ArticleNetwork neuroscience (Cambridge, Mass.)2022
Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times.
Article in Network neuroscience (Cambridge, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The clinical relevance of healthy neurodevelopmental connectivity in childhood and adolescence: a meta-analysis of resting-state fMRI.Frontiers in neuroscience · 2025Pooled it
- Brain network pathophysiology in dystonia.Dystonia (Lausanne, Switzerland) · 2025Article
- Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Dynamical differential covariance recovers directional network structure in multiscale neural systems.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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4 authors.
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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. When we applied dCov to rs-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 dCov-FCs 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 significantly correlated with behavior.
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