Complex, Sex-Specific Associations between Pubertal Timing and Brain Structure-Function Fusion
Poster No:
987
Submission Type:
Abstract Submission
Authors:
Marlena Duda1, Adam Omary2, Mark Curtis3, Theresa Cheng2, Zening Fu4, Leah Somerville2, Deanna Barch5, Vince Calhoun6
Institutions:
1Georgia State University, Atlanta, GA, 2Harvard University, Cambridge, MA, 3Washington University, St Louis, MO, 4Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georg, Atlanta, GA, 5Washington University, Saint Louis, MO, 6GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Zening Fu
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georg
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georg
Atlanta, GA
Introduction:
A thorough understanding of human brain development from childhood to early adulthood is essential for modeling healthy neurodevelopment and identifying mechanisms underlying potential health risks in adolescents. Puberty is marked by several physical and endocrine changes that occur during adolescent development, and early pubertal timing (experiencing puberty earlier relative to same-age peers) has shown associations to increased risk for depression, anxiety, and other negative impacts to well-being (Ullsperger 2017); however, the mechanisms driving these effects are not yet understood. Recent works have underscored the interplay between brain structure and function during puberty and development (Baum 2020). Building on this, we implement a guided fusion approach to deepen our understanding of multimodal brain development as a function of pubertal timing (PT).
Methods:
We utilized T1 sMRI, resting state fMRI scans and self-reported pubertal measures from N=512 adolescent subjects (46% male) aged 8-18 (mean = 13.33 years) in the HCP-D cohort. Pubertal composite (PC) scores were computed as the average of the Shirtcliff-adjusted PDS and Morris-Udry scores (Shirtcliff 2009). PCs were centered, inverted, and age-regressed to reflect PT, where negative scores indicate subjects with early PT and positive scores indicate late PT. We employed a guided fusion technique using the mCCA-R + jICA framework (Sui 2018; model order = 8) to extract co-varying patterns of gray matter volume (GMV) and functional network connectivity (FNC) related to PT. We modeled sex-specific linear/nonlinear associations of puberty to loading parameters using generalized additive models (GAMs).
Results:
We focus on 3 joint components (JCs) that were most strongly associated with PT above the effect of age. JC1 is comprised of structural regions in the default mode (DM) network (anterior and posterior cingulate [ACC and PCC] regions) and functional edges connecting DM to cerebellar (CB) and cognitive control (CC) regions, as well as a hub of anticorrelation between the postcentral gyrus (PoCG) and DM. JC2 was enriched for CC regions, with structural peaks in the middle cingulate and middle frontal regions, as well as functional CC hubs with strong correlations/anticorrelations to both DM and CB networks. JC3 appeared to be functionally and structurally more distributed across domains, but exhibited structural peaks in the precuneus, as well as bilaterally in temporal regions and inferior parietal lobules (IPLs), and had functional hubs in PoCG, IPLs, hypothalamus, and DM. GAM models showed significant associations between expression of FNC and GMV components (loading parameters) to both age and PT across all 3 components. Associations of age to GMV loadings were more complex (nonlinear) than to FNC loadings and showed inverse-U shape trajectories characteristic of GMV during this developmental period (Giedd 1999). Sex-specific associations of PT to FNC and GMV loading parameters revealed higher complexity associations for males than females, specifically for GMV components in models that explained the most deviance in the data (JC1 & JC3). Overall PT showed positive associations with both FNC and GMV loadings, indicating lower expression of component patterns in those with early PT.
Conclusions:
We present multimodal associations of brain structure and function with PT. PT had the strongest associations with (and explained more variance of) loading parameters in components that were strongly enriched for regions in the DM in both structural and functional maps. Regions in the DM are known to be involved in self-awareness (precuneus), social cognition and emotional awareness (ACC) and internally directed thought (PCC), and thus PT-related variance in these areas could contribute to increased risk for depression and anxiety. Furthermore, our results support the model of accelerated development due to early PT, as associations to loadings of those with early PT mimic those of more advanced age.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Multivariate Approaches 2
Keywords:
Development
FUNCTIONAL MRI
PEDIATRIC
STRUCTURAL MRI
Other - Multimodal Data Fusion
1|2Indicates the priority used for review
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Ge, X., Natsuaki, M. N., Jin, R. & Biehl, M. C. (2011)A contextual amplification hypothesis: Pubertal timing and girls’ emotional and behavioral problems. In Understanding girls’ problem behavior: How girls’ delinquency develops in the context of maturity and health, co-occurring problems, and relationships p. 11–29 (Wiley Blackwell).
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., ... & Rapoport, J. L. (1999). Brain development during childhood and adolescence: a longitudinal MRI study. Nature neuroscience, 2(10), 861-863.
Shirtcliff, E. A., Dahl, R. E., & Pollak, S. D. (2009). Pubertal development: correspondence between hormonal and physical development. Child development, 80(2), 327-337.
Sui, J. et al. (2018). Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat. Commun. 9, 3028. PMCID: 6072778.
Ullsperger, J. M. & Nikolas, M. A. (2017). A meta-analytic review of the association between pubertal timing and psychopathology in adolescence: Are there sex differences in risk? Psychol. Bull. 143, 903– 938.