Background/Objectives: Self-construal—the extent to which the self is construed as autonomous from or connected with others—shapes cognition and social behavior, yet its system-level neural architecture remains poorly understood. This study examined whether independent and interdependent self-construal are associated with distinct patterns of nodal centrality and connectivity in the resting brain and whether these patterns covary with in-group favoritism. Methods: Graph-theoretical analyses were applied to resting-state fMRI data from 67 college students. Degree centrality (DC) was the primary analysis; seed-based static functional connectivity (sFC) and dynamic functional connectivity variability (FCV) from DC-derived seeds served as exploratory follow-ups. In-group favoritism was assessed with a minimal-group point-allocation task. Results: Independent self-construal was positively associated with DC in the left inferior occipital gyrus and right lingual gyrus; exploratory sFC linked these occipital seeds to frontal, temporal, and parietal regions, with greater exploratory FCV in their coupling with frontoparietal control areas. Interdependent self-construal was positively associated with DC in the medial prefrontal cortex and thalamus, with exploratory sFC to occipital, temporal, and insular regions but no significant FCV effects. The self-construal difference score yielded convergent DC results and was negatively associated with in-group favoritism; occipital–postcentral coupling was positively associated with in-group favoritism in an exploratory correlation. Conclusions: Independent and interdependent self-construal are associated with distinct nodal centrality profiles, linking independence to occipital perceptual hubs and interdependence to medial prefrontal–thalamic hubs. Exploratory connectivity analyses extend these findings to the coupling of these hubs with distributed brain regions and to in-group favoritism.

Purpose: Poor sleep quality is prevalent across the population and may significantly impact both physical and mental health. However, our understanding of the complex mechanisms underlying poor sleep quality is still incomplete, particularly regarding the various contributing factors. To address this, we utilized a data-driven causal discovery analysis (CDA) approach to explore causal pathways of sleep quality. Patients and Methods: We relied on a large sample of healthy young adults from the Human Connectome Project (HCP; n = 1206 [54% female, 56% unmarried/non-cohabiting]) to explore causal pathways of sleep quality. We first used exploratory factor analysis to cluster 122 broad phenotypic variables into 21 factors and computed the functional connectivity of 13 resting-state brain networks. Then, using Greedy Fast Causal Inference (GFCI), we simultaneously integrated the obtained phenotypic factors, brain network connectivity, and sleep quality into the causal discovery analysis and ultimately constructed a causal model. Results: The model proposes a hierarchical structure with causal effects propagating through complex interactions across multiple domains, ultimately linked to changes in sleep quality. Our causal model identified three phenotypic factors (negative affect, somaticism, and delay discounting) as directly linked to sleep quality. In addition, we examined causal models of sleep quality across gender (male and female) and relationship status (unmarried/non-cohabiting and married/cohabiting) and found some demographic-specific pathways. Conclusion: Our data-driven model reveals complex mechanisms by which factors from different domains influence sleep quality and highlights several key factors that influence sleep quality, which may have important implications for the development of sleep theories and the improvement of sleep quality.