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Causal inference in neuronal time-series using adaptive decomposition.

João Rodrigues¹, Alexandre Andrade¹

¹Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisboa, Portugal.

Journal of Neuroscience Methods

|February 28, 2015

Summary

This summary is machine-generated.

Adaptive data analysis enhances Granger causality (GC) for neuroscience, improving causal effect detection in neuronal signals. Combining empirical mode decomposition (EMD) with GC metrics offers superior sensitivity and specificity.

Keywords:

Directed functional connectivity Empirical mode decomposition Granger causality Time series analysis Time-frequency analysis

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Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Assessing directed functional connectivity in neuronal data commonly uses Granger causality (GC).
Current GC methods aim to discern causal effects in both time and frequency domains.
Existing approaches often lack precision in frequency localization.

Purpose of the Study:

To investigate the impact of adaptive data analysis on the Granger causality framework.
To develop a novel method combining empirical mode decomposition (EMD) with causal analysis for improved neuronal signal analysis.
To enhance frequency localization of causal effects in neuronal data.

Main Methods:

Utilized empirical mode decomposition (EMD) to decompose neuronal signals into intrinsic mode functions (IMFs).
Estimated causality between IMFs with comparable instantaneous frequencies (IFs).
Proposed a method to attribute causality to specific frequencies based on driving IMF IFs for improved localization.

Main Results:

Evaluated various EMD algorithms and causality metrics using simulated datasets.
Identified synchrosqueezing wavelet transform and noise-assisted multivariate EMD with generalized partial directed coherence or Geweke's GC as optimal.
Demonstrated improved performance on a benchmark dataset with real animal recordings and enhanced frequency resolution for simulated data.

Conclusions:

Adaptive data analysis significantly benefits the Granger causality framework.
The proposed EMD-based method enhances the accuracy and frequency resolution of causal inference in neuronal data.
This approach offers a fruitful addition to existing causal analysis techniques in neuroscience.