Self-awareness (SA) research is crucial for understanding cognition, social behavior, mental health, and education, but SA's underlying network architecture, particularly connectivity patterns, remains largely uncharted. We integrated meta-analytic findings with connectivity-behavior correlation analyses to systematically identify SA-related regions and connections in healthy adults. Edge-weighted networks capturing public, private, and composite SA dimensions were established, where weights represented correlation strengths between tractography-derived structural connectivities and SA levels quantified through behavioral assessments. Then, multilevel SA networks were extracted across a spectrum of correlation thresholds. Robust full-threshold analyses revealed their hierarchical continuum encompassing distinct lateralization patterns, topological transitions, and characteristic hourglass-like architectures. Pathological analysis demonstrated SA connectivity disruptions in schizophrenia (SZ) and major depressive disorder (MDD): approximately 40 % of SA-related connectivities were altered in SZ and 20 % in MDD, with 90 % of MDD alterations overlapping with SZ. While disease-specific and shared alterations were also observed in network-level topological properties, the core SA connectivity framework remained preserved in both disorders. Collectively, these findings significantly advanced our understanding of SA's neurobiological substrates and their pathological deviations.
Keywords: Connectivity; Lateralization; Meta-analysis; Network topology; Psychiatric disorders.
Copyright © 2025. Published by Elsevier Inc.