Resting-state Functional Connectivity and Objective vs. Subjective Sleep Quality in Adolescence: A Preliminary Investigation

• In Event: Poster Session 14
  In Poster Session: PS 14 Topic - Biological Processes: Neuroscience and Genetics

Abstract

72% of adolescents report sleeping less than the CDC-recommended weekly amount (Wheaton et al., 2018). In adolescents, reduced sleep duration and quality are associated with poorer academic performance (Roberts et al., 2009) and increased risk for psychological problems (Goldstone et al., 2020). To better understand the underlying mechanisms of this association, researchers have examined the effects of sleep deprivation (SD) on the adult brain. These studies point to changes in the default-mode-network (DMN) and its anticorrelated network, the executive-control-network/frontoparietal (ECN/FPN) as a function of sleep loss (Ben Simon et al., 2017; Shao et al., 2013). There have been few studies with adolescents, utilizing partial SD (Beebe et al., 2009; Robinson et al., 2018) and naturalistic variation in objective (actigraphy) sleep (Lunsford-Avery et al., 2020). These studies have found that reduced actigraphy-based sleep quality (composite of sleep efficiency and awakenings) is associated with reduced resting-state functional connectivity (RSFC) within the DMN (Tashjian et al., 2018). Rarely, however, are both objective and subjective sleep measures included in the same study in relation to brain RSFC.

This pre-registered analysis aims to examine the association between patterns of RSFC and objective vs. subjective sleep quality in a sample of adolescents. Specifically, we aim to replicate previous findings that reduced connectivity between the DMN and ECN is predictive of poorer subjective and objective indices of sleep quality in adolescents. Objective sleep quality is defined as average actigraphy-based sleep efficiency over 2 weeks. Self-reported sleep quality is defined as average sleep satisfaction reported daily via a smartphone application for 2 weeks.

173 participants underwent a resting-state scan 4 years prior to sleep data collection. We expect that RSFC between the DMN and ECN will be less anti-correlated in adolescents who subsequently report lower sleep satisfaction and have reduced sleep efficiency than in adolescents with higher sleep satisfaction and greater sleep efficiency. Second, given that self-reported sleep satisfaction and actigraphy-assessed sleep efficiency are often uncorrelated (Matthews et al., 2018), we expect that overall associations between RSFC and subjectively- and objectively-assessed sleep will be different. Third, given previous studies of within-network connectivity of the DMN in relation to sleep loss vulnerability, we hypothesize that reduced connectivity within the DMN will be associated with reduced subjective and objective sleep quality.

We will conduct a group Independent Components Analysis on the resting-state data to identify the DMN and the ECN/FPN (bilateral). Our independent variables include RSFC within the DMN-only, and between the bilateral ECN and DMN. Our dependent variables are self-reported sleep satisfaction and actigraphy-based sleep efficiency, resulting in 6 linear regression models (FDR-multiple comparisons corrections will be applied). Thus far, we have assessed sleep in 61 participants. We expect 80 more participants to complete these sleep assessments over the next 5 months, well before the conference. Our full analytic plan is at https://osf.io/pmkuw/.

Poor sleep is prevalent in adolescents and often has detrimental consequences. This research interrogates brain-related factors that are related to poor sleep to identify possible biological predictors and intervention targets for those who struggle with sleep.

Authors

• Vanessa López, Stanford University
  Presenting Author

• Jaclyn Schwartz Kirshenbaum, Columbia University
  Non-Presenting Author

• Rajpreet Chahal, Stanford University
  Non-Presenting Author

• Saché M Coury, Stanford University
  Non-Presenting Author

• Jillian R Segarra, Stanford University
  Non-Presenting Author

• Rachel Manber, Stanford University
  Non-Presenting Author

• Eti Ben Simon, University of California, Berkeley
  Non-Presenting Author

• Ian Gotlib, Stanford University
  Non-Presenting Author