Abstract
INTRODUCTION: The brain's spontaneous neural activity can be recorded during rest using resting state functional magnetic resonance imaging (rs-fMRI), and intricate brain functional networks and interaction patterns can be discovered through correlation analysis. As a crucial component of rs-fMRI analysis, effective connectivity analysis (EC) may provide a detailed description of the causal relationship and information flow between different brain areas. It has been very helpful in identifying anomalies in the brain activity of depressed teenagers. METHODS: This study explored connectivity abnormalities in brain networks and their impact on clinical symptoms in patients with depression through resting state functional magnetic resonance imaging (rs-fMRI) and effective connectivity (EC) analysis. We first introduce some common EC analysis methods, discuss their application background and specific characteristics. RESULTS: EC analysis reveals information flow problems between different brain regions, such as the default mode network, the central executive network, and the salience network, which are closely related to symptoms of depression, such as low mood and cognitive impairment. This review discusses the limitations of existing studies while summarizing the current applications of EC analysis methods. Most of the early studies focused on the static connection mode, ignoring the causal relationship between brain regions. However, effective connection can reflect the upper and lower relationship of brain region interaction, and provide help for us to explore the mechanism of neurological diseases. Existing studies focus on the analysis of a single brain network, but rarely explore the interaction between multiple key networks. DISCUSSION: To do so, we can address these issues by integrating multiple technologies. The discussion of these issues is reflected in the text. Through reviewing various methods and applications of EC analysis, this paper aims to explore the abnormal connectivity patterns of brain networks in patients with depression, and further analyze the relationship between these abnormalities and clinical symptoms, so as to provide more accurate theoretical support for early diagnosis and personalized treatment of depression.