Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

Abstract

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

Dynamical differential covariance recovers directional network structure in multiscale neural systems

“…Interestingly, Δp had very high sensitivity (true positive rate) even when very few connections were thresholded as positive. This might be due to its sparse estimation ( 21 , 22 ).…”

“…We previously introduced differential covariance (dCov) ( 21 , 22 ), a directed FC estimation method, and highlighted the performance of two matrices, Δc, which is the correlation between the derivative signal and the signal itself, and Δp, which is the partial covariance between them. In simulated test cases, dCov detected network connections with higher sensitivity than many of the methods reviewed in Smith et al ( 6 ).…”