Neuroimage. 2011 May 27. [Epub ahead of print]
Granger causality with signal-dependent noise.
Luo Q, Ge T, Feng J.
SourceDepartment of Mathematics and Department of Management, National University of Defense Technology, Hunan 410073, PR China; Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China.
Abstract
It is generally believed that the noise variance in in vivo neuronal data exhibits time-varying volatility, particularly signal-dependent noise. Despite a widely used and powerful tool to detect causal influences in various data sources, Granger causality has not been well tailored for time-varying volatility models. In this technical note, a unified treatment of the causal influences in both mean and variance is naturally proposed on models with signal-dependent noise in both time and frequency domains. The approach is first systematically validated on toy models, and then applied to the physiological data collected from Parkinson patients, where a clear advantage over the classical Granger causality is demonstrated.