Neural mechanisms underlying susceptibility factors in depression

Abstract

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 depression-related 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.