대규모 신경망의 관점에서 본 우울증

Abstract

Recent developments in the emerging science of large-scale neural networks offer a new understanding of a coherent paradigm for cognition. The perspective of large-scale neural networks provides a powerful framework for investigating psychopathology in psychiatric disorders. In a similar vein, altered organizations in large-scale neural networks are shown to play a prominent role in depression. In this respect, this review gives an overview of a diverse literature on depression from the perspectives of large-scale neural networks. First, both definition and function of large-scale neural networks will be provided. Second, from a large-scale neural networks perspective, symptoms of depression will be discussed. Next, the relationship between psychodynamics of depression and altered organizations in large-scale neural networks will be addressed. Lastly, it will be explained how antidepressants and psychotherapy influence on large-scale neural networks. Understanding depression in terms of large-scale neural networks will be expected to provide a better option of treatment for depression.