抑郁症脑功能网络微状态及其动态特性的研究

Objective marks is necessary for depression diagnosis, which mainly depends on symptom evaluation at present. It has been accepted that funcitonal network abnormality exists in the depression, hopfully as a biomark for desease diagnosis in the future. However, few study was reported on functional network dynamics, especially under short period such as in millisecond. It is supposed that different modes of functional network might exist when one was dealling with a certain stimuli, while the distribution of such microstate over temporal and frequency fields might reveal the inherent abnormaity in depression. Our study include: 1) cross-frequncy coupling and effecitive connectivity analysis on depression; 2) microstate mode extraction and dynamic analysis over real-time and averaged MEG signals; 3) no-linear mapping mechanism development between the microstate mode dynamics and the depression severity. From this study, desease related abnormal microsate modes and their dynamics can be detected in order to theoretically support the depression diagnosis and decision making.

目前抑郁症的临床诊断仍依靠临床症状和相关量表评定，缺少客观的生物学指标。抑郁症相关神经环路探讨对疾病的认识有积极作用，但目前尚未有定论，尤其是功能网络疾病相关动态特性少有探索。本项目将利用脑磁图高时间分辨率信息，发展相关技术，挖掘脑功能网络在处理外部刺激过程中的若干种微状态，并分析这些微状态在时频分布上与疾病密切相关的动态性能。研究内容为：1）从脑磁图交叉频段的耦合和神经环路效能连接信息中分析抑郁症相关特异性；2）发展相应技术，挖掘出对应不同子功能的脑网络短时微状态,探讨疾病导致的模式变异及其实时和非实时的标记性动态改变；3）以功能网络关键模式特征为核心，建立功能网络微状态动态性能和疾病状态间的非线性匹配机制。通过本项目，从功能网络微状态出发，探究疾病相关神经环路关键模式及其动态性能，为抑郁症诊断和治疗决策提供了理论依据。

针对抑郁症的临床诊断仍依靠临床症状和相关量表评定，缺少客观的生物学指标现状，项目从功能网络疾病相关动态特性进行了探索。本项目利用脑磁图高时间分辨率信息，发展相关技术，挖掘脑功能网络在处理外部刺激过程中的若干种微状态，并分析这些微状态在时频分布上与疾病密切相关的动态性能。研究内容及其关键结果为：1）建立了脑磁图交叉频段的耦合和神经环路效能连接方式，并从中获得抑郁症相关特异性；2）挖掘出脑网络短时微状态分析技术,并发现患者的默认网络额外存在一个状态，用于补偿默认子网络之间的交互异常；3）以功能网络关键模式特征为核心，建立了功能网络微状态动态性能和疾病状态间的非线性匹配机制并开放了应用系统。