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KMID : 0605720170230010005
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Journal of the Korean Society of Biological Therapies in Psychiatry
2017 Volume.23 No. 1 p.5 ~ p.12
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Depression in the Perspective of Large-Scale Neural Networks
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Kim Yang-Tae
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Abstract
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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.
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KEYWORD
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Neural networks, Depression, Psychodynamic, Treatment
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