Constructing Brain Connectivity Model Using Causal Network Reconstruction Approach - Saetia, Supat; Yoshimura, Natsue; Koike, Yasuharu (Frontiers in Neuroinformatics, 2021)
Studying brain function is a challenging task. In the past, we can only study brain anatomical structure postmortem or infer brain function from clinical data of patient with brain injury. Nowadays technology, such as functional magnetic resonance imaging (fMRI), enable non-invasive brain activity observation. Several approaches have been proposed to interpret brain activity data. Brain connectivity model is a graphical tool that represents the interaction between brain regions during certain state. It depicts how a brain region cause changes to other part of the brain, which can be implied as information flow. We can use this model to help us interpret how brain works. There are several mathematical framework that can be used to infer connectivity model from brain activity signal. Granger causality is one of such approaches, and is one of the first that has been applied to brain activity data. However, due to the concept of the framework such as the use of pairwise correlation, combined with the limitation of brain activity data, such as low temporal resolution in case of fMRI signal, it makes the interpretation of the connectivity difficult. Here we propose the application of Tigramite causal discovery framework on fMRI data. The Tigramite framework uses measures such as causal effect to analyse causal relation in the system. This enable the framework to identify both direct and indirect pathway or connectivity. In this paper, we applied the framework to Human Connectome Project motor rask-fMRI dataset. Then, we show the results and discuss how the framework improves interpretability of the connectivity model. We hope that this framework will help us understand more complex brain function such as memory, consciousness, or resting-state of the brain in the future.