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bioRxiv · 10.1101/2021.06.18.448901

Dynamical differential covariance recovers directional network structure in multiscale neural systems

Chen, Y.·Rosen, B. Q.·Sejnowski, T. J.
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Abstract

Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem (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 under nonstationarity conditions. The method was first validated and compared with other methods on networks with false positive motifs and multiscale neural simulations where the ground truth connectivity was known. When applied to recordings of resting-state functional magnetic resonance imaging (rs-fMRI) recordings, DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). DDC is a promising new family of methods for estimating functional connectivity that can be generalized to a wide range of dynamical models and recording techniques and to other applications where system identification from sparse data is needed. Significance StatementThrough dynamical interactions with each other, neurons make it possible for us to sense, move and think. It is now possible to simultaneously record from many individual neurons and brain regions. Methods for analyzing these large-scale recordings are needed that can reveal how the patterns of activity give rise to behavior. We developed an efficient, intuitive and robust way to analyze these recordings and validated it on simulations of model neural networks where the ground truth was known. We called this method dynamical differential covariance (DDC) because it can estimate not only the presence of a connection but also which direction the information is flowing in a network between neurons or cortical areas. We also successfully applied DDC to brain imaging data from functional Magnetic Resonance Imaging.

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Chen, Y., Rosen, B. Q., Sejnowski, T. J.. 2021-06-20. Dynamical differential covariance recovers directional network structure in multiscale neural systems. https://doi.org/10.1101/2021.06.18.448901

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