Imagine a giant puzzle with pieces labeled socioeconomic status (SES), brain structure, and cognition. This groundbreaking research delves into the intricate connections between these pieces using mechanistic models of development. Like detectives on a case, scientists are investigating how SES influences brain measures such as cortical surface area and thickness, which in turn impact cognitive skills like executive function and language. But it’s not a simple task, my curious friends! SES is a complex concept that involves environmental factors like family income and parental education. The challenge is to unravel the causal pathways linking SES, brain development, and behavior across different levels of description and even genetic influences. Why do we pursue this intricate web of connections, you ask? Well, solving this puzzle can guide policies and interventions to level the developmental playing field. Imagine if by understanding the specific pathways between SES and brain development, we could design effective interventions to bridge developmental gaps! Spoiler alert: this article explores one specific example in depth – language development – using a multiscale computational model based on an artificial neural network. By connecting empirical data on SES effects with MRI measurements of brain structure, researchers can explore different causal pathways. They examine the impact of prenatal brain development versus postnatal cognitive stimulation on developmental outcomes. The results reveal the cumulative effects of social disadvantage on multiple pathways and emphasize the necessity of effective interventions. If you’re ready to dive into the complexities of how SES shapes brain structure and cognition, join us on this exciting scientific journey!

Abstract
Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences.

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