ArticleProceedings of the National Academy of Sciences of the United States of America2022
Dynamical differential covariance recovers directional network structure in multiscale neural systems.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 23 citations in OpenAlex.
- Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Distributed neural codes of the 3D position in the marmoset frontal cortex and hippocampus.Nature communications · 2026Article
- Simultaneously determining regional heterogeneity and connection directionality from neural activity and symmetric connection.PLoS computational biology · 2025Article
- Advances in large-scale electrophysiology with high-density microelectrode arrays.Lab on a chip · 2025Review
- Article
- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons.Frontiers in computational neuroscience · 2025Article
- Oscillatory components of bidirectional cardio-respiratory coupling in depression and suicidal ideation: insights from swarm decomposition and entropy analysis.Frontiers in network physiology · 2025Article
- Causal connectivity measures for pulse-output network reconstruction: Analysis and applications.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling.Scientific reports · 2024Article
- Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times.Network neuroscience (Cambridge, Mass.) · 2022Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
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. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions. Moreover, DDC scales well with the number of recording sites and the computation required is comparable to that needed for covariance. DDC was validated and compared favorably 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), DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). DDC is a promising family of methods for estimating connectivity that can be generalized to a wide range of dynamical models and recording techniques and to other applications where system identification is needed.
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