Abstract
Investigating causal neural interactions are essential to understanding subsequent behaviors. Many statistical methods have been used for analyzing neural activity, but efficiently and correctly estimating the direction of network interactions remains difficult (1). Here, we derive dynamical differential covariance (DDC), a new method based on dynamical network models that detects directional interactions with low bias and high noise tolerance without the stationary assumption. The method is first validated on networks with false positive motifs and multiscale neural simulations where the ground truth connectivity is known. Then, applying DDC to recordings of resting-state functional magnetic resonance imaging (rs-fMRI) from over 1,000 individual subjects, DDC consistently detected regional interactions with strong structural connectivity. DDC can be generalized to a wide range of dynamical models and recording techniques.
Competing Interest Statement
The authors have declared no competing interest.