Exploring Causal Pathways to Sleep Quality in Young Adults Using a Multimodal Data-Driven Causal Discovery Analysis

Area of Science:

• Neuroscience
• Psychology
• Data Science

Background:

• Poor sleep quality is a widespread issue affecting physical and mental health.
• The intricate mechanisms and contributing factors to poor sleep quality remain incompletely understood.

Purpose of the Study:

• To investigate the causal pathways influencing sleep quality using a data-driven causal discovery analysis (CDA) approach.
• To explore the complex interactions between phenotypic factors, brain network connectivity, and sleep quality.

Main Methods:

• Utilized data from the Human Connectome Project (HCP) with 1206 healthy young adults.
• Employed exploratory factor analysis to group 122 phenotypic variables into 21 factors.
• Applied Greedy Fast Causal Inference (GFCI) to construct a causal model integrating phenotypic factors, brain network connectivity, and sleep quality.

Main Results:

• A hierarchical causal model was developed, demonstrating complex inter-domain interactions affecting sleep quality.
• Identified negative affect, somaticism, and delay discounting as direct phenotypic contributors to poor sleep quality.
• Discovered demographic-specific causal pathways for sleep quality related to gender and relationship status.

Conclusions:

• The data-driven model elucidates multifaceted mechanisms influencing sleep quality.
• Highlights critical factors impacting sleep, offering insights for sleep theory development and interventions.
• Suggests potential for targeted strategies to improve sleep quality based on identified causal pathways.