Incorporating Error-Rate-Controlled Prior in Modelling Brain Functional Connectivity

Inferring effective connectivity using fMRI is of increasing importance for understanding brain function. Dynamic Bayesian network (DBN) modeling has been suggested as a promising and suitable method for this purpose. However, in practice, the success of DBN modeling is largely limited for reasons of intensive computational complexity in large networks and of accurately controlling the error rate of the model structural features. Very recently, we have developed a framework that is able to control the false discovery rate (FDR) of the discovered network edges. In this paper, we propose incorporating an FDR- controlled network-structure prior into DBN modeling for brain functional connectivity. Simulation results show that the proposed method can significantly accelerate the DBN learning process while simultaneously controlling the FDR. Its application to a real fMRI study revealed that certain functional connectivities in Parkinson\´s disease patients\´ brain are "recovered" by L-dopa medication, and also that extra connections between brain regions may represent compensatory mechanisms.