No. 923

Title:

Evaluating directed connectivity of BOLD signals applying the multivariate autoregressive model and relative power contribution

Author(s):

Yamashita, Okito(ATR Computational Neuroscience Labs), Sadato, Norihiro(National Institute for Physiological Science/JST),
Okada, Tomohisa(Institute of Biomedical Research and Innovation), Ozaki, Tohru(ISM)

Key words:

Causality; fMRI; Directed connectivity; Multivariate autoregressive model (with exogenous variables); Relative power contribution.

Abstract:

In this article, we propose a statistical method to evaluate directed connectivity using functional magnetic-resonance imaging (fMRI) data. The multivariate autoregressive (MAR) model was combined with the relative power contribution (RPC) in this analysis. The MAR model was fitted to the data to specify the direction of connections, and the RPC quantifies the strength of connections. As the RPC is computed in the frequency domain, we can evaluate the connectivity for each frequency component. From this we can establish whether the specified connections represent low- or high-frequency connectivity, which cannot be examined solely using the estimated MAR coefficients. We applied this analysis method to fMRI data obtained during visual motion tasks, confirming previous reports of bottom-up connectivity. Furthermore, we used the MAR model with exogenous variables (MARX) to extend our understanding of these data, and to show how the input to V1 transfers to higher cortical areas.