Poverty Shrinks the Brain

Socioeconomic status has important effects on brain size and organization.

Posted Nov 25, 2020

Socioeconomic status (SES) is based on factors such as family income, parents’ education and neighborhood poverty. A low SES in childhood is associated with poor outcomes in adulthood across many domains. How and why SES disadvantage sets the stage for negative outcomes in adulthood has been recently uncovered by brain scientists.

On the other hand, a factor that predicts positive outcomes in life is high self-control. Self-control is the capacity to regulate behaviors, thoughts and emotions. This general capacity permeates many skills necessary for success, such as decision-making and emotion regulation. It subsumes many abilities such as response inhibition, which is the ability to suppress inappropriate responses. For example, someone who has poor response inhibition may find it difficult to resist the urge to tell a dirty joke at a corporate meeting. The ability to suppress unsuitable responses has been related to adult well-being (1). Brain activation in the inferior frontal gyrus (IFG) is critical for response inhibition and greater activation in this area predicts inhibition of inappropriate responses (2).

The two constructs, response inhibition performance and SES, are intimately related. In one study, family income, maternal education and neighborhood poverty affected performance on a response inhibition measure (3). The task required that children press a button when a mole appeared on the screen (whack the mole by pressing the button) and inhibit pressing the button when a vegetable appeared on the screen (not to whack an eggplant). This is similar to responding in a classroom only when a teacher chooses the child and not blurting out answers when the teacher selects another student.

The children performed this task while their brains were scanned. Inhibition trials (vegetable) were less frequent than GO trials (mole), thus it was harder to suppress responses. Both accuracy and reaction times were taken into account. Children who enjoyed higher family income, more educated mothers and enriched neighborhoods performed better on the response inhibition task. Only neighborhood poverty predicted response inhibition performance by affecting neural activity in the IFG. In other words, children living in poorer neighborhoods showed lower IFG activation during the response inhibition task and therefore performed poorly.

Another study investigating neighborhood deprivation, prefrontal cortex and general neurocognition in children and adolescents showed similar results (4). The prefrontal cortex (PFC) was related to neighborhood deprivation and neurocognition. In fact, PFC size explained a portion of the relationship between neighborhood and neurocognition. It is sobering to concretely "see" the deleterious effects of impoverished neighborhoods on important neural activity in the brain.

The damaging effects of low SES are not limited to IFG. A study focusing on the effects of income on academic achievement suggests that the damage extends to many parts of the brain. The income-achievement gap — the difference in academic performance between students from low-income and high-income backgrounds — has widened over the last few years. A kindergarten student living in poverty is likely to have cognitive scores 60% lower than ones living in more affluent families (5).

Unfortunately, this has implications for brain development. In one study, the researchers correlated cortical gray matter volume and thickness with income and test performance on statewide standardized testing (6). Cortical thickness across all brain lobes was greater in students from higher-income than lower-income backgrounds. And brain thickness was positively related to test performance. This is not surprising given that the cortex supports memory, language, decision-making and intelligence. Astonishingly, cortical thickness in some brain areas explained 44% of the income-achievement gap!

Another study scanned 1,099 participants aged 3 to 20 years. Their findings were similar: Income was positively associated with brain surface area (7). They reported two additional key findings. First, the income-brain surface area relationship remained even when controlling for age, sex, race and ethnicity. Second, the link between income and surface area was much more robust in children from low-income backgrounds. In other words, for children from low-income families, smaller differences in income were associated with large differences in brain surface areas. However, for children from high-income families, smaller increases in income were not as meaningful. This makes sense; giving a family living in poverty an extra $500 per week would be more meaningful than giving the same amount to a family making $200,000 per year. Although income was related to many cortical areas, it was especially related to brain parts important for language, impulse control, spatial abilities and other neurocognitive abilities. 

