Poster No:
831
Submission Type:
Abstract Submission
Authors:
Olga Boer1, Ingmar Franken2, Ryan Muetzel3, Janna Cousijn4, Hanan El Marroun2
Institutions:
1Erasmus University Rotterdam / Erasmus Medical Center, Rotterdam, South-Holland, 2Erasmus University Rotterdam, Rotterdam, South-Holland, 3Erasmus Medical Center, Rotterdam, Zuid Holland, 4Erasmus University Rotterdam, Rotterdam, Zuid-Holland
First Author:
Olga Boer
Erasmus University Rotterdam / Erasmus Medical Center
Rotterdam, South-Holland
Co-Author(s):
Janna Cousijn
Erasmus University Rotterdam
Rotterdam, Zuid-Holland
Introduction:
Substance use during adolescence, especially when initiated early, has been associated with substance use disorders (SUD) later in life (Huggett et al., 2019; Trujillo et al., 2019). When investigating the neurobiological underpinnings of this association, it is crucial to disentangle the causes from consequences of substance use. As such, identifying pre-existing brain variations allows us to pinpoint brain areas that might be specifically vulnerable to the effects of substance use. While the existing literature has identified pre-existing brain variations as vulnerability markers for substance use initiation (Boer et al., 2022), the role of timing of early initiation is yet unclear. Furthermore, previous studies often relied on small samples, which could lead to inflated effect sizes and false-positive findings. Therefore, the current study employed a population-based prospective study cohort, the Generation R Study (Kooijman et al., 2016), to investigate the association between brain morphology in late childhood (age 10) and very early alcohol and tobacco initiation (age < 13) in adolescence.
Methods:
Brain morphology (gray matter volume, cortical thickness and surface area) in children was assessed two times using magnetic resonance imaging (MRI), around age 10 and age 14. At age 14, participants reported on alcohol and tobacco use initiation. To examine pre-existing brain differences in very early substance use initiators, we used logistic regression to examine the longitudinal association between brain morphology of regions of interest (ROIs) around age 10 and reported initiation (yes/no) of alcohol/tobacco use before the age of 13 (N = 2218). Then, to determine whether any pre-existing differences were not present at age 10, but emerged between the two neuroimaging waves, we examined the cross-sectional association between brain morphology of the ROIs around age 14, and alcohol/tobacco use initiation before age 13 (N = 1817). Separately, we examined both the longitudinal and cross-sectional associations with a surface-based approach (without predefined ROIs) using the R package QDECR (Lamballais & Muetzel, 2021). Sensitivity analyses included rerunning analyses after applying inverse probability weighting, and stratifying the samples by sex. Models were adjusted for age at neuroimaging, sex, maternal ethnicity and education, household income, prenatal alcohol/tobacco exposure, parental psychopathology and history of SUD, and child non-verbal IQ. Missing data on confounders was imputed using multiple imputation by chained equations (van Buuren & Groothuis-Oudshoorn, 2011) and a false discovery rate correction was applied to control for type I errors (Benjamini & Hochberg, 1995).
Results:
No associations were found between brain morphology at both research waves and early alcohol/tobacco use initiation (age < 13), neither in the ROI nor the surface-based analyses. Sex-specific analyses revealed a cross-sectional association between smaller thalamic volume at age 14 and early initiated tobacco use in girls (OR = .26, CI[.11 - .58], p = .001, pFDR = .03), possibly reflecting the emergence of sex-specific pre-existing neural variations between age 10 and 14.
Conclusions:
The present study could not find robust brain morphometry measures of very early (age < 13) alcohol/tobacco initiation in a large population-based sample of young adolescents. This finding is important for interpreting existing research on neurobiological consequences of substance use in the general population. Furthermore, a sex-specific finding for smaller thalamic volume hinted towards differential substance use vulnerability in girls, considering the assumed role of the thalamus in SUD development (Huang et al., 2018). Future longitudinal studies are needed to specify whether these findings can be extended to later initiation and continuation of alcohol and tobacco use in later stages of adolescence.
Emotion, Motivation and Social Neuroscience:
Social Neuroscience Other 1
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 2
Modeling and Analysis Methods:
Univariate Modeling
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Addictions
Cortex
Development
MRI
NORMAL HUMAN
PEDIATRIC
STRUCTURAL MRI
Sub-Cortical
Thalamus
Other - substance use
1|2Indicates the priority used for review
Provide references using author date format
Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing [https://doi.org/10.1111/j.2517-6161.1995.tb02031.x]. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300. https://doi.org/https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Boer, O. D., El Marroun, H., & Franken, I. H. A. (2022). Brain Morphology Predictors of Alcohol, Tobacco, and Cannabis Use in Adolescence: A Systematic Review. Brain Res, 148020. https://doi.org/S0006-8993(22)00244-X
Huang, A. S., Mitchell, J. A., Haber, S. N., Alia-Klein, N., & Goldstein, R. Z. (2018). The thalamus in drug addiction: from rodents to humans. Philos Trans R Soc Lond B Biol Sci, 373(1742). https://doi.org/rstb.2017.0028
Huggett, S. B., Keyes, M., Iacono, W. G., McGue, M., Corley, R. P., Hewitt, J. K., & Stallings, M. C. (2019). Age of initiation and transition times to tobacco dependence: Early onset and rapid escalated use increase risk for dependence severity. Drug Alcohol Depend, 202, 104-110. https://doi.org/S0376-8716(19)30199-1
Kooijman, M. N., Kruithof, C. J., van Duijn, C. M., Duijts, L., Franco, O. H., van, I. M. H., de Jongste, J. C., Klaver, C. C., van der Lugt, A., Mackenbach, J. P., Moll, H. A., Peeters, R. P., Raat, H., Rings, E. H., Rivadeneira, F., van der Schroeff, M. P., Steegers, E. A., Tiemeier, H., Uitterlinden, A. G., . . . Jaddoe, V. W. (2016). The Generation R Study: design and cohort update 2017. Eur J Epidemiol, 31(12), 1243-1264. https://doi.org/10.1007/s10654-016-0224-9
Lamballais, S., & Muetzel, R. L. (2021). QDECR: A Flexible, Extensible Vertex-Wise Analysis Framework in R. Front Neuroinform, 15, 561689. https://doi.org/10.3389/fninf.2021.561689
Trujillo, C. A., Obando, D., & Trujillo, A. (2019). An examination of the association between early initiation of substance use and interrelated multilevel risk and protective factors among adolescents. PLoS One, 14(12), e0225384. https://doi.org/PONE-D-19-16848
van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1 - 67. https://doi.org/10.18637/jss.v045.i03