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Causal connectivity measures for pulse-output network reconstruction: Analysis and applications
Zhong-Qi K Tian1,2,3, Kai Chen1,2,3, Songting Li1,2,3
1School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.
Summary
Researchers explored how different causality measures relate to structural connectivity in pulse-output networks. They found that inferred causal connectivity accurately reflects structural connectivity, enabling network reconstruction without complex global data.
Area of Science:
- Neuroscience
- Network Science
- Computational Biology
Background:
- Inferring causal connectivity is crucial for understanding network function.
- Causal connectivity estimates depend on the chosen causality measure and may differ from structural connectivity.
- The relationship between causal and structural connectivity, especially for pulse-output networks, requires clarification.
Purpose of the Study:
- To investigate the relationship between causal and structural connectivity in nonlinear networks with pulse signals.
- To analyze how commonly used causality measures (time-delayed correlation, mutual information, Granger causality, transfer entropy) relate to each other and to structural connectivity.
- To develop a method for reconstructing network structure from pulse-output data.
Main Methods:
- Theoretical analysis of four causality measures applied to pulse signals.
- Simulation using a Hodgkin-Huxley network model.
- Analysis of a real mouse brain network with spike output.
Main Results:
- Established theoretical relationships among time-delayed correlation, mutual information, Granger causality, and transfer entropy for pulse signals.
- Demonstrated that causal connectivity inferred by these measures aligns well with the underlying structural connectivity in both simulated and real networks.
- Showcased that pairwise reconstruction of structural connectivity is possible without global network information, avoiding the curse of dimensionality.
Conclusions:
- Causal connectivity inferred from pulse-output signals directly reflects the network's structural connectivity.
- The proposed framework offers an effective and practical method for reconstructing the structure of pulse-output networks.
- This approach simplifies network analysis by enabling pairwise reconstruction, bypassing the need for extensive global data.
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