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Objective: Developmental dyscalculia (DD) is a specific learning disability characterized by a deficiency in math learning. It is suggested that dyscalculia might be caused by a connectivity deficit in the brain. The main purpose of our study was to provide a method to assist the diagnosis of dyscalculia using effective connectivity modeled by discrete Dynamic Bayesian Network (dDBN). Methods: dDBN was modeled using the functional magnetic resonance imaging (fMRI) data obtained from twelve children with dyscalculia (8 female, mean age: 11.25) and fifteen typically developing children (9 female, mean age: 11.26) volunteers while they were performing a numerosity task. The approach consisted of three steps. Firstly, imaging data were analyzed by repeated-measures ANOVA with group (dyscalculic/control), number modality (symbolic/non-symbolic tasks), and difficulty (0.7/0.5 ratios) as factors, using SPM12 software. Significantly activated voxel clusters related to the main effect of group were selected as the region of interests (ROI). There were six ROIs: the left and right intraparietal sulci, right anterior cingulate cortex, left medial prefrontal cortex, left hippocampus, and occipital cortex. Secondly, dDBN was used to model the brain connectivity using the fMRI time series data of these ROIs. The effective connectivity is modeled for each task by using two methods, which were virtual-typical-subject and individual-structure methods. Finally, the Bayesian score was used to classify dyscalculic participants from control participants for each method separately. Results: A prefrontal-parietal and hippocampal network in the left hemisphere shows effective connectivity in dyscalculia, which might be the result of their brain's compensation mechanisms. In addition, when the connectivity maps of the symbolic task were used, the classification performance reached 70% accuracy. Conclusion: Hyper-connectivity was observed in children with DD. The significant connectivity differences are mainly reported in the left hemisphere for the symbolic task. This study was supported within the scope of TUBITAK project number 214S069. [ABSTRACT FROM AUTHOR]