Association of Inflammatory Activity With Larger Neural Responses to Threat and Reward Among Children Living in Poverty

Objective:Children exposed to severe, chronic stress are vulnerable to mental and physical health problems across the lifespan. To explain how these problems develop, the neuroimmune network hypothesis suggests that early-life stress initiates a positive feedback loop between peripheral inflammatory cells and networked brain regions involved in threat and reward processing. The authors sought to test this hypothesis by studying a sample of urban children from diverse socioeconomic backgrounds.Methods:The authors examined the basic predictions of the neuroimmune network hypothesis in 207 children (mean age=13.9 years, 63% female; 33% Black; 30% Hispanic), focusing on poverty as a stressor. The children had fasting blood drawn to quantify five inflammatory biomarkers—C-reactive protein, tumor necrosis factor-α, and interleukins-6, -8, and -10—which were averaged to form a composite score. Children also completed two functional MRI tasks, which measured amygdala responsivity to angry facial expressions and ventral striatum responsivity to monetary rewards.Results:Poverty status and neural responsivity interacted statistically to predict inflammation. Among children living in poverty, amygdala threat responsivity was positively associated with inflammation, and the same was true for ventral striatum responsivity to reward. As children’s socioeconomic conditions improved, these brain-immune associations became weaker. In sensitivity analyses, these patterns were robust to alternative measures of socioeconomic status and were independent of age, sex, racial and ethnic identity, and pubertal status. The associations were also condition specific; no interactions were apparent for amygdala responsivity to neutral faces, or striatal responsivity to monetary losses.Conclusions:These findings suggest that childhood poverty is associated with accentuated neural-immune signaling, consistent with the neuroimmune network hypothesis.