Multimodal analysis uncovers links between grey matter volume and frequency-specific connectivity states in schizophrenia
Schizophrenia (SZ) is a complex psychiatric disorder that, as currently defined, exhibits substantial heterogeneity in both pathophysiology and etiology. Studies of SZ have shown alterations in both static and dynamic functional network connectivity (sFNC and dFNC, resp.), as well as measurable group differences in brain structure. The recently proposed filter-banked connectivity (FBC) method has gone beyond the standard dFNC sliding-window approach to estimate FNC within distinct frequency bands spanning the full spectral range of connectivity, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that SZ subjects exist in a less structured, more disconnected low-frequency (i.e., static) FNC state than controls and showed a preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional etiology of SZ. Building on these findings, we sought to link frequency-specific patterns of FNC to structural brain characteristics in the context of SZ. Specifically, we utilize a multi-set CCA + joint ICA data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the frequency spectrum. We found functional connections in both low- and high-frequency SZ-dominant states were significantly related to alterations in GMV in several areas in the frontal and temporal cortices, which have formerly been implicated in SZ.