Probabilistic framework for brain connectivity from functional MR images.
This 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.
School of Computer Engineering and the BioInformatics Research Centre, Nanyang Technological University, 50 Nanyang Avenue,639798 Singapore. asjagath@ntu.edu.sg
Jagath C
JC
Rajapakse
Yang
Y
Wang
Xuebin
X
Zheng
Juan
J
Zhou
D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology*; D009434:Neural Pathways / Q000033:anatomy & histology / Q000502:physiology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity