SILICA-SNC: FROM STREAMLINES TO NETWORKS

Abstract

Whole brain tractography contains rich trajectory-level information about white matter organization, yet conventional structural connectomes capture only part of it. We introduce Structural Network Coupling (SNC), a network representation enabled by SILICA (Streamline Independent Component Analysis). SILICA decomposes tractography into structural source components and their streamline-specific loading profiles, while SNC characterizes relationships among these components through their shared trajectory expression. The resulting component-level network retains direct correspondence to the underlying white-matter pathways and supports network visualization and graph-based analysis. SNC produced anatomically interpretable coupling among trajectory-resolved white-matter components and revealed a highly organized group architecture that independently recapitulated commissural, projection, and association systems. It also showed strong hemispheric segregation and structured cross-system coupling, and was highly stable across split-half samples. SNC extends SILICA from trajectory decomposition to network-level organization and provides a flexible framework for future structural and multimodal network analysis.