Local structure-function relationships in human brain networks across the lifespan

Abstract

The human connectome is the set of physical pathways linking brain regions to one another. Empirical and in silico studies have demonstrated that the structure of this network helps shape patterns of functional coupling between brain regions. To better understand this link between structure and function, a growing number of studies have derived geometric, dynamic, and topological predictors from structural connectivity in order to make predictions about correlation structure. These studies, however, have typically focused on global (whole-brain) predictions using a restricted set of predictors. Here, we investigate a wide range of predictors and shift focus onto predictions of local (regional) patterns of functional coupling. We show that, globally, no individual predictor performs well and, that even the best predictors are largely driven by their ability to predict functional coupling between directly connected regions. We then use the same predictors to make predictions of local coupling and find marked improvement. Notably, the most predictable local FC is linked to sensorimotor regions, which are best predicted by measures based on topological similarity, mean first passage times of random walkers, and the brain's embedding in Euclidean space. We then show that by combining the predictive power of more than one predictor using multi-linear models, we can further improve local predictions. Finally, we investigate how global and local structure-function coupling changes across the human lifespan. We find that, globally, the magnitude of coupling decreases with biological age, which is paralleled by an increase in the number of multi-step pathways. We also show that, locally, structure function coupling is preserved in higher order cognitive systems, but preferentially decreases with age in sensorimotor systems. Our results illuminate the heterogeneous landscape of structure-function coupling across the cerebral cortex and help clarify its changes with age.

Multi-policy models of interregional communication in the human connectome

“…This observation is in line with previous studies demonstrating that shortest paths matrices are relatively poor predictors of static resting FC compared to matrices based on diffusion or other decentralized processes, e.g. communicability [13–15, 25, 26].…”

“…Model fitness is typically defined ad hoc and the differences in performances between models – if model comparison is even performed – is often small [26]. Second, until recently [26, 27], communication models have been applied exclusively at the whole-brain level, i.e. the modeling framework assumes that every pair of brain regions communicates using the same policy.…”

Adolescent development of multiscale structural wiring and functional interactions in the human connectome

“…In a follow-up study, the authors showed increases in structurefunction coupling, particularly in transmodal cortices [3]. Another recent study showed consistent findings that the correspondence of structural and functional networks is preserved in transmodal regions across the adult lifespan, while sensorimotor systems showed a decreasing trend of structurefunction coupling [39]. In the current work, we built on this growing literature to assess how adolescent changes in multiscale cortical wiring are associated with changes in brain network function.…”

Network communication models narrow the gap between the modular organization of structural and functional brain networks

“…A growing body of evidence supports that CMs can explain a greater portion of variation in FC than SC alone. Previous studies along these lines have focussed mostly on the global scale [30, 31]—FC between regions pairs across the whole brain—and local scale [62, 63]—FC profiles of individual regions. Here, we add to this literature by considering how network communication models can uncover mesoscopic relationships between brain structure and function.…”