Sciences. 2020; 10(6):391
Chetty R, Hendren N, Kline P, Saez E. Where is the land of opportunity? The geography of intergenerational mobility in the United States. The Quarterly Journal of Economics. 2014; 129(4):1553-1623
Assari S. Parental education and youth inhibitory control in the Adolescent Brain Cognitive Development (ABCD) Study: Blacks’ diminished returns. Brain Sciences. 2020; 10(5):312
Assari S. Nucleus accumbens functional connectivity with the default mode network: Black children’s diminished returns of household income. Research in Health Science. 2021; 6(3)
Assari S. Parental education attainment and educational upward mobility; role of race and gender. Behavioral Sciences. 2018; 8(11):107
Assari S. Parental education better helps white than black families escape poverty: National survey of children’s health. Economies. 2018; 6(2):30
Assari S, Boyce S, Bazargan M. Subjective family socioeconomic status and adolescents’ attention: Blacks’ diminished returns. Children. 2020; 7(8):80
Spera C, Wentzel KR, Matto HC. Parental aspirations for their children’s educational attainment: Relations to ethnicity, parental education, children’s academic performance, and parental perceptions of school climate. Journal of Youth and Adolescence. 2009; 38(8):1140-1152
Assari S, Akhlaghipour G, Boyce S, Bazargan M, Caldwell CH. African American children’s diminished returns of subjective family socioeconomic status on fun seeking. Children. 2020; 7(7):75
Morris AS, Silk JS, Steinberg L, Myers SS, Robinson LR. The role of the family context in the development of emotion regulation. Social Development. 2007; 16(2):361-388
Assari S, Boyce S, Bazargan M, Caldwell CH. African Americans’ diminished returns of parental education on adolescents’ depression and suicide in the Adolescent Brain Cognitive Development (ABCD) study. European Journal of Investigation in Health, Psychology and Education. 2020; 10(2):656-668
Pabayo R, Molnar BE, Kawachi I. The role of neighborhood income inequality in adolescent aggression and violence. Journal of Adolescent Health. 2014; 55(4):571-579
Assari S, Caldwell CH. High risk of depression in high-income African American boys. Journal of Racial and Ethnic Health Disparities. 2018; 5(4):808-819
Assari S, Boyce S, Caldwell CH, Bazargan M. Minorities’ diminished returns of parental educational attainment on adolescents’ social, emotional, and behavioral problems. Children. 2020; 7(5):49
Harrist AW, Criss MM. Parents and peers in child and adolescent development: Preface to the special issue on additive, multiplicative, and transactional mechanisms. Children. 2021; 8(10):831
Assari S, Boyce S, Bazargan M, et al. Parental educational attainment, the superior temporal cortical surface area, and reading ability among American children: A test of marginalization-related diminished returns. Children (Basel). 2021; 8(5)
Assari S, Boyce S, Bazargan M. Subjective socioeconomic status and children’s amygdala volume: Minorities' diminish returns. NeuroScience. 2020; 1(2):59-74
Boyce S, Darvishi M, Marandi R. Rahmanian R, Akhtar S, Assari S, Racism-Related Diminished Returns of Socioeconomic Status on Adolescent Brain and Cognitive Development. Research in Health Science. 2021; 6(4):1. DOI: 10.22158/rhs.v6n4p1
Assari S. Race, ethnicity, family socioeconomic status, and children’s hippocampus volume. Research in Health Science. 2020; 5(4):25
Assari S. Parental education, household income, and cortical surface area among 9-10 years old children: Minorities' diminished returns. Brain Sciences. 2020; 10(12)
Assari S, Boyce S, Saqib M, Bazargan M, Caldwell CH. Parental education and left lateral orbitofrontal cortical activity during N-back task: An fMRI study of American adolescents. Brain Sciences. 2021; 11(3). DOI: 10.1155/2015/274056
Mackey AP, Finn AS, Leonard JA, et al. Neuroanatomical correlates of the income-achievement gap. Psychological science. 2015; 26(6):925-933
Karcher NR, O’Brien KJ, Kandala S, Barch DM. Resting-state functional connectivity and psychotic-like experiences in childhood: Results from the adolescent brain cognitive development study. Biological Psychiatry. 2019; 86(1):7-15
Rakesh D, Zalesky A, Whittle S. Similar but distinct–Effects of different socioeconomic indicators on resting state functional connectivity: Findings from the Adolescent Brain Cognitive Development (ABCD) Study®. Developmental Cognitive Neuroscience. 2021; 51:101005
Su M, Li P, Zhou W, Shu H. Effects of socioeconomic status in predicting reading outcomes for children: The mediation of spoken language network. Brain and Cognition. 2021; 147:105655
Casey B, Cannonier T, Conley MI, et al. The adolescent brain cognitive development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience. 2018; 32:43-54
Research A, Staff CRE. NIH’s Adolescent Brain Cognitive Development (ABCD) study. Alcohol Research: Current Reviews. 2018; 39(1):97
Auchter AM, Mejia MH, Heyser CJ, et al. A description of the ABCD organizational structure and communication framework. Developmental Cognitive Neuroscience. 2018; 32:8-15
Garavan H, Bartsch H, Conway K, et al. Recruiting the ABCD sample: Design considerations and procedures. Developmental Cognitive Neuroscience. 2018; 32:16-22
Lawson GM, Hook CJ, Hackman DA, et al. Socioeconomic status and neurocognitive development: Executive function. Executive Function in Preschool Children: Integrating Measurement, Neurodevelopment, and Translational Research. 2014:1-28
Lawson GM, Camins JS, Wisse L, et al. Childhood socioeconomic status and childhood maltreatment: Distinct associations with brain structure. PloS One. 2017; 12(4):e0175690
Leijser LM, Siddiqi A, Miller SP. Imaging evidence of the effect of socio-economic status on brain structure and development. In: Paper Presented at: Seminars in Pediatric Neurology. 2018
Lawson GM, Duda JT, Avants BB, Wu J, Farah MJ. Associations between children’s socioeconomic status and prefrontal cortical thickness. Developmental Science. 2013; 16(5):641-652
Richels CG, Johnson KN, Walden TA, Conture EG. Socioeconomic status, parental education, vocabulary and language skills of children who stutter. Journal of Communication Disorders. 2013; 46(4):361-374
Van Houdt CA, van Wassenaer-Leemhuis AG, Oosterlaan J, van Kaam AH, Aarnoudse-Moens CS. Developmental outcomes of very preterm children with high parental education level. Early Human Development. 2019; 133:11-17
Duncan GJ, Magnuson K. Socioeconomic status and cognitive functioning: Moving from correlation to causation. Wiley Interdisciplinary Reviews: Cognitive Science. 2012; 3(3):377-386
Kalil A, Ryan R, Corey M. Diverging destinies: Maternal education and the developmental gradient in time with children. Demography. 2012; 49(4):1361-1383
Guryan J, Hurst E, Kearney M. Parental education and parental time with children. Journal of Economic Perspectives. 2008; 22(3):23-46
Davis-Kean PE. The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment. Journal of Family Psychology. 2005; 19(2):294
Assari S, Caldwell CH, Zimmerman MA. Family structure and subsequent anxiety symptoms; minorities’ diminished return. Brain Sciences. 2018; 8(6):97
Herrnstein RJ, Murray C. The Bell Curve: Intelligence and Class Structure in American Life. Simon and Schuster. New York: Free Press. 1994