Neural Network Topologies Supporting Individual Variations in Vividness of Visual Imagery

Vividness of visual imagery varies considerably across individuals. Yet, the neural foundations of this variability remains an open question. In this study, we investigate divergent perspectives by examining how individual differences in imagery vividness—assessed with the Vividness of Visual Imagery Questionnaire (VVIQ-2)—relate to intrinsic brain network organization, using graph theory metrics derived from structural and functional connectomes based on diffusion-weighted imaging (DWI; n = 525) and resting-state functional magnetic resonance imaging (rs-fMRI; n = 556). Connectivity patterns were analyzed across four targeted networks: an imagery-specific network identified from task-based fMRI studies, and three canonical resting-state networks (occipital, salience, and default mode). Structural connectivity analyses revealed that higher vividness was associated with greater local efficiency and clustering in the occipital network, highlighting the role of low-level visual structural networks in sensory-related integration. Additionally, global efficiency in the right insular cortex—a core node of the salience network—was positively associated with VVIQ scores, suggesting that vivid imagery benefits from more efficient salience-driven control processes supporting internal access and stabilization. Functional connectivity analyses, in turn, identified a positive association between vividness and local efficiency in the left fusiform gyrus, a high-level visual area implicated in visual imagery, suggesting its contribution to integrative aspects of mental imagery. These complementary findings offer a network-level reconciliation of prior accounts: specifically, they suggest that vivid imagery emerges from the structural architecture of sensory and salience systems—reflected in the occipital network and right insular cortex—and in high-level visual integration, captured by functional connectivity in the left fusiform gyrus. Together, these findings bring us closer to a comprehensive understanding of the neural architecture underlying visual imagery.