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- W2007421864 abstract "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." @default.
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- W2007421864 date "2010-02-01" @default.
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- W2007421864 title "A nonlinear identification method to study effective connectivity in functional MRI" @default.
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- W2007421864 doi "https://doi.org/10.1016/j.media.2009.09.005" @default.
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