No Brain Gets Left Behind

This article is authored by Dominic Tran with the mentorship of Sarah M. Tashjian and is a part of the 2018 pre-graduate spotlight week.

For most kids in the United States, formal schooling begins at the age of 5 as they enter elementary school and learn the essential skills of reading, writing, and mathematics. Although most students start formal schooling around the same age, student performance is already disparate due to complications related to socioeconomic status (SES) (Lee & Burkam, 2002).  SES is typically defined as the social standing of an individual and is measured through a combination of factors such as education, income and occupation. Recent studies suggest that children from low SES households are already at a disadvantage even before they start school due to differences in language processing skills and vocabulary (Fernald et al., 2014). For example, reported average cognitive scores of children in the highest SES group (defined as: top 20%) are 60% above the scores of the lowest SES group (defined as: bottom 20%) (Lee & Burkam, 2002). These results remain constant even when taking other demographic differences such as race into account. As students reach higher grade levels, other studies have also found differences in brain development that may mediate the influence of poverty on children’s learning and achievement such as functioning in the brain, specifically regions of the frontal lobe, temporal lobe, and hippocampus  (Hair et al., 2015). Although it may seem that lower SES students are at a disadvantage early in life, childhood interventions that promote less stressful households and are aimed at improving children’s executive function may offset the effects of low SES on academic achievement (Diamond & Lee, 2007).

Although children from economically disadvantaged families start kindergarten with lower language and cognitive skills than those from more advantaged families, this difference is observable as early as 18 months of age (Fernald et al., 2014). A study from Stanford University found that there is a significant difference in number of words known and language processing efficiency at 18 months of age, and by 24 months, there was a six-month gap between SES groups in processing skills critical to language development (Fernald et al., 2014). Researchers attributed this difference to environmental differences related to SES. For example, physical conditions of everyday life related to safety, sanitation, and noise level conditions tend to differ for children in lower and higher SES families. These living environments as well as exposure to higher levels of stress may play a role in the development of language and cognitive skills in children.

 SES differences in language processing skill and vocabulary are evident at 18 months (Fernald, et al., 2014)

SES differences in language processing skill and vocabulary are evident at 18 months (Fernald, et al., 2014)

As children of low SES households progress through school, other studies found that these children will have lower scores on standardized testing, poorer grades, and lower overall educational attainment (Duncan & Brooks-Gunn, 1997; Havemen & Wolfe, 1995). Because of these findings, one study looked at how systematic differences in the brain mediated the association between SES and educational performance. Using a longitudinal design, researchers followed children and adolescents from age 4 to 22 years and collected neuroimaging data that could provide information about the overall brain structure of individuals from different SES households (Hair et al., 2015). The study found that regional grey matter volumes of children below 1.5 times the federal poverty level were 3 to 4 percentage points below the developmental norm (p < 0.5). These grey matter differences were correlated with students scoring 4 to 7 points below the average for standardized testing. The specific decreases in grey matter were found in regions of the frontal lobe, temporal lobe, and the hippocampus. Each of these regions are believed to be crucial for school readiness. For example, the frontal lobe is considered important for top-down control of complex learning, the temporal lobe is important for memory and language comprehension, and the hippocampus is important for processing spatial and contextual information that is required for long-term memory functioning (Fuster, 2001; Jobard, Crivello and Tzourio-Mazoyer, 2003; Squire, 1992). These results suggest that a possible reason for why many students from low SES households are unable to meet educational standards is due to atypical structural development of several areas in the brain that are crucial for learning. With neurobiological disparities between children of different SES households, researchers of the study propose that environmental factors that are characteristic of low SES homes may have facilitated these structural differences which ultimately affect academic performance. As a result, policy concerning programs that aim to improve the living environment of low SES children is largely needed to close the academic achievement gap.

Despite neurobiological evidence for the academic achievement gap between high and low SES children, several studies have shown that the effects of low SES can be offset by modifications in the home environment (Hackman & Farah, 2009). Children from lower SES households are often exposed to more stressful and less cognitively stimulating environments that may affect their academic performance (McLoyd, 1998; Evans, 2004). Their environments may be characterized by crowding, family instability, and inconsistent disciplinary practices. Family-based training programs have been used in an attempt to improve the home environment for low SES children. One study administered such a program to lower SES preschoolers that improved many school readiness skills such as receptive language skills and nonverbal IQ (Neville et al., 2013). Features of the program involved training sessions for caregivers as well as attention training for children. Furthermore, a program called Tools of the Mind was incorporated into the curriculum of a preschool classrooms that improved students’ executive functioning, which is crucial for success in school and life (Diamond & Lee, 2007). Simple changes to the classroom such as having students play an active role in planning out their day and verbalizing what they are going to do improved their performance on tasks that measured their executive functioning compared to students under a typical preschool curriculum. With the implementation of more programs that assist in promoting school readiness skills, students who come from lower SES households may be able to perform just as well as their higher SES peers.

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Dominic is a 4th year Psychobiology major at UCLA who is passionate about working with adolescents and children. The brain has always been a great interest to him and has led him pursue many activities such as volunteering at the Galvan Lab and teaching as part of UCLA InterAxon. After taking Dr. Galván's course on Behavior and Brain Development, he became particularly interested in how socioeconomic status impacts brain development in children. Currently, Dominic is working towards a career in medicine and is interested in the fields of pediatrics and neurology. In his spare time, he likes to practice his Spanish, hone his cooking skills, and swim.

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References

Brooks-Gunn, J. and Duncan, G. (1997). The Effects of Poverty on Children. The Future of Children, 7(2), p.55.

Diamond, A. and Lee, K. (2011). Interventions Shown to Aid Executive Function Development in Children 4 to 12 Years Old. Science, 333(6045), pp.959-964.

Evans, G. (2004). The Environment of Childhood Poverty. American Psychologist, 59(2), pp.77-92.

Fernald, A., Marchman, V. and Weisleder, A. (2012). SES differences in language processing skill and vocabulary are evident at 18 months. Developmental Science, 16(2), pp.234-248.

Fuster, J. (2001). The Prefrontal Cortex—An Update. Neuron, 30(2), pp.319-333.

Hackman, D. and Farah, M. (2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences, 13(2), pp.65-73.

Hair, N., Hanson, J., Wolfe, B. and Pollak, S. (2015). Association of Child Poverty, Brain Development, and Academic Achievement. JAMA Pediatrics, 169(9), p.822.

Haveman, R. and Wolfe, B. (1995). The Determinants of Children's Attainments: A Review of Methods and Findings. Journal of Economic Literature, [online] 33(4), pp.1829–1878. Available at: http://www.jstor.org/stable/2729315 [Accessed 10 Apr. 2018].

Jobard, G., Crivello, F. and Tzourio-Mazoyer, N. (2003). Evaluation of the dual route theory of reading: a metanalysis of 35 neuroimaging studies. NeuroImage, 20(2), pp.693-712.

Lee, V. and Burkam, D. (2002). Inequality at the starting gate. Washington, D.C.: Economic Policy Institute.

McLoyd, V. (1998). Socioeconomic disadvantage and child development. American Psychologist, 53(2), pp.185-204.

Neville, H., Stevens, C., Pakulak, E., Bell, T., Fanning, J., Klein, S. and Isbell, E. (2013). Family-based training program improves brain function, cognition, and behavior in lower socioeconomic status preschoolers. Proceedings of the National Academy of Sciences, 110(29), pp.12138-12143.

Squire, L. (1992). "Memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans": Correction. Psychological Review, 99(3), pp.582-582.