The brain is an adaptive organ whose development is responsive to its early-life environment. This means that early experiences shape the biological substrates of lifelong health and well-being. Indeed, exposure to significant adversity in childhood increases the risk of a range of cognitive and behavioural difficulties later in life. Previous work has sought to understand this pathway by identifying neural correlates of exposure to poverty and violence, focusing particularly on the morphology of several key cortical and sub-cortical structures. However, region-specific correlations alone are not enough to generate a coherent account of the impact of child adversity. This thesis aims to address this gap by conducting a global, causal, and mechanistic investigation of the impact of adverse experiences in childhood, leveraging network science methods and computational modelling to accommodate more of the complexity inherent in the nature of childhood experiences and development.

In Chapter 2, I aim to ascertain whether early-life adversity alters the development of rodent structural brain organisation. Using imaging data obtained from collaborators at Yale University, I first reconstruct the structural connectomes of a sample of mice, half of which were exposed to a paradigm of unpredictable postnatal stress. I then simulate the development of each connectome using generative network modelling, a computational approach that generates complex networks probabilistically based on a trade-off between the cost and value of connections. After validating the quality of the simulations, I conduct a case-control comparison of the models that best replicate the brain networks of adversity-exposed and unexposed mice, and explore potential implications of an observed difference in model parameters. I conclude that early adversity may cause an increase in stochasticity in the formation of the structural connectome.

In Chapter 3, I aim to establish a global understanding of the nature of early adverse experiences as they occur in the general population. The analysis tests a prominent theory within the field, the Dimensional Model of Early Adversity (McLaughlin, Sheridan, & Lambert, 2014). Using data from a longitudinal cohort study, I apply a data-driven network and clustering approach to identify dimensions of adversity, or groups of adverse experiences that impact children in similar ways. Contrary to my pre-registered hypotheses—and prior work on the dimensional model—I show that a range of deprivation-related experiences are closely related to later cognitive and socioemotional difficulties. I conclude that deprivation is a broad and robust predictor of later psychological outcomes, and that theories of early adversity would benefit from triangulation of findings through diverse methodologies.

In Chapter 4, I aim to replicate findings from Chapter 2 in humans, and to extend them by determining which early experiences are most predictive of brain wiring in young adulthood and by assessing the relationship between model parameters and cognitive and socioemotional difficulties. I first reconstruct and simulate the development of the structural connectomes of a subsample of participants from the longitudinal cohort used in Chapter 3. A data-driven partial least squares (PLS) regression shows that no early experiences of adversity predict brain wiring parameters in young adulthood. Sensitivity analyses confirm the robustness of this null finding, for which I find some previous support in the literature.

Finally, in the General Discussion I delineate broader conclusions that can be drawn from this work, acknowledge its overarching limitations, and suggest promising next steps in the study of early adversity. I conclude with an addendum on the application of neuroscience research to public education and policy, in which I warn against neuroscience overreach and make specific proposals for improving the integrity, quality, and utility of the scientific study of childhood adversity.