Multimodal Neuroimaging Signatures of Sleep Problems in Preadolescence
Poster No:
950
Submission Type:
Abstract Submission
Authors:
Yulin Wang1, Debo Dong2
Institutions:
1Southwest University, Chongqing, --- Select One ---, 2Southwest University, Chongqing, China
First Author:
Co-Author:
Introduction:
Pediatric sleep related problems (SRP) are not homogenous but a composite concept containing multiple domains ranging from behavioral difficulties (e.g., bedtime resistance), to diagnosable sleep disorders (e.g., sleepwalking). Previous studies investigating the alterations of brain structure and function associated with pediatric SRP only focused on one domain of sleep problems or one brain modality, which leads to the relationship between pediatric SRP, and multi-model brain networks remains unknown.
Methods:
Here, we simultaneously examined structural and functional brain patterns in relation to dimensions of SRP in a large cohort of children aged 9-11 years old from the Adolescent Brain Cognitive Development dataset (Volkow et al., 2018) by applying a multivariate approach (partial least squares). Pediatric SRP was characterized with the parent-reported Sleep Disturbance Scale for Children (SDSC) (Bruni et al., 1996) as well as the sleep related items in the Child Behavior Checklist (CBCL) (Achenbach and Rescorla, 1983). To profile neural substrates, we combined multiple intrinsic measures of brain structure (i.e., cortical surface area, thickness, volume) and functional connectivity at rest in our analysis (see Fig 1 for an overview of the main analyses). We expected that the synergistic incorporation of multiple brain measures may capture multiple scales of brain organization during this critical developmental moment, and thus offer sensitivity in identifying covariance patterns between neuroimaging signatures and pediatric SRP dimensions. We further examined if multimodal substrates of pediatric SRP associated with the well-established cortical developmental maps derived from the neuromaps software toolbox(Markello et al., 2022). We conducted our analysis in a Discovery subsample of the ABCD cohort and validated all findings in a Replication subsample from the same cohort. Multiple sensitivity and robustness analyses verified consistency of our findings. We further tested whether the significant multimodal image spatial pattern of the dimension observed in the discovery dataset could predict the general sleep problems in children in the independent HCP-D dataset (n=166), serving as external validations. Importantly, we also investigated whether the identified SRP related multimodal neuroimaging correlates can predict the developmental trajectory of internalizing and externalizing problems two years later.
Results:
Our multivariate analysis(Fig.1) reveals a robust and interpretable composite general sleep disturbances dimension which covaries with distinct morphological and functional connectivity signatures(Fig.2), mainly contextualized along cortical developmental hierarchical gradients mainly involving the somatosensory, attention and default mode networks in the discovery subsample. The consistency of results was observed in the replication subsample, indicating generalizability and resilience to variations in analytical parameters. These findings were also generalized to the HCP-D dataset. Specifically, the identified multimodal neuroimaging signatures demonstrate predictive utility for SRP in an independent sample of unrelated children from the HCP-D dataset. Critically, the identified multi-model neuroimaging signatures mediate the interplay between pediatric SRP and internalizing/externalizing symptoms. Moreover, the identified SRP-related multi-model neuroimaging correlates can predict the developmental trajectory of internalizing/externalizing behavioral difficulties. Specifically, children with higher multimodal image composite scores tend to experience a slower decrease in their internalizing and externalizing behavioral difficulties over time.
Conclusions:
These findings represent a first step toward capturing the multimodal neurobiological changes underlying the dimension of SRP in preadolescence, which may inform the development of brain-based interventions aimed at improving sleep and mental health outcomes throughout development.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural)
Multivariate Approaches 2
Keywords:
Development
Machine Learning
MRI
Multivariate
Pediatric Disorders
Sleep
Other - multi-model neuroimaging；psychopathology symptoms；prediction
1|2Indicates the priority used for review
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Provide references using APA citation style.
Bruni, O., Ottaviano, S., Guidetti, V., Romoli, M., Innocenzi, M., Cortesi, F., & Giannotti, F. (1996). The Sleep Disturbance Scale for Children (SDSC) Construct ion and validation of an instrument to evaluate sleep disturbances in childhood and adolescence. Journal of sleep research, 5(4), 251-261.
Achenbach, T. M. (1983). Manual for the child behavior checklist and revised child behavior profile. University of Vermont.
Markello, R. D., Hansen, J. Y., Liu, Z. Q., Bazinet, V., Shafiei, G., Suárez, L. E., ... & Misic, B. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature Methods, 19(11), 1472-1479.