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Tiger W. Lin
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Publisher: Journals Gateway
Network Neuroscience (2022) 6 (2): 614–633.
Published: 01 June 2022
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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. Author Summary Our ability to sense, think, feel and react is reflected in the activation patterns of distinct brain regions. These patterns can be observed noninvasively by functional magnetic resonance imaging (fMRI). Statistical dependencies between the activities in brain regions have been widely used to measure functional connectivity (FC). However, due to common inputs from other brain areas, two correlated brain regions may not have direct neural fibers connecting them, which makes correlated activity difficult to interpret. A new method for measuring FC called differential covariance (dCov) was applied to fMRI recordings from anesthetized mice and resting human subjects. The analysis closely matched known neuronal fiber connections. In addition, dCov-FC from individual humans predicted reaction times for several types of psychological tests.
Includes: Supplementary data