[抑郁症的神经影像学研究:现状和未来方向]

Asako Mori, Yasumasa Okamoto, Go Okada, Shigeto Yamawaki
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引用次数: 0

摘要

神经成像研究的最新进展使我们能够无创地测量大脑功能。在过去的几十年里,神经影像学测量已被大量应用于阐明重度抑郁症(MDD)的神经生物学机制。由于重度抑郁症是一个广泛的范围,包括各种症状,如情绪低落或失去兴趣,并被认为影响生物异质性人群,我们尚未阐明重度抑郁症的生物学基础。将重度抑郁症视为整体网络异常而不是特定脑区或神经递质缺陷似乎更有希望。我们首先全面回顾现有的神经影像学文献,包括结构和功能磁共振成像、磁共振波谱、单光子发射计算机断层扫描和正电子发射断层扫描研究。然后,我们展示了我们对重度抑郁症的神经影像学研究作为诊断和治疗的生物标志物的潜在应用。我们相信这将有助于开发一个更综合的重度抑郁症神经功能障碍模型。
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[Neuroimaging studies of depression: Current status and future direction.]

Recent advances in neuroimaging studies enable us to measure brain function non-invasively. Over the past several decades, neuroimaging measurement has been substantially applied to elucidate the neurobiological mechanisms underlying major depressive disorder (MDD). Since MDD is a wide spectrum which consists of various symptoms such as low mood or loss of interest and is considered to affect a biologically het- erogeneous population, we have not yet elucidated the biological underpinnings of MDD. It seems to be more promising to consider MDD as an integral network abnormality instead of deficits in specific brain regions or neurotransmitters. We firstly provide a comprehensive review of the extant neuroimaging literature including structural and functional magnetic resonance imaging, magnetic resonance spectroscopy, single pho- ton emission computed tomography, and positron emission tomography studies. Then we show our neuroimaging studies for MDD toward po- tential application as a biomarker for diagnosis and treatment. We believe it will facilitate the development, of a more integrative model of neural dysfunction in MDD.

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Experience-dependent development of visual cortical functions. Experimental disease models for mechanistic understanding and drug discovery for psychiatric disorders. [Basal Ganglia Circuit Mechanisms in Cognitive Learning]. The contribution of neuroplasticity induced in cholinergic neurons of the laterodorsal tegmental nucleus to cocaine addiction. [Neuroimaging studies of depression: Current status and future direction.]
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