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A new dynamic Bayesian network approach for determining effective connectivity from fMRI data

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Two techniques based on the Bayesian network (BN), Gaussian Bayesian network and discrete dynamic Bayesian network (DBN), have recently been used to determine the effective connectivity from functional magnetic resonance imaging (fMRI) data in an exploratory manner and to provide a new method for exploring the interactions among brain regions. However, Gaussian BN ignores the temporal relationships of interactions among brain regions, while discrete DBN loses a great deal of information by discretizing the data. To overcome these limitations, the current study proposes a new BN method based on Gaussian assumptions, termed Gaussian DBN, to capture the temporal characteristics of connectivity with less associated loss of information. A set of synthetic data were generated to measure the robustness of this method to noise, and the results were compared with discrete DBN. In addition, real fMRI data obtained from twelve normal subjects in the resting state was used to further demonstrate and validate the effectiveness of the Gaussian DBN method. The results demonstrated that the Gaussian DBN was more robust than discrete DBN and an improvement over BN.

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This work was supported by the Funds of the National Natural Science Foundation of China (61210001, 61222113), Program for New Century Excellent Talents in University (NCET-12-0056), and program of State Key Laboratory of Cognitive Neuroscience and Learning (CNLYB1216).

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Correspondence to Xia Wu.

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Wu, X., Wen, X., Li, J. et al. A new dynamic Bayesian network approach for determining effective connectivity from fMRI data. Neural Comput & Applic 24, 91–97 (2014).

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