Does this bleak relationship dissipate as children get older? A recent study suggests that the relationship between SES and the brain remains across a range of life-span stages (20 to 89 years) (8). A particular stage seems to be vulnerable to the damaging effects of lower SES, namely middle age (35 to 64 years). Along the same vein, lower SES is associated with reduced cortical gray matter thickness in this stage. The SES effect on thinning the brain remained after controlling for physical and mental health, cognitive ability and participants’ demographics. But maybe the thinning of the brain was a result of childhood poverty? Not so! The researchers found that participants’ childhood SES was not causing the relationship between SES in adulthood and brain pathologies.

It seems like lower SES in middle-aged people is associated with reduced brain efficiency (8). The brain contains many functional networks that are segregated in organization. The segregation in the brain networks enables specialization and efficient communication across these networks (9). As we get older, the networks become less segregated and this reduction in specialization is related to a decline in cognition as we get older. SES can accelerate the de-segregation of brain systems leading to poor cognitive performance and abilities and exacerbate age-related cognitive decline.

In sum, the above studies strongly suggest that SES can have a profound effect on brain health. These impacts are inflicted by inequality. Higher SES is a neuroprotective factor across all developmental stages from childhood to adolescence to adulthood. It would be negligent for policymakers to ignore the troubling effects of low SES. One solution that has been proposed is to give monthly cash to mothers shortly after they give birth. In an ongoing study, some mothers will be randomly assigned to receive several hundred dollars each month and some will be assigned to receive a nominal cash gift. The results of this recent study are expected to provide a concrete action to close the gap. Finding solutions is imperative, given the traumatic effects of poverty on the brain.


(1) Young, S.E., Friedman, N.P., Miyake, A., Willcutt, E.G., Corley, R.P., Haberstick, B.C., Hewitt, J.K., 2009. Behavioral disinhibition: liability for externalizing spectrum disorders and its genetic and environmental relation to response inhibition across adolescence. J. Abnorm. Psychol. 118 (1), 117–130. https://doi.org/10.1037/ a0014657.

(2) Aron, A.R., Poldrack, R.A., 2006. Cortical and subcortical contributions to Stop signal response inhibition: role of the subthalamic nucleus. J. Neurosci. 26 (9), 2424–2433. https://doi.org/10.1523/JNEUROSCI.4682-05.2006.

(3) Tomlinson, R.C., Burt, S.A., Waller, R. et al. (2020). Neighborhood poverty predict altered neural and behavioral response inhibition. NeuroImage, 209. https://doi.org/10.1016/j.neuroimage.2020.116536.

(4) Vargas, T., Damme, K.S.F. & Mittal, V.A. (2020). Neighborhood deprivation, prefrontal morphology and neurocognition in late childhood to early adolescence. NeuroImage, https://doi.org/10.1016/j.neuroimage.2020.117086.

(5) https://files.eric.ed.gov/fulltext/ED522775.pdf

(6) Mackey, A., Finn, A.S., et al. (2015). Neuroanatomical Correlates of the Income-Achievement Gap. Psychological Science, 26(6), 925-933.

(7) Family income, parental education and brain structure in children and adolescents," Noble, K.G., Houston, S.M., Bartsch, H., Kan, E., Kuperman, J.M., Akshoomoff, N., Amaral, D.G., Bloss, C.S., Libiger, O., Schork, N.J., Murray, S.S., Casey, B.J., Chang, L., Ernst, T.M., Frazier, J.A., Gruen, J.R., Kennedy, D.N., Van Zijl, P., Mostofsky, S., Kaufmann, W.E., Keating, B.G., Kenet, T., Dale, A.M., Jernigan, T.L., & Sowell, E.R. for the Pediatric Imaging, Neurocognition, and Genetics Study, Nature Neuroscience, doi:10.1038/nn.3983, 2015.

(8) Chan, M.Y., Na, J., et al. (2018). Socioeconomic status moderates age-related differences in the brain's functional network organization and anatomy across the adult lifespan, PNAS, 115 (22), www.pnas.org/cgi/doi/10.1073/pnas.1714021115.

(9) Sporns O, Betzel RF (2016). Modular brain networks. Annu Rev Psychol 67:613–640.