Nonlinear dynamic causal models for fMRI. Export

@article{Ste2008, abstract = {Models of effective connectivity characterize the influence that neuronal populations exert over each other. Additionally, some approaches, for example Dynamic Causal Modelling (DCM) and variants of Structural Equation Modelling, describe how effective connectivity is modulated by experimental manipulations. Mathematically, both are based on bilinear equations, where the bilinear term models the effect of experimental manipulations on neuronal interactions. The bilinear framework, however, precludes an important aspect of neuronal interactions that has been established with invasive electrophysiological recording studies; i.e., how the connection between two neuronal units is enabled or gated by activity in other units. These gating processes are critical for controlling the gain of neuronal populations and are mediated through interactions between synaptic inputs (e.g. by means of voltage-sensitive ion channels). They represent a key mechanism for various neurobiological processes, including top-down (e.g. attentional) modulation, learning and neuromodulation. This paper presents a nonlinear extension of DCM that models such processes (to second order) at the neuronal population level. In this way, the modulation of network interactions can be assigned to an explicit neuronal population. We present simulations and empirical results that demonstrate the validity and usefulness of this model. Analyses of synthetic data showed that nonlinear and bilinear mechanisms can be distinguished by our extended DCM. When applying the model to empirical fMRI data from a blocked attention to motion paradigm, we found that attention-induced increases in V5 responses could be best explained as a gating of the V1-->V5 connection by activity in posterior parietal cortex. Furthermore, we analysed fMRI data from an event-related binocular rivalry paradigm and found that interactions amongst percept-selective visual areas were modulated by activity in the middle frontal gyrus. In both practical examples, Bayesian model selection favoured the nonlinear models over corresponding bilinear ones.}, address = {Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK; Branco Weiss Laboratory (BWL), Institute for Empirical Research in Economics, University of Zurich, Switzerland.}, author = {Stephan, Klaas Enno E. and Kasper, Lars and Harrison, Lee M. and Daunizeau, Jean and den Ouden, Hanneke E. and Breakspear, Michael and Friston, Karl J.}, citeulike-article-id = {2938306}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neuroimage.2008.04.262}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18565765}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18565765}, day = {15}, doi = {10.1016/j.neuroimage.2008.04.262}, issn = {1095-9572}, journal = {NeuroImage}, keywords = {binocular-rivalry, dcm, first-paper, fmri, nonlinear, own-publication, uzh-choice}, month = {August}, number = {2}, pages = {649--662}, posted-at = {2008-09-15 23:59:01}, priority = {0}, title = {Nonlinear dynamic causal models for fMRI.}, url = {http://dx.doi.org/10.1016/j.neuroimage.2008.04.262}, volume = {42}, year = {2008} }

TY - JOUR ID - Ste2008 L3 - citeulike-article-id:2938306 N2 - Models of effective connectivity characterize the influence that neuronal populations exert over each other. Additionally, some approaches, for example Dynamic Causal Modelling (DCM) and variants of Structural Equation Modelling, describe how effective connectivity is modulated by experimental manipulations. Mathematically, both are based on bilinear equations, where the bilinear term models the effect of experimental manipulations on neuronal interactions. The bilinear framework, however, precludes an important aspect of neuronal interactions that has been established with invasive electrophysiological recording studies; i.e., how the connection between two neuronal units is enabled or gated by activity in other units. These gating processes are critical for controlling the gain of neuronal populations and are mediated through interactions between synaptic inputs (e.g. by means of voltage-sensitive ion channels). They represent a key mechanism for various neurobiological processes, including top-down (e.g. attentional) modulation, learning and neuromodulation. This paper presents a nonlinear extension of DCM that models such processes (to second order) at the neuronal population level. In this way, the modulation of network interactions can be assigned to an explicit neuronal population. We present simulations and empirical results that demonstrate the validity and usefulness of this model. Analyses of synthetic data showed that nonlinear and bilinear mechanisms can be distinguished by our extended DCM. When applying the model to empirical fMRI data from a blocked attention to motion paradigm, we found that attention-induced increases in V5 responses could be best explained as a gating of the V1-->V5 connection by activity in posterior parietal cortex. Furthermore, we analysed fMRI data from an event-related binocular rivalry paradigm and found that interactions amongst percept-selective visual areas were modulated by activity in the middle frontal gyrus. In both practical examples, Bayesian model selection favoured the nonlinear models over corresponding bilinear ones. IS - 2 JF - NeuroImage SN - 1095-9572 AD - Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK; Branco Weiss Laboratory (BWL), Institute for Empirical Research in Economics, University of Zurich, Switzerland. EP - 662 TI - Nonlinear dynamic causal models for fMRI. VL - 42 SP - 649 KW - binocular-rivalry KW - dcm KW - first-paper KW - fmri KW - nonlinear KW - own-publication KW - uzh-choice AU - Stephan, Klaas Enno AU - Kasper, Lars AU - Harrison, Lee AU - Daunizeau, Jean AU - den Ouden, Hanneke AU - Breakspear, Michael AU - Friston, Karl PY - 2008/08/15/ UR - http://dx.doi.org/10.1016/j.neuroimage.2008.04.262 ER -