Socio-economic disadvantage is associated with alterations in brain wiring economy

Abstract

AbstractThe quality of a child’s social and physical environment is a key influence on brain development, educational attainment and mental wellbeing. However, there still remains a mechanistic gap in our understanding of how environmental influences converge on changes in the brain’s developmental trajectory. In a sample of 145 children with structural diffusion tensor imaging data, we used generative network modelling to simulate the emergence of whole brain network organisation. We then applied data-driven clustering to stratify the sample according to socio-economic disadvantage, with one of the resulting clusters containing mostly children living below the poverty line. A formal comparison of the simulated networks from the generative model revealed that the computational principles governing network formation were subtly different for children experiencing socio-economic disadvantage, and that this resulted in significantly altered developmental timing of network modularity emergence. Children in the low socio-economic status (SES) group had a significantly slower time to peak modularity, relative to the higher SES group (t(69) = 3.02, P = 3.50 × 10-4, d = 0.491). In a subsequent simulation we showed that the alteration in generative properties increases the variability in wiring probabilities during network formation (KS test: D = 0.012, P < 0.001). One possibility is that multiple environmental influences such as stress, diet and environmental stimulation impact both the systematic coordination of neuronal activity and biological resource constraints, converging on a shift in the economic conditions under which networks form. Alternatively, it is possible that this stochasticity reflects an adaptive mechanism that creates “resilient” networks better suited to unpredictable environments.Author SummaryWe used generative network models to simulate macroscopic brain network development in a sample of 145 children. Within these models, network connections form probabilistically depending on the estimated “cost” of forming a connection, versus topological “value” that the connection would confer. Tracking the formation of the network across the simulation, we could establish the changes in global brain organisation measures such as integration and segregation. Simulations for children experiencing socio-economic disadvantage were associated with a shift in emergence of a topologically valuable network property, namely modularity.