抑郁障碍的核心脑机制—基于fMRI元分析的证据

The Core Neural Mechanisms underlying Depression Disorder: A Meta-analysis of f MRI Studies

抑郁症又称抑郁障碍,是情感障碍的主要类型之一.其典型临床症状包括心境低落、认知功能损伤、思维迟缓、意志活动减退等.抑郁症的病因复杂,受到生物、心理和环境等多因素的影响.近年随着脑影像技术,特别是功能性磁共振的发展,对抑郁症背后的神经机制已有了很多的了解.但越来越多的实验结果并没有带来聚敛性的证据.相反,更多的脑区被发现与抑郁障碍有关.本文尝试利用Neurosynth平台,分析总结特异于抑郁障碍的核心脑机制.Neurosynth是基于自然语言处理和机器学习的无偏的全自动化元分析技术,其最重要的特点是可以根据现有的脑激活模式推断相应的心理认知过程,即逆向推断.元分析流程可以大体分为6步:利用自然语言处理技术读取坐标;离析全文,根据关键词挑选相关文献;人工检查,排除无关文献;分类,根据关键词,将所有的激活坐标分为两类:与抑郁障碍有关和与抑郁障碍无关;元分析,计算和比较与抑郁障碍相关和无关的坐标;生成统计推断结果.本次元分析包含307篇抑郁障碍的文献,137篇重度抑郁障碍的文献和252篇焦虑障碍的文献,发现最特异于抑郁障碍的三大脑区:杏仁核、腹内侧前额叶和背外侧前额叶.具体而言,杏仁核的过度激活体现了过强的情绪加工;腹内侧前额叶的过度激活反映了抑郁患者过度自我关注;而背外侧前额叶的激活减弱则体现了抑郁患者执行控制功能的缺陷.进一步的辨别比较表明,杏仁核和腹内侧前额叶的过度激活也是焦虑障碍的神经机制,逆向推断表明杏仁核和腹内侧前额叶都有一定的几率能推断焦虑障碍和抑郁障碍.其中最特异于抑郁障碍(相对于焦虑障碍)的是背外侧前额叶的功能异常,无法抑制过敏化的情绪加工是抑郁障碍的重要脑机制.

Depression disorder is a mental disorder characterized by a pervasive and persistent low mood, and is accompanied by low self-esteem and by a loss of interest or pleasure in normally enjoyable activities. Biological, psychological, and social factors all play a role in inducing depression. In the last decades, the rapid growth of literatures on neuroimaging psychiatry has led to major advances in our understanding of affective disorder such as depression. However, it has also made it increasingly difficult to aggregate and synthesize neuroimaging findings to reach a conclusive agreement. We try to locate the core nerural mechanisms that are specific to depression disorder based on extant literature, by employing a newly developed automated brain-mapping framework—Neurosynth that uses text-mining, meta-analysis and machine-learning techniques to decode broad cognitive states from brain activity（i.e., reverse interference）. The processing stream involves the following six steps： Activation coordinates are extracted from published neuroimaging articles using an automated parser. The full text of all articles is parsed, and each article is ＂tagged＂ with a set of terms that occur at a high frequency in that article. A list of several thousand terms that occur at high frequency in 20 or more studies are generated. For each term of interest（e.g., ＂depression＂）, the entire database of coordinates is divided into two sets： those that occur in articles containing the term and those that do not. A giant meta-analysis is performed comparing the coordinates reported for studies with and without the term of interest. In addition to producing statistical inference maps（i.e., z and p value maps）, we also compute posterior probability maps, which display the likelihood of a given term being used in a study if activation is observed at a particular voxel. We repeat the series of steps for depression, anxiety as well as major depression. In sum, the meta-analysis suggests that amygdala, VMPFC and DLPFC are the core nerural mechanisms underlying depression disorder. Among them, differential anlysis indicates that the dysfunction of amygdala and VMPFC is also the key brain regions for other affective disorder such as anxiety, but the malfunction of DLPFC is most predictive and specific to depression disorder. The inability to down-regulate emotion reactions may be the key neural mechanism for this particular disorder. Thus, our study provides clues for future research on neural basis underlying depression disorder, with a special emphasis on DLPFC.