基于动态因果模型（DCM）功能连接参数的抑郁症识别

epression recognition based on fMRI provide the possibility for objective diagnosis of disease. Dynamic causal modeling (DCM) can explore the effective connectivity between brain regions via modeling on neural activity. The abnormal of connectivity parameters may relate to depression status. This paper studies the effective connectivity between anterior cingulate cortex, right inferior frontal gyrus and hippocampus, and then utilized such connectivity parameteres under sad stimuli for depression recognition model. The rsults demonstrated the their ability for depression recognition.