Abnormal Functional Integration of Brain in Depression Evidences from Effective Connectivity Abstract: Functional magnetic resonance imaging (fMRI) is currently the mainstay of neuroimaging in cognitive neuroscience, which could use to understand how different parts of the brain respond to external stimuli by measuring signal changes in the brain. It has a wide range of applications in clinical diagnosis, such as depression disorders. Although depression disorders place an enormous burden on society, and ranked as the fourth leading cause of burden among all diseases, until recently the pathogenesis of depression is not clear. On the one hand, depression is often accompanied with many brain regions or systems abnormality, rather than single region damage. On the other hand, as a nonlinear dynamic system, the human brain is affected by multi-factors, and there are interactions between brain regions, which mask it difficult to understand the complex cognitive process of the brain. So using the method of function integration to dynamic monitor the interaction of multiple brain regions might reveal the pathophysiological mechanism of depression. With the development of the function integration, it has become able to explore the functional brain integration of multiple brain regions, and deeply understand the dynamic mode of the human brain, which represents an important direction of the cognitive neuroscience in the current and future. Functional integration mainly include functional connectivity and effective connectivity. Functional connectivity describes the temporal correlations between spatially remote neurophysiological events, but this method couldn’t reveal the direction of the connection between brain regions; while, effective connectivity mainly reflects the influence one neural system exerts over another, meanwhile, it could reflect how the experimental conditions influence the interaction between the brain regions. That is to say, effective connectivity can describe the dynamic interaction between the brain regions. The main methods of effective connectivity include structural equation model, dynamic causal model, granger causality analysis, psychophysiological interaction and so on. Major depression is characterized by an attentional bias: disproportionately allocate their attention to threatening stimuli and negative memories. Attention bias is accompanied by abnormal connection between the cognitive control system (e.g., DLPFC) and the limbic system (e.g., amygdala). However, it is not yet clear whether this bias represents impaired top-down cognitive control over affective responses, potentially linked to deficits in DLPFC; or enhanced bottom-up responses to affectively laden stimuli that disturb cognitive control mechanisms, potentially linked to deficits in amygdala. By using the methods of effective connection, we can better understanding the dynamic change of these two systems, and provide an effective and specific treatment method for depression. Effective connectivity can be understood from following respects: 1) the concept of the effective connectivity and the necessity of effective connectivity studies; 2) introduction the methods of effective connectivity, including the principles, main contributors, their advantages and drawbacks; 3) the applications to depression; 4) the limitations of the previous studies and the direction for future studies. In the future, effective connectivity will be widely used in basic research and clinical applications, particularly in neural pathway of the diseases and would highly enrich our knowledge about the dynamic integration of the brain regions.