论文

2025

1. arXiv

   Network structural change point detection and reconstruction for balanced neuronal networks

   Kai Chen, Mingzhang Wang, Songting Li, and 1 more author

   2025
2. arXiv

   Nonlinear Network Reconstruction by Pairwise Time-delayed Transfer Entropy

   Kai Chen, Zhong-qi K. Tian, Yifei Chen, and 2 more authors

   2025
3. CSIAM-LS

   The Asymptotic Behavior of Conditional Granger Causality with Respect to Sampling Interval

   Jinlong Mei, Kai Chen, Yanyang Xiao, and 2 more authors

   CSIAM Transactions on Life Sciences, 2025

   摘要 DOI PDF

   Granger causality (GC) stands as a powerful causal inference tool in time series analysis. Typically estimated from time series data with finite sampling rate, the GC value inherently depends on the sampling interval τ. Intuitively, a higher data sampling rate leads to a time series that better approximates the real signal. However, previous studies have shown that the bivariate GC converges to zero linearly as τapproaches zero, which will lead to mis-inference of causality due to vanishing GC value even in the presence of causality. In this work, by performing mathematical analysis, we show this asymptotic behavior remains valid in the case of conditional GC when applying to a system composed of more than two variables. We validate the analytical result by computing GC value with multiple sampling rates for the simulated data of Hodgkin-Huxley neuronal networks and the experimental data of intracranial EEG signals. Our result demonstrates the hazard of GC inference with high sampling rate, and we propose an accurate inference approach by calculating the ratio of GC to τas τapproaches zero.

2024

1. CSIAM-LS

   The Asymptotic Behavior of Conditional Granger Causality with Respect to Sampling Interval (Online)

   Jinlong Mei, Kai Chen, Yanyang Xiao, and 2 more authors

   CSIAM Transactions on Life Sciences, Jun 2024
2. PNAS

   Causal Connectivity Measures for Pulse-Output Network Reconstruction: Analysis and Applications

   Zhong-qi K. Tian, Kai Chen, Songting Li, and 2 more authors

   Proceedings of the National Academy of Sciences, Apr 2024

   摘要 DOI Bib PDF 附录 海报

   The causal connectivity of a network is often inferred to understand network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the network’s underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, e.g., neural networks with spike output, and address the above issues based on four commonly utilized causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking a simulated Hodgkin–Huxley network and a real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore illustrating a direct link between the causal and structural connectivity. We stress that the structural connectivity of pulse-output networks can be reconstructed pairwise without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction.

   @article{tian2024causal,
     title = {Causal Connectivity Measures for Pulse-Output Network Reconstruction: {{Analysis}} and Applications},
     author = {Tian, Zhong-qi K. and Chen, Kai and Li, Songting and McLaughlin, David W. and Zhou, Douglas},
     year = {2024},
     month = apr,
     journal = {Proceedings of the National Academy of Sciences},
     volume = {121},
     number = {14},
     pages = {e2305297121},
     issn = {0027-8424, 1091-6490},
     doi = {10.1073/pnas.2305297121},
     copyright = {All rights reserved},
     dimensions = {true},
   }

2023

1. bioRxiv

   Quantitative relations among causality measures with applications to pulse-output nonlinear network reconstruction

   Zhong-qi K. Tian, Kai Chen, Songting Li, and 2 more authors

   bioRxiv, Apr 2023

   摘要 DOI Bib PDF

   The causal connectivity of a network is often inferred to understand the network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the network’s underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, e.g., neural networks with spike output, and address the above issues based on four intensively utilized causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking the simulated Hodgkin-Huxley neural network and the real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore establishing a direct link between the causal and structural connectivity. We stress that the structural connectivity of networks can be reconstructed pairwise without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction.Significance Statement Inferring network connectivity is a key challenge in many diverse scientific fields. We investigate networks with pulse signal as measured output and solve the above reverse-engineering issue by establishing a direct link between the network’s causal connectivity and structural connectivity. Here, the causal connectivity can be inferred by any one of the four causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We analytically reveal the relationship among these four measures and show that they are equally effective to fully reconstruct the network connectivity pairwise. Our work provides a practical framework to reconstruct the structural connectivity in general pulse-output nonlinear networks or subnetworks.Competing Interest StatementThe authors have declared no competing interest.

   @article{Tian2023,
     author = {Tian, Zhong-qi K. and Chen, Kai and Li, Songting and McLaughlin, David W. and Zhou, Douglas},
     title = {Quantitative relations among causality measures with applications to pulse-output nonlinear network reconstruction},
     elocation-id = {2023.04.02.535284},
     year = {2023},
     doi = {10.1101/2023.04.02.535284},
     publisher = {Cold Spring Harbor Laboratory},
     journal = {bioRxiv}
   }

2. Nonlinear Pulse-coupled Network Reconstruction by Pairwise Time-delayed Transfer Entropy (in preparation)

   Zhong-qi K. Tian, Kai Chen, Wei P. Dai, and 2 more authors

   Apr 2023