A nonlinear identification method to study effective connectivity in functional MRI

作者：Li Xingfeng; Marrelec Guillaume; Hess Robert F; Benali Habib^*

来源：Medical Image Analysis, 2010, 14(1): 30-38.

DOI：10.1016/j.media.2009.09.005

• fMRI
• Effective connectivity
• Nonlinear system identification
• Granger causality test
• Nonlinear autoregressive exogenous model

In this paper we propose a novel approach for characterizing effective connectivity in functional magnetic resonance imaging (fMRI) data. Unlike most other methods, our approach is nonlinear and does not rely on a priori specification of a model that contains structural information of neuronal populations. Instead, it relies on a nonlinear autoregressive exogenous model and nonlinear system identification theory; the model's nonlinear connectivities are determined using a least squares method. A statistical test was developed to quantify the significance of the influence that regions exert on one another. We compared this approach with a linear method and applied it to the human visual cortex network. Results show that this method can be used to model nonlinear interaction between different regions for fMRI data.

• 出版日期2010-2