Age and sleep quality’s impact on brain dynamics is correlated with α4β2 receptor density
Poster No:
2095
Submission Type:
Abstract Submission
Authors:
Anthony Villegas1, S. Parker Singleton2, Keith Jamison1, Ceren Tozlu3, Amy Kuceyeski4
Institutions:
1Weill Cornell Medicine, New York, NY, 2University of Pennsylvania, Philadelphia, PA, 3Weill Cornell Medicine, NYC, NY, 4Cornell, Ithaca, NY
First Author:
Co-Author(s):
Introduction:
Approximately one in three adults in the United States report insufficient sleep, a widespread issue with significant public health implications. Poor sleep quality affects more than just fatigue; it disrupts cognitive functions such as working memory, executive function, and decision-making (Zavecz, 2020) and contributes to mental health disorders, including anxiety and depression (Abdelhack, 2023; Dietch, 2016). Despite these well-documented impacts, the relationship between sleep quality and brain function remains complex and poorly understood. Heterogeneous outcomes among individuals with poor sleep quality highlight the limitations of conventional measures and underscore the need for novel biological markers. Innovative approaches are required to better characterize the neurobiological underpinnings of sleep quality and its effects on brain dynamics.
Methods:
We used high-resolution, preprocessed MRI data from the Human Connectome Project – Young Adult S1200 dataset (van Essen 2013) dataset. Regional fMRI time-series and structural connectomes were extracted from 958 subjects using the 268-region Shen atlas. K-means clustering was employed to characterize commonly recurring brain states. Network control theory (NCT), a linear dynamical systems approach, allows quantification of transition energy (TE)-the control energy required to move between recurring brain states (Gu 2015). Average TE quantifies overall energy requirements for transitions across all brain states, in this case an optimal of four states was found. The Pittsburgh Sleep Quality Index (PSQI) assessed sleep quality, and linear regression explored the relationship between average TE and sleep quality, accounting for age, sex, and mean framewise displacement as covariates and age*PSQI as an interaction term. We further correlated sleep-related receptor density maps (from NeuroMaps) with our t-statistics of the linear model's coefficients for the age-PSQI interaction term to investigate possible underlying mechanisms for sleep-by-age effects on TE.
Results:
A significant effect of age (p=0.017) and the interaction between age and PSQI (p=0.039) was found with average, whole-brain TE. Post-hoc tests revealed that in younger individuals, worse sleep quality was associated with higher global TE while in older individuals this association was reversed (see Fig 1A). Furthermore, regional predictive effects of the age-PSQI interaction on TE (Fig 1B) were significantly correlated with regional alpha-4-beta-2 receptor density (p=0.003), see Fig 2.
Conclusions:
Our results highlight that the interaction between age and sleep quality (PSQI) is significantly associated with brain network dynamics, as reflected by global transition energy. Specifically, it seems as though the association between sleep quality depends on an individual's age. Correlations between regional TE's age*sex interaction effect and sleep related receptor maps revealed α4β2 as a potential neurobiological mechanism underlying these effects. These findings underscore the combined role of age and sleep quality in shaping brain dynamics, emphasizing their importance for understanding brain function and its variability across individuals.
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
Acetylcholine
ADULTS
Computational Neuroscience
Consciousness
Data analysis
Sleep
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dietch JR (2016), ‘Psychometric Evaluation of the PSQI in U.S. College Students’, Journal of Clinical Sleep Medicine, vol. 12, no. 8
Cornblath EJ (2020), ‘Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands’, Communications Biology, 22;3(1):261.
Gu S (2015), ‘Controllability of structural brain networks’, Nature Communications, 1;6:8414.
Singleton, S.P (2022), ‘Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape’, Nature Communications, 13, 5812
Van Essen DC (2013), ‘The WU-Minn Human Connectome Project: an overview’, Neuroimage. 15;80:62-79.
Zavecz Z (2020), ‘The relationship between subjective sleep quality and cognitive performance in healthy young adults: Evidence from three empirical studies’, Sci Rep, 10(1):4855.