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IEEE Trans Med Imaging · Jun 2008
Probabilistic framework for brain connectivity from functional MR images.
- Jagath C Rajapakse, Yang Wang, Xuebin Zheng, and Juan Zhou.
- School of Computer Engineering and the BioInformatics Research Centre, Nanyang Technological University, 50 Nanyang Avenue,639798 Singapore. asjagath@ntu.edu.sg
- IEEE Trans Med Imaging. 2008 Jun 1; 27 (6): 825-33.
AbstractThis paper unifies our earlier work on detection of brain activation (Rajapakse and Piyaratna, 2001) and connectivity (Rajapakse and Zhou, 2007) in a probabilistic framework for analyzing effective connectivity among activated brain regions from functional magnetic resonance imaging (fMRI) data. Interactions among brain regions are expressed by a dynamic Bayesian network (DBN) while contextual dependencies within functional images are formulated by a Markov random field. The approach simultaneously considers both the detection of brain activation and the estimation of effective connectivity and does not require a priori model of connectivity. Experimental results show that the present approach outperforms earlier fMRI analysis techniques on synthetic functional images and robustly derives brain connectivity from real fMRI data.
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