Socioeconomic disadvantage, brain morphometry, and attentional bias to threat in middle childhood

  • Alexander J. DuffordEmail author
  • Hannah Bianco
  • Pilyoung Kim


Socioeconomic disadvantage is associated with higher rates of psychopathology as well as hippocampus, amygdala and prefrontal cortex structure. However, little is known about how variations in brain morphometry are associated with socio-emotional risks for mood disorders in children growing up in families experiencing low income. In the current study, using structural magnetic resonance imaging, we examined the relationship between socioeconomic disadvantage and gray matter volume in the hippocampus, amygdala, and ventrolateral prefrontal cortex in a sample of children (n = 34) in middle childhood. Using an affective dot probe paradigm, we examined the association between gray matter volume in these regions and attentional bias to threat, a risk marker for mood disorders including anxiety disorders. We found that lower income-to-needs ratio was associated with lower bilateral hippocampal and right amygdala volume, but not prefrontal cortex volumes. Moreover, lower attentional bias to threat was associated with greater left hippocampal volume. We provide evidence of a relationship between income-related variations in brain structure and attentional bias to threat, a risk for mood disorders. Therefore, these findings support an environment-morphometry-behavior relationship that contributes to the understanding of income-related mental health disparities in childhood.


Family income Hippocampus Amygdala Attentional bias Middle childhood Morphometry 



This work was supported by the National Institute of Child Health and Human Development [R21HD078797; R01 HD090068]; the Professional Research Opportunity for Faculty (PROF) and Faculty Research Fund (FRF), University of Denver; and the Victoria S. Levin Award For Early Career Success in Young Children's Mental Health Research, Society for Research in Child Development (SRCD). Special thanks to Daniel Mason, Christian Capistrano, Laura Jeske, Daniel Bartholomew, Naomi Wallace, Nanxi Xu, and Christina Congleton for their help in recruitment, data collection, and data entry. The authors thank Christopher Madan for his guide on visualization of the MRI data (, Dr. Sarah Watamura for her insightful comments on the manuscript, as well as Dr. Cathy Durso, Dr. Kimberly Chiew, and Dr. Peter Sokol-Hessner for their comments on the statistical analyses. The authors also thank Rebekah Tribble for her editorial comments. The authors declare that they have no conflicts of interest in the research.

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© Psychonomic Society, Inc. 2018

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of DenverDenverUSA

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