Integrating Environmental Systems to Predict Subcortical Volume and Psychopathology in Youth
Poster No:
983
Submission Type:
Abstract Submission
Authors:
Jivesh Ramduny1, Samuel Paskewitz1, Inti Brazil2, Arielle Baskin-Sommers1
Institutions:
1Yale University, New Haven, CT, 2Radboud University, Nijmegen, Nijmegen
First Author:
Co-Author(s):
Introduction:
Environmental factors have long been shown to influence structural brain development and adolescent psychopathology (Barch, 2022; Brito, 2014; Maxwell, 2023; Tooley, 2021; Weissman, 2023). However, almost no research has included environmental factors spanning micro-to-macro-systems, brain structure, and psychopathology in an integrated framework. Here, we assessed the ways and degree to which multi-system environmental factors during late childhood predict subcortical gray matter (GM) volume and psychopathology during early adolescence.
Methods:
We used baseline (9-10 years) and 2-year follow-up (11-12 years) data from the Adolescent Brain Cognitive Development Study (N=2766) (Casey, 2018). The environmental factors were obtained at baseline and captured multiple systems related to family (family conflict, parenting style, income-to-needs ratio), school (school involvement), neighborhood (neighborhood deprivation, neighborhood safety and crime, residential segregation), and policy (marijuana laws). The subcortical GM structures were labeled using an automated, atlas-based, volumetric segmentation procedure at 2-year follow-up (Hagler, 2019). We obtained psychopathology data at 2-year follow-up from the Child Behavior Checklist (Achenbach, 2001). We employed a novel Bayesian latent profile analysis (LPA) to obtain distinct multi-system environment profiles during late childhood without a priori specifying the desired number of profiles (Paskewitz, 2024). The profiles were used in a path analysis to predict their direct and indirect effects on subcortical GM volume and psychopathology during early adolescence. For direct effects, the coefficient estimates, standardized errors (SE), and statistical significance were reported. For indirect effects, the coefficient estimates and 95% confidence intervals (CI), which were obtained from a bootstrapping procedure, were reported.
Results:
Bayesian LPA revealed 9 environmental profiles with excellent certainty and discrimination (entropy=0.90) (Fig.1). Two distinct profiles predicted greater externalizing problems in adolescents (Fig.2): (i) adversity across school and neighborhood systems (estimate [SE]=0.26 [0.095], P=0.006), and (ii) family conflict and low school involvement (estimate [SE]=0.48 [0.14], P=0.001). In contrast, a profile of family and neighborhood affluence predicted fewer externalizing difficulties (estimate [SE]=-0.16 [0.060], P=0.009). Further, we found differences in the way multi-system environment profiles predicted externalizing psychopathology via subcortical GM volume (Fig.2). The first pathway showed that family and neighborhood affluence predicted higher subcortical GM volume, which in turn, predicted fewer externalizing problems (estimate=-0.006, 95% bootstrap CI=[-0.020, -0.00032]). The second pathway indicated that family economic and neighborhood adversity predicted lower subcortical GM volume, which in turn, predicted greater externalizing difficulties (estimate=0.012, 95% bootstrap CI=[0.0012, 0.036]).
Conclusions:
The Bayesian LPA produced reliable profiles with more subtle variations in childhood environmental experiences-which is difficult to obtain with conventional techniques due to the tradeoff between number of participants in a sample and number of profiles that produces meaningful discrimination. We captured direct and indirect influences of environmental factors across multiple systems on externalizing psychopathology. Specifying the equifinal pathways to externalizing psychopathology provides an evidence base for establishing different types of interventions based on the needs and risk profiles of youth.
Lifespan Development:
Early life, Adolescence, Aging 1
Normal Brain Development: Fetus to Adolescence
Modeling and Analysis Methods:
Bayesian Modeling 2
Classification and Predictive Modeling
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Development
STRUCTURAL MRI
Sub-Cortical
1|2Indicates the priority used for review
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[2] Brito, N.H. (2014). Socioeconomic status and structural brain development. Frontiers in Neuroscience, 8:276
[3] Maxwell, M.Y. (2023). Relationship Between Neighborhood Poverty and Externalizing Symptoms in Children: Mediation and Moderation by Environmental Factors and Brain Structure. Child Psychiatry and Human Development, 54(6):1710-1722
[4] Tooley, U.A. (2021). Environmental influences on the pace of brain development. Nature Reviews Neuroscience, 22(6):372-384
[5] Weissman, D.G. (2023). State-level macro-economic factors moderate the association of low income with brain structure and mental health in U.S. children. Nature Communications, 14(1):2085
[6] Casey, B.J. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32:43-54
[7] Hagler, D.J.Jr, (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. Neuroimage, 202:116091
[8] Achenbach, T.M. (2001). Manual for the ASEBA school-age forms & profiles: an integrated system of multi-informant assessment.Burlington: University of Vermont, Research Center for Children, Youth & Families
[9] Paskewitz, S. (2024). Enhancing within-person estimation of neurocognition and the prediction of externalizing behaviors in adolescents. Computational Psychiatry, 8(1):119-141