Causality Analysis of fMRI Data Based on the Directed Information Theory Framework.

IEEE Trans Biomed Eng. 2015 Sep 24;

Authors: Wang Z, Alahmadi A, Zhu D, Li T

Abstract
This paper aims to conduct fMRI based causality analysis in brain connectivity by exploiting the directed information (DI) theory framework. Unlike the well known Granger Causality (GC) analysis, which relies on the linear prediction technique, the directed information theory framework does not have any modeling constraints on the sequences to be evaluated and ensures estimation convergence. Moreover, it can be used to generate the Granger causality graphs. In this paper, first, we introduce the core concepts in the directed information framework. Second, we present how to conduct causality analysis using directed information measures between two time series. We provide the detailed procedure on how to calculate the DI for two finite time series. The two major steps involved here are optimal bin size selection for data digitization, and probability estimation. Finally, we demonstrate the applicability of DI based causality analysis using both the simulated data and experimental fMRI data, and compare the results with that of the Granger Causality analysis. Our analysis indicates that GC analysis is effective in detecting linear or nearly linear causal relationship, but may have difficulty in capturing nonlinear causal relationships. On the other hand, DI based causality analysis is more effective in capturing both linear and nonlinear causal relationships. Moreover, it is observed that brain connectivity among different regions generally involves dynamic two-way information transmissions between them. Our results show that when bidirectional information flow is present, DI is more effective than GC to quantify the overall causal relationship.

PMID: 26415198 [PubMed - as supplied by publisher]