## Comparing Dynamic Causal Models (2004)

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Venue: | NEUROIMAGE |

Citations: | 87 - 34 self |

### BibTeX

@ARTICLE{Penny04comparingdynamic,

author = {W. D. Penny and K. E. Stephan and A. Mechelli and K. J. Friston},

title = {Comparing Dynamic Causal Models},

journal = {NEUROIMAGE},

year = {2004},

volume = {22},

pages = {1157--1172}

}

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### Abstract

This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are used to make inferences about effective connectivity from functional Magnetic Resonance Imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both with regard to the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

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Citation Context ...of several alternative models is optimal given the data. Such decisions are of great practical relevance because we still lack detailed knowledge about the anatomical connectivity of the human brain (=-=Passingham et al., 2002-=-). Decisions about the intrinsic connectivity of DCMs are therefore usually based on inferring connections from supposedly equivalent areas in the Macaque brain for which the anatomical connectivity i... |

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Citation Context ...about the intrinsic connectivity of DCMs are therefore usually based on inferring connections from supposedly equivalent areas in the Macaque brain for which the anatomical connectivity is well known =-=[42]-=-. This procedure has many pitfalls, however, including a multitude of incompatible parcellation schemes and frequent uncertainties about the homology and functional equivalence of areas in the brains ... |

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Citation Context ...quivalence of areas in the brains of man and monkey. This problem may be less severe in sensory systems, but is of particular importance for areas involved in higher cognitive processes like language =-=[1]-=-. Thus, there are often considerable degrees of freedom when defining the connectional structure of DCMs of the human brain. We show how Bayes factors can be used to guide the modeller in making such ... |

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Citation Context ...lved in the processing of various stimulus attributes, but is particularly sensitive to motion information, i.e. V5 shows increased responses to inputs from V1 whenever the applied stimulus is moving =-=[4]-=- (Fig. 3B). In DCM, this bottom-up process would be modeled by modulating the V1 V5 forward connection by a vector that indicates when the stimuli were moving (MOT, Fig. 3A). Importantly, however, not... |

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Citation Context ...here Λ is an L × L diagonal matrix with Λii denoting error variance in the ith region. 2.4 Modelling bottom-up and top-down effects Many applications of DCM, both in this article and in previous wo=-=rk [18, 31], refer -=-to ”bottom-up” and ”top-down” processes in the visual system, and we envisage that a large number of future applications of DCM will address the same issue. Some of the possible DCM architectu... |

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Citation Context ...nections [5, 24], such that (ii) the responsiveness of these V5 neurons to simultaneous inputs from V1 is enhanced, possibly through interactions between dendritic and somatic postsynaptic potentials =-=[41]-=- (see Fig. 3D). Although this level of detail cannot currently be modeled in DCMs, we can describe precisely the same mechanism at a coarser level by allowing the V1 V5 forward connection 8sto be modu... |

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Citation Context ...emispheres. Recent studies have indicated, however, that asymmetries in the intra-hemispheric functional couplings may be an equally important determinant of hemispheric specialization (Friston 2003) =-=[30, 43]-=-. This section demonstrates the ability of DCM to correctly identify asymmetries of modulatory intra-hemispheric connectivity despite the presence of reciprocal inter-hemispheric connections between h... |

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Citation Context ... is mediated neurophysiologically by feedback connections from higher areas (represented by ”X” in Fig. 3D) that (i) influence those neurons in V5 which receive inputs from V1 via forward connecti=-=ons [5, 24]-=-, such that (ii) the responsiveness of these V5 neurons to simultaneous inputs from V1 is enhanced, possibly through interactions between dendritic and somatic postsynaptic potentials [41] (see Fig. 3... |

7 |
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Citation Context ... of several alternative models is optimal given the data. Such decisions are of great practical relevance because we still lack detailed knowledge about the anatomical connectivity of the human brain =-=[34]-=-. Decisions about the intrinsic connectivity of DCMs are therefore usually based on inferring connections from supposedly equivalent areas in the Macaque brain for which the anatomical connectivity is... |

5 |
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Citation Context ...ocesses may, at first glance, seem at odds with traditional cognitive theories that relate bottom-up processes to so-called ‘‘forward’’ connections and top-down processes to ‘‘backward’’ connections (=-=Ungerleider et al., 1998-=-). Here we try to clarify this relationship using some simple examples from the visual system and emphasize the need for precise terminology when distinguishing between the levels of anatomical connec... |

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Citation Context ...s in 12sclassical statistics (eg. p < 0.05), so one has developed around the use of Bayes factors. Raftery [36], for example, presents an interpretation of Bayes factors as shown in Table 1. Jefferys =-=[23]-=- presents a similar grading for the comparison of scientific theories. These partitionings are somewhat arbitrary but do provide rough descriptive statements. Bayes factors can also be directly interp... |