A nonlinear identification method to study effective connectivity in functional MRI.
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.
Li
Xingfeng
X
Inserm, UPMC Univ Paris 06, UMR_S 678, Laboratoire d'Imagerie Fonctionnelle, and LINeM, GHU Pitié-Salpêtrière, 91 bd de l'Hôpital, F-75634 Paris Cedex 13, France.
Marrelec
Guillaume
G
Hess
Robert F
RF
Benali
Habib
H
D000465:Algorithms; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009434:Neural Pathways / Q000033:anatomy & histology / Q000502:physiology*; D017711:Nonlinear Dynamics; D014793:Visual Cortex / Q000033:anatomy & histology / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*