Functionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion Approach

Abstract

AbstractThe precise relationship between brain structure and dynamic neural function has long been an open area of research, and recently, a topic of much debate. Whether investigated through the lens of interregional white matter connectivity or cortical surface morphology, the common thread that links many studies in this subfield of research is the focus on identifying a singular structural basis set, upon which functional activation signals are reconstructed to define the linkage between structure and function. Such approaches are limited in two respects; first, these basis sets are defined solely upon structural data and ignore the influence of functional coupling entirely, and second, these approaches operate on the somewhat narrow assumption that a single structure-function coupling governs the activity of the whole brain at all times. The first limitation can be addressed with the use of multimodal data fusion, which identifies hidden linkages between structural and functional brain imaging data; however, many multimodal fusion approaches still necessitate functional data to be heavily summarized over the time dimension, resulting in temporally rigid structure-function linkages. Regarding the second limitation, given that functional brain activity and connectivity vary over multiple timescales, it is natural to consider this also might be true of structure-function couplings. Here, we introduce dynamic fusion, implemented as an ICA-based symmetric fusion approach, which enables flexible, time-resolved linkages between structure and function utilizing dynamic functional connectivity (dFNC) states. We show evidence that challenges current claims regarding structural basis sets and suggests that temporally evolving structural basis sets can better reflect dynamic functional manifolds and better capture diagnostically relevant structure-functional coupling than traditionally computed structural bases. Lastly, differential analysis of component stability across repeated scans from a control cohort reveals organization of static and dynamic structure/function coupling falls along unimodal/transmodal hierarchical lines.