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Probabilistic Boolean Networks (PBNs) have recently been applied to infer functional connectivity between brain regions of interested (ROIs), and to identify the existence of connectivity abnormality in Parkinson's Disease (PD). In addition to PBNs' promising application in inferring significant brain connections, PBN modeling for brain ROIs also enables researchers to study dynamic activities of the system under stochastic condition, gaining essential information regarding asymptotic behaviors of ROIs for potential therapeutic intervention in PD. In this paper, we will present a PBN model for fMRI analysis and study its asymptotic behavior. The PBN results indicate significant differences in asymptotic behaviors between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamical network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention.