Description Length Guided Unified Granger Causality Analysis

In this article, we propose a description length guided unified Granger causality analysis (uGCA) framework for sequential medical imaging. While existing efforts of GCA focused on causal relation design and statistical methods for their improvement, our strategy adopts the minimum description length (MDL) principle in the GCA procedure where the MDL principle offers a unified model selection criteria for deciding the optimal model in the sense of description length. Under this framework, we present different description length forms of linear Granger representations under several coding schemes that all achieve the lower bounds on redundancy, thus producing valid MDL model selection criteria. The efforts are validated using a 5-node network synthetic experiment, illustrating its potential advantage over conventional two-stage approach. The subtle distinction between the performance of different uGCA forms is investigated as well. More importantly, the proposed approach gives a more similar network topology than conventional approach in a challenging fMRI dataset, in which neural correlates of mental calculation elicited by visual and auditory stimulation (respectively) in the same task paradigm, allowing one to evaluate the performance of different GCA methods.