A new study has identified five distinct profiles that link a person’s sleep patterns with their health, cognitive abilities, and lifestyle factors, each with a unique signature in the brain. The research suggests that a one-size-fits-all approach to sleep health is insufficient and that understanding these individual profiles could lead to more personalized support for well-being. The findings were published in the scientific journal PLOS Biology.
For decades, scientists have understood that sleep is connected to a wide array of biological, psychological, and social factors. Research has consistently linked poor or insufficient sleep to negative outcomes like cognitive difficulties, poor mental health, and increased risk for physical diseases. However, many studies have tended to simplify sleep into categories like “good” versus “poor” or “short” versus “long.” This approach fails to capture the complex, multidimensional nature of sleep, which includes aspects like duration, quality, regularity, and daytime alertness.
Researchers, led by Aurore A. Perrault of Concordia University, recognized this gap. They wanted to move beyond single-association studies and use a more holistic approach. The team aimed to see if they could identify naturally occurring patterns, or profiles, that connect the many dimensions of sleep with an equally broad range of health, cognitive, and lifestyle measures. By also looking at brain imaging data, they hoped to find the neurobiological underpinnings of these different sleep experiences.
To conduct their investigation, the researchers utilized a large, publicly available dataset from the Human Connectome Project. Their final sample included 770 healthy young adults, aged between 22 and 36. Participants had completed a detailed sleep questionnaire, the Pittsburgh Sleep Quality Index, which assesses seven different dimensions of sleep, including satisfaction, the time it takes to fall asleep, sleep duration, disturbances, and use of sleep aids.
The team paired this sleep data with 118 other measures collected from the same individuals. These biopsychosocial measures covered a wide territory, including self-reported mental health, personality traits, emotional states, substance use, lifestyle habits, and performance on cognitive tasks that tested functions like memory and attention.
The researchers then applied a sophisticated statistical method called canonical correlation analysis, a technique designed to uncover the strongest relationships between two complex sets of variables. This allowed them to identify latent patterns that optimally linked the seven sleep dimensions with the 118 biopsychosocial factors.
The analysis revealed five distinct sleep-biopsychosocial profiles. The first profile was the most dominant, explaining a large portion of the relationship between sleep and the other factors. It was characterized by a general pattern of poor sleep, including low sleep satisfaction, taking a long time to fall asleep, frequent sleep disturbances, and daytime impairment. This pattern was strongly associated with general psychopathology, including symptoms of depression, anxiety, and negative emotions like fear and anger.
The second profile also showed a strong connection to psychopathology, particularly attention problems and low conscientiousness. However, in a stark contrast to the first profile, individuals fitting this pattern did not report general sleep difficulties, only feelings of daytime impairment. The researchers termed this profile “sleep resilience,” suggesting some individuals may maintain seemingly healthy sleep patterns even in the face of mental health challenges.
“Our study showed that different aspects of sleep are related, but can also be separable domains with specific connections to biopsychosocial factors (lifestyle, mental and physical health and cognitive performances). This highlights the importance of considering the full picture of an individual’s sleep to help clinicians make more accurate assessments and guide treatment,” said Perrault.
The remaining three profiles were driven by more specific aspects of sleep. The third profile was defined by the use of sleep-aid medication. This was linked not to poor mental health, but to high satisfaction with social relationships. At the same time, these individuals showed poorer performance on visual memory and emotion recognition tasks.
The fourth profile was characterized almost exclusively by short sleep duration, with individuals reporting sleeping less than six to seven hours per night. This lack of sleep was not strongly tied to mental health complaints but was associated with worse performance across multiple cognitive tasks, including those involving emotional processing, language, and problem-solving. This profile was also linked to higher levels of aggressive behavior.
The fifth profile was centered on sleep disturbances, which can include waking up frequently, breathing issues, or pain during the night. Like the first profile, this pattern was connected to mental health issues, specifically anxiety and thought problems. It was also associated with substance use, including alcohol and cigarettes, and poorer performance on cognitive tasks related to language and working memory.
“The dominance of mental health markers in most of the profiles is not surprising as sleep is one of the five key domains of human functioning likely to affect mental health,” explained Valeria Kebets, a co-author of the study.
The researchers then examined resting-state functional magnetic resonance imaging data from the participants to see if these five profiles were reflected in brain organization. They found that each profile was associated with a distinct pattern of brain network connectivity. For example, the first profile of poor sleep and psychopathology was linked to increased communication between deep brain structures and the somatomotor network, which is involved in processing bodily sensations and movement. The “sleep resilience” profile, on the other hand, showed a different brain signature, perhaps indicating a neural mechanism that protects sleep quality.
“The different sleep profiles were also supported by unique patterns of brain function measured with MRI, suggesting that sleep experiences are reflected not just in health and behavior, but also in the brain’s wiring and activity,” noted Perrault. Alterations in the somatomotor network appeared in several of the profiles, suggesting this brain system may play a significant role in the relationship between sleep, health, and behavior.
The study has some limitations that the authors acknowledge. The sleep and health data were primarily based on self-reports, which can sometimes differ from objective measurements. Future research could incorporate data from wearable devices or lab-based sleep studies to see if these profiles hold. Also, because the study was a snapshot in time, it cannot determine cause and effect. It is unclear if poor sleep leads to health problems, if health problems disrupt sleep, or if another factor influences both. The study population was also limited to healthy young adults, so these specific profiles may not apply to other age groups or clinical populations.
Despite these limitations, the research provides a more nuanced understanding of how sleep relates to overall well-being. By identifying these distinct profiles, the study moves beyond simplistic labels and offers a framework for recognizing the varied ways individuals experience sleep and its consequences. This approach could eventually equip both researchers and clinicians with better tools to support individual health by tailoring interventions to a person’s specific sleep-biopsychosocial profile.
“The current study emphasizes that by using a multidimensional approach to identify distinct sleep-biopsychosocial profiles, we can begin to untangle the interplay between individuals’ variability in sleep, health, cognition, lifestyle, and behavior—equipping research and clinical settings to better support individuals’ well-being,” the researchers concluded. “Future investigations into how the multifaceted relationships between sleep and biopsychosocial factors differ or change according to age, sex, and other demographics would likely benefit from data-driven approaches.”
The study, “Identification of five sleep-biopsychosocial profiles with specific neural signatures linking sleep variability with health, cognition, and lifestyle factors,” was authored by Aurore A. Perrault, Valeria Kebets, Nicole M. Y. Kuek, Nathan E. Cross, Rackeb Tesfaye, Florence B. Pomares, Jingwei Li, Michael W. L. Chee, Thien Thanh Dang-Vu, and B. T. Thomas Yeo.
