Incorporating Error-Rate-Controlled Prior in Modelling Brain Functional Connectivity
Inferring effective connectivity using fMRI is of in-creasing 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 discoverednetwork 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.
Junning Li Z.Jane Wang Martin J.McKeown
Department of Electrical and Computer Engineering University of British Columbia Canada Department of Medicine (Neurology)Pacific Parkinsons Research Centre University of British Columbia
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）