Functionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion Approach - Duda, Marlena; Chen, Jiayu; Belger, Aysenil; Ford, Judith; Mathalon, Daniel; Preda, Adrian; Turner, Jessica; Van Erp, Theo; Pearlson, Godfrey; Calhoun, Vince D. (Human brain mapping, 2025)
ABSTRACT The precise relationship between brain structure and function has been investigated through a multitude of lenses, but one detail that is held constant across most neuroimaging studies in this space is the identification of a singular structural basis set of the brain, upon which functional activation signals can be reconstructed to examine the linkage between structure and function. Such basis sets can be considered “functionally independent”, as they are derived through structural data alone and have no explicit association to functional data. Recent work in multimodal fusion has facilitated a more integrated view of structure–function linkages by enabling the equal contribution of both modalities to the joint decomposition, resulting in components that are independent within modality but co‐vary closely across modalities. These existing symmetric fusion approaches thus identify structural bases given an associated functional context. In this work, we consider an additional layer of precision to the investigation of structure–function coupling by studying these context‐dependent linkages in a time‐resolved manner. In other words, we ask which features of brain structure become (or remain) salient given the dynamically changing functional contexts (i.e., dynamic functional connectivity states, task structure, etc.) the brain may pass through during a given fMRI scan. We introduce “dynamic fusion”, an ICA‐based symmetric fusion approach that enables flexible, time‐resolved linkages between brain structure and dynamic brain function. We show evidence that temporally resolved and functionally contextualized structural basis sets can accurately reflect dynamic functional processes and capture diagnostically relevant structure–functional coupling while detecting nuanced functionally driven structural components that cannot be captured with traditionally computed structural bases. Lastly, differential analysis of component stability across repeated scans from a control cohort reveals that the organization of static and dynamic structure‐function coupling falls along unimodal/transmodal hierarchical lines. The dynamic fusion of multimodal neuroimaging data is a method for studying time‐varying structure–function coupling in the brain. We show evidence of brain organization along a spectrum of highly static to highly dynamic structure–function coupling, which largely falls along unimodal/transmodal hierarchical lines.