Regression dynamic causal modeling for resting-state fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI-regression dynamic causal modeling (rDCM)-extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

Automated summary

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used for studying brain connectivity. Researchers have developed a method called rDCM that extends to rs-fMRI, offering directional estimates and scalability to whole-brain networks. Through simulations and empirical tests, rDCM has shown to be computationally efficient and produce biologically plausible results consistent with established models of effective connectivity.