摘要
抑郁症是一种常见的精神类疾病,导致其发病的影响因素众多,但其神经机制尚不清楚.本文首先回顾了与抑郁易感性有关的认知理论,包括Beck提出的认知模型理论以及Abramson提出的抑郁无望理论模型.其次,从遗传因素、外部环境因素以及个体心理因素3个方面阐述了易感性因素对抑郁影响及其作用的神经机制.最后,基于认知神经科学的现状和局限性并结合抑郁研究现状,提出了抑郁研究可能面临的挑战和对未来研究展望.具体而言,未来研究应从统计建模的角度出发,整合基因-脑影像-行为大数据,先从横向研究角度比较探讨和分析抑郁形成的各种影响因素,建立有效的因素模型;再从纵向跟踪的角度探明各种易感因素在抑郁发生中的作用机制,建立抑郁的预测模型;最终实现基于基因-脑影像-行为大数据的融合,从而对抑郁的发生和发展进行有效的预测和早期干预,降低抑郁症的发病率.
Major depressive disorder（MDD） is one of the most common mental disorders. The World Health Organization currently estimates that there are approximately 350 million depressive patients worldwide. MDD not only affects the life quality of individuals and their families, but also brings about a heavy financial burden to the society. The factors that contribute to the onset of MDD are complex and its underlying neural mechanisms have remained unclear. The modern medical science proposes ＂early detection, early treatment＂ of diseases. Therefore, early prediction and diagnosis of the MDD onsets are becoming a trend in depressive studies. This review firstly sketched the related cognitive theories of susceptibility in depression, including Beck＇s Cognitive Model of Depression and Abramson＇s Theory of Helplessness and Depression. Secondly, we elaborated the way in which susceptibility factors exerted their influences on depression and its underlying neural mechanisms from the perspectives of gene, external environment and individual psychological factors, respectively. Among the depressionrelated candidate genes, 5-HTTLPR（serotonin-transporter-linked polymorphic region） plays a critical role in modulating the cognitive-affective system which is associated with depression. The factors of the external environment which might lead to depression mainly involve the perceived stress and the social support when individuals experience negative life events. Those factors exert lasting and overwhelming influences on the structure and function of brain regions which are related to abnormalities of the cognitive-affective modulating system. Stress may affect the hippocampus and the prefrontal cortex which are closely related to depression, while social support involves the prefrontal cortex, the anterior cingulate cortex and the corpus striatum which are associated with cognition-affection modulating system. The individual psychological factors that might contribute to depression include rumination, attribution, neuroticism and extraversion and are believed to be associated with the prefrontal cortex, the cingulate cortex, the subcortical nuclei such as hippocampus and amygdala. Finally, we analyzed the limitations of cognitive neuroscience, such as low statistical testing power, verifiability and reproducibility, and the fact that multimodal brain imaging is currently not sufficient to uncover the neural mechanisms of the brains. Based on the status quo of research into cognitive neuroscience and depression, we put forward the prospective challenges and outlooks for future research into depression. Specifically, the future research is expected to start from the perspective of statistical modeling and to aim at gene-brain-behavior integration. Then, cross-sectional studies are expected to explore and analyze the various factors affecting the depression and to establish an effective factorial model. Structural equation model（SEM） and machine learning model are effective approaches to build, estimate and verify causal models. On the other hand, the longitudinal studies are expected to ascertain the roles of various risk factors in depressive progression and to establish the predictive model of depression. The clinical practice of the built model is yet to be verified from the perspective of intervention and treatment. For instance, transcranial magnetic stimulation（TMS）, transcranial electrical stimulation（TES） and other approaches could be used to verify the effects of treatment. Eventually, based on the gene-brain-behavior interplay, it could provide a valuable model for predicting the occurrence and development of depression, conducting early intervention and thus reducing the incidence of depression.
出处
《科学通报》
EI
CAS
CSCD
北大核心
2016年第6期654-667,共14页
Chinese Science Bulletin
基金
国家自然科学基金(31571137
31271087)
重庆市自然科学基金(cstc2015jcyj A10106)
中央高校基本业务费创新团队项目(SWU1509383)资助
关键词
抑郁
易感性因素
神经机制
预测模型
depression
susceptibility factors
neural mechanisms
predictive model
