Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

Abstract

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

Neural integration and segregation revealed by a joint time-vertex connectome spectral analysis

“…Recent efforts to constrain dynamic functional connectivity to anatomical connectivity have shown improved reconstruction of connectivity in electrophysiological signals (49) and fMRI (50). We have introduced and validated with existing literature using intra cortical recordings, a new approach to estimate the frequency-resolved dynamics of different modes of information processing in the brain.…”

A Joint Subspace Mapping Between Structural and Functional Brain Connectomes

“…It is worth noting that there is no known closed-form solution to (8). We adopt the iterative algorithm described in [23] to find an approximate solution.…”

“…First, we construct matrices X and Y using both structure and function connectomes of the subjects; then we learn the spectrum mapping W in (6). Second, we solve (8) to obtain group level joint eigenmodes A. In the next iteration, we re-train the mapping W in (5) using these joint eigenmodes followed by estimating a refined group joint eigenmodes using the new mapping.…”

“…Several studies have aimed towards a unified understanding of the mapping between structural and functional connectivity [3]–[8] A broad class of existing literature is built on generative models of functional activity. [1], [3], [9], [10].…”

“…Several studies have aimed towards understanding of the mapping between structural and functional connectivity [20,17,16,21,22,19,23,24,25]. A broad class of these studies are built on complex generative models of functional activity [10,20,26,27,28,29,25] typically that generate simulated functional time-series from which the functional connectome is estimated.…”