Poster No:
949
Submission Type:
Abstract Submission
Authors:
Hollie Byrne1, Rachel Visontay1, Emma Devine1, Lindsay Squeglia2, Louise Mewton1
Institutions:
1The University of Sydney, Sydney, New South Wales, 2Medical University of South Carolina, Charleston, SC
First Author:
Hollie Byrne
The University of Sydney
Sydney, New South Wales
Co-Author(s):
Emma Devine
The University of Sydney
Sydney, New South Wales
Introduction:
Alcohol initiation prior to age 15 is associated with hazardous alcohol use in later life. As such, identifying predictive markers of early alcohol initiation is essential to inform prevention and early intervention efforts. This work used a structural covariance network analysis to examine whether the structural organisation of the brain age 9-10 is predictive of early alcohol initiation prior to age 15.
Methods:
Data from the Adolescent Brain Cognitive Development (ABCD) Study release 5.0 were used. Early alcohol initiation was defined as participants consuming a full drink of alcohol at any timepoint between 1- (ages 10-11) and 4-year follow-up (ages 13-14). Participants who reported consuming a full drink of alcohol at baseline, who did not participate in the 4-year follow-up, and/or did not meet ABCD neuroimaging quality control inclusion criteria at baseline were excluded, and siblings were removed at random. Of the 11,868 participants enrolled in the study, 3,904 (47.6% female) participants met inclusion criteria, of which 181 reported having consumed a full drink of alcohol before age 15. FreeSurfer-derived Desikan-Killiany measurements of cortical thickness determined from baseline neuroimaging assessments (age 9-10) were included in the analysis and harmonized using RELIEF (Kraft et al., 2024). Differences in cortical thickness between groups was assessed using general linear models controlling for age and sex and adjusted for multiple comparisons using FDR-correction. Structural covariance networks were generated using Pearson's correlations of studentized residuals between pairs of cortical regions. Permutation testing was performed across 16 densities with a two-tailed αFDR-level of 0.05 to assess differences in graph-level measures of network segregation (modularity, clustering coefficient, and local efficiency) and integration (average shortest path length and global efficiency) between groups. Robustness of these measurements were assessed using area under the curve (AUC) testing. Age and sex were included a first-tier covariates in the models, while race, ethnicity, religion, parental education, maternal alcohol use during pregnancy and sipping at baseline were subsequently incorporated as second-tier covariates. Finally, due to the large difference in group sizes, a sensitivity analysis using a sample matched by propensity scores at a 1:1 ratio was performed. Network generation and statistical analyses were performed using the BrainGraph package in R (Watson, 2024).
Results:
While no differences in cortical thickness measurements were present between groups, participants who reported alcohol consumption prior to age 15 demonstrated lower clustering coefficient [AUC difference = -0.0078, 95% confidence interval = -0.0160, 0.0023, p = 0.010] compared to the alcohol-abstaining group, independent of age and sex. No differences in modularity, local efficiency, average shortest path length or global efficiency were identified. When introducing second-tier covariates into the models, and when comparing to the matched sample, clustering coefficient was consistently lower across multiple densities in the alcohol initiation group; however, this did not survive FDR-correction at any given density or AUC testing. Both groups were comparable across all other measures of network integration and segregation.
Conclusions:
This work provided preliminary evidence to suggest adolescents aged 9-10 who report alcohol use prior to the age of 15 demonstrate lower clustering coefficient compared to their abstaining peers, independent of age and sex. However, sociodemographic and environmental factors like race, ethnicity, religion, maternal alcohol use during pregnancy, parental education, and/or alcohol sipping at baseline may better explain neural differences that precede early alcohol initiation. Replication in a larger sample of early alcohol initiators to investigate such relationships warrants consideration.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
Computational Neuroscience
Cortex
Development
MRI
STRUCTURAL MRI
Other - Alcohol
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Were any animal research approved by the relevant IACUC or other animal research panel?
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Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Provide references using APA citation style.
Kraft, D., Bon, G. M., Breton, É., Seidel, P., & Kaufmann, T. (2024). Removing scanner effects with a multivariate latent approach: A RELIEF for the ABCD imaging data?. Imaging Neuroscience, 2, 1-7.
Watson C (2024). brainGraph: Graph Theory Analysis of Brain MRI Data. R package version 3.0.3, https://github.com/cwatson/braingraph.
No