Abstract
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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Friston KJ (1994) Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp 2(1–2):56–78
McIntosh AR, Gonzalez-Lima F (1994) Structural equation modeling and its application to network analysis in functional brain imaging. Hum Brain Mapp 2(1–2):2–22
Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. NeuroImage 19(4):1273–1302
Zheng X, Rajapakse JC (2006) Learning functional structure from fMR images. NeuroImage 31(4):1601–1613
Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700–711
Schwarz G (1978) Estimate the dimension of a model. Ann Stat 6:461–464
Olmsted SM (1983) On representing and solving decision problems, in EES department. Stanford University, Stanford, CA
Shachter RD, Kenley CR (1989) Gaussian influence diagrams. Manage Sci 35(5):527–550
Karahoca D, Karahoca A, Yavuz Ö (2012) An early warning system approach for the identification of currency crises with data mining techniques. Neural Comput Appl. doi:10.1007/s00521-012-1206-9
Sohrabi B, Mahmoudian P, Raeesi I (2011) A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system. Neural Comput Appl 21:1017–1029. doi:10.1007/s00521-011-0674-7
Farid M, HosseinAbadi MM, Yazdani-Chamzini A et al (2012) Developing a new model based on neuro-fuzzy system for predicting roof fall in coal mines. Neural Comput Appl. doi:10.1007/s00521-012-1271-0
Li R, Chen K, Zhang N (2009) Effective connectivity analysis of default mode network based on the Bayesian network learning approach. Proc SPIE 7262
Rajapakse JC, Zhou J (2007) Learning effective brain connectivity with dynamic Bayesian networks. NeuroImage 37(3):749–760
Burge J, Lane T, Link H (2009) Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp 30(1):122–137
Zeng Z and Ji Q (2010) Knowledge based activity recognition with dynamic bayesian network. Computer Vision–ECCV
Bhattacharya S, Ringo Ho MH, Purkayastha S (2006) A Bayesian approach to modeling dynamic effective connectivity with fMRI data. NeuroImage 30(3):794–812
Shachter RD, Kenley CR (1989) Ganssin influence diagrams. Manage Sci 35:527–550
Geiger D, Heckerman D (1994) Learning Gaussian networks. Technical report MSR-TR-94-10
Heckerman D (1995) A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06
Husmeier D (2003) Inferring dynamic Bayesian networks with MCMC. Available from: http://www.bioss.ac.uk/~dirk/software/DBmcmc/
Greicius MD (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci 101(13):4637–4642
Jiao Q, Lu G, Zhang Z et al (2011) Granger causal influence predicts BOLD activity levels in the default mode network. Hum Brain Mapp 32(1):154–161
Forsberg A, Engler H, Almkvist O et al (2008) PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging 29:1456–1465
Kobayashi Y, Amaral DG (2003) Macaque monkey retrosplenial cortex: II. Cortical afferents. J Comp Neurol 466:48–79
Kobayashi Y, Amaral DG (2007) Macaque monkey retrosplenial cortex: III. Cortical efferents. J Comp Neurol 502:810–833
Acknowledgments
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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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). https://doi.org/10.1007/s00521-013-1465-0
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DOI: https://doi.org/10.1007/s00521-013-1465-0