On this page you will find the script corresponding to the paper “Making Connections: Neurodevelopmental Changes in Brain Connectivity after Adverse Experiences in Early Adolescence” by Ayla Pollmann, Remo Sasso, Kathryn Bates and Delia Fuhrmann.
Please cite the paper if you are re-using some of the code provided here.
Abstract: Adverse childhood experiences have been linked to detrimental mental health outcomes in adulthood. This study investigates a potential neurodevelopmental pathway between adversity and mental health outcomes: brain connectivity.
We used data from the prospective, longitudinal Adolescent Brain Cognitive Development (ABCD) study (N ≈ 12.000, participants aged 9-13) and assessed structural brain connectivity using fractional anisotropy (FA) of white matter tracts. The adverse life experiences modelled included family conflict and traumatic experiences. K-Means clustering, and Latent Basis Growth Models (LBGM), were used to determine subgroups based on total levels and trajectories of brain connectivity, and multinomial regression was used to determine associations between cluster membership and adverse experiences.
Results showed that higher family conflict was associated with higher FA levels across brain tracts (e.g., t(3) = -3.81, β = -0.09, pbonf = .003) and within the corpus callosum (CC), Fornix and anterior thalamic radiations (ATR). A decreasing FA trajectory across two brain imaging timepoints was linked to lower socioeconomic status and neighbourhood safety. Socioeconomic status was related to FA across all brain tracts (e.g., t(3) = 3.44, β = 0.10, pbonf = .01), the CC and the ATR. Neighbourhood safety was associated with FA in the Fornix and ATR (e.g., t(1) = 3.48, β = 0.09, pbonf = .01).
There is a complex and multifaceted relationship between adverse experiences and brain development, where adverse experiences during early adolescence are related to brain connectivity. These findings underscore the importance of studying adverse experiences beyond early childhood to understand lifespan developmental outcomes.
Overview
To examine individual differences, we used Latent Basis Growth Modelling and K-means clustering in an exploratory analysis. We determined subpopulations based on the trajectory and total level of FA across all brain tracts, the corpus callosum, the fornix and the anterior thalamic radiations. This page includes the following analyses:
Latent Basis Growth Curve Modelling.
For K-means: The optimal number of clusters based on the Silhouette and Elbow method.
K-means clustering and multinomial logistic regression to predict cluster membership based on adversity experiences at baseline per brain region.
Ayla Pollmann - 2023