A study has shed light on the connection between sleep patterns and brain function. Cognitive abilities are influenced by sleep quality.
Researchers discovered a significant link between brain connectivity – how different brain regions communicate – and the amount of sleep individuals typically get. This finding opens new avenues for understanding the intricate relationship between sleep and cognitive function.
Prior research has extensively documented the benefits of sleep for memory, attention, and overall cognitive performance. However, the link between sleep and the brain’s functional organization, and its impact on behavior, remained less explored. To address this gap, researchers investigated how sleep patterns are reflected in the brain’s functional connectivity.
The study utilized data from two major sources: the Human Connectome Project and the Adolescent Brain Cognitive Development Study. This combined data included brain imaging and sleep information from over 11,000 participants, ranging from children to young adults.
Anurima Mummaneni, an undergraduate researcher at the University of Chicago and member of the Cognition, Attention, & Brain Lab led by Monica D. Rosenberg, commented,
“There has been a long-standing interest in sleep and the brain networks that might influence sleep duration. The rich data from these studies, including sleep data alongside functional magnetic resonance imaging (fMRI) data, provided a unique opportunity to gain a deeper understanding of these networks.”
Anurima Mummaneni – University of Chicago
Large datasets were analyzed to explore the connection between brain function and sleep. The Human Connectome Project provided data on 1,206 young adults (aged 22-35) with a focus on self-reported sleep quality obtained through questionnaires. The Adolescent Brain Cognitive Development Study involved 11,875 individuals (aged 9-10 to 19-20) with sleep duration measured objectively using Fitbit devices.
In both datasets, researchers examined functional Magnetic Resonance Imaging (fMRI) data. fMRI creates a map of brain activity by detecting changes in blood flow. This allowed them to analyze functional connectivity patterns – how different brain regions communicate.
The study identified specific connectivity patterns that predicted an individual’s sleep duration. This ability to predict sleep held true across datasets and age groups, suggesting a universal link between brain function and sleep needs.
“The research shows that similar brain networks influence sleep duration across individuals, regardless of age. Sleep needs vary, with some requiring more or less sleep for optimal function. Interestingly, despite these differences, consistent patterns emerged in brain networks linked to longer and shorter sleep durations across a large participant pool.”
Anurima Mummaneni
The study delved deeper into the brain networks linked to sleep patterns. Two distinct networks were identified, encompassing various regions like the cerebellum, motor cortices, and subcortical areas. Interestingly, networks associated with longer sleep (“high-duration networks”) involved connections between the cerebellum and motor cortices. Conversely, networks linked to shorter sleep (“low-duration networks”) were more widespread, involving connections between the occipital lobes, motor cortices, and parietal regions.
The research also revealed a surprising finding. Resting-state brain activity, the brain’s natural state when not actively engaged, proved to be a better predictor of sleep duration than activity during tasks. This challenges past assumptions about brain function, suggesting the brain’s resting state might hold greater influence on sleep patterns than previously believed.
An intriguing link emerged between predicted sleep duration and cognitive performance. Brain connectivity patterns associated with sleep duration also seemed to correlate with performance on cognitive tasks, hinting at a deeper connection between sleep quality and cognitive abilities.
“The correlation between predicted sleep durations and actual working memory performance in some datasets was unexpected, even with strict training parameters. In simpler terms, individuals whose brain models predicted a tendency for longer sleep also performed better on working memory tests. While the sleep-memory connection is well-established, it was fascinating that our models seemed to reflect these connections even without being specifically trained to do so.”
Anurima Mummaneni
Despite the valuable insights gained, the study acknowledges limitations. The correlations between predicted and actual sleep duration, while statistically significant, were modest. This suggests the models, though theoretically sound, cannot precisely predict individual sleep patterns.
Furthermore, the research design is correlational, meaning it cannot establish cause-and-effect. It remains unclear whether brain connectivity influences sleep or vice versa, or if another factor like stress or age plays a role.
“The causal relationships between sleep and identified brain networks require further investigation. Does sleep duration impact these networks, or do the networks themselves affect sleep quality and duration? Are other factors, like stress, at play? These are questions I’m eager to explore in future studies.”
Anurima Mummaneni
Looking ahead, researchers are interested in whether sleep-predictive brain networks remain stable within individuals. For instance, if someone typically gets 8 hours of sleep, do their sleep-related brain networks change after an all-nighter? Or are these networks relatively stable, reflecting an average sleep pattern over time? The ability to predict sleep duration across diverse datasets suggests some level of consistency, potentially indicating that sleep-related networks remain stable from late childhood to young adulthood.
https://doi.org/10.1002/hbm.26488
Abstract
Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includes n-back task-based and resting-state fMRI data from adults aged 22–35 years (task n = 896; rest n = 898). We applied connectome-based predictive modeling (CPM) to predict participants’ mean sleep duration from their functional connectivity patterns. Models trained and tested using 10-fold cross-validation predicted self-reported average sleep duration for the past month from n-back task and resting-state connectivity patterns. We replicated this finding in data from the 2-year follow-up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includes n-back task and resting-state fMRI for adolescents aged 11–12 years (task n = 786; rest n = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10-fold cross-validation again predicted sleep duration from n-back task and resting-state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting-state functional brain connectivity patterns reflect sleep duration in youth and young adults.
