How developmental neuroscience can help address the problem of child poverty

Abstract

Nearly 1 in 5 children in the United States lives in a household whose income is below the official federal poverty line, and more than 40% of children live in poor or near-poor households. Research on the effects of poverty on children's development has been a focus of study for many decades and is now increasing as we accumulate more evidence about the implications of poverty. The American Academy of Pediatrics recently added "Poverty and Child Health" to its Agenda for Children to recognize what has now been established as broad and enduring effects of poverty on child development. A recent addition to the field has been the application of neuroscience-based methods. Various techniques including neuroimaging, neuroendocrinology, cognitive psychophysiology, and epigenetics are beginning to document ways in which early experiences of living in poverty affect infant brain development. We discuss whether there are truly worthwhile reasons for adding neuroscience and related biological methods to study child poverty, and how might these perspectives help guide developmentally based and targeted interventions and policies for these children and their families.

Keywords: brain; child poverty; development; socioeconomic status.

Figure 1.

Among all children under 18 years in the United States, 41% are low-income children and 19% –approximately one in five – are poor. This means that children are overrepresented among our nation’s poor; they represent 23% of the population but comprise 32% of all people in poverty. Many more children live in families with incomes just above the poverty threshold. The percentage of low-income children under age 18 years surpasses the percentage of low-income adults. © National Center for Children in Poverty (www.nccp.org) Basic Facts about Low-Income Children: Children under 18 Years, 2016 Reprinted with permission

Figure 2.

Brain regions that appear to consistently show negative associations between child poverty and gray matter development.

Figure 3.

Differences in trajectory of brain growth among infants from low (blue), middle (red) and high (green) income families. There is no statistical difference between the growth rates of those from middle and high income families. Reprinted from: Hanson et al. (2013).

Figure 4.

Data from Hair et al. (2015) is used to show the relationships between low family income, children’s brain growth, and children’s subsequent performance on Math Computation and Reading Comprehension achievement tests.

Figure 5.

Volumetric comparisons for the left amygdala (panel a) and hippocampus (Left hippocampus shown in Panel b; Right hippocampus in Panel c). For each graph, standardized residuals controlling for total gray matter, pubertal stage, and sex are shown on the vertical axis, while group is shown on the horizontal axis. In the bottom corner of the figure are example hand-tracings of the amygdala (outlined in red) and hippocampus (outlined in blue). Reprinted from Hanson et al. (2015) with permission.