Department of Psychiatry, College of Medicine, Korea University, Seoul, Republic of Korea.
Department of Psychiatry, Gachon University Gil Medical Center, Incheon, Republic of Korea.
Prog Neuropsychopharmacol Biol Psychiatry. 2018 Jan 3;80(Pt B):71-80. doi: 10.1016/j.pnpbp.2017.06.024. Epub 2017 Jun 23.
Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future.
心境障碍是一组高发的精神障碍，会给社会经济带来巨大负担。目前有多种方法可用于确定心境障碍的病因、症状和治疗学的潜在机制；然而，神经影像学研究通过观察活体大脑为心境障碍的神经基础提供了最直接的证据。前额叶皮层、海马体、杏仁核、丘脑、腹侧纹状体和胼胝体与抑郁症和双相情感障碍有关。确定这些解剖区域对抑郁症和双相情感障碍的独特和共同贡献，拓宽并深化了我们对心境障碍的理解。然而，神经影像学研究结果在多大程度上有助于现实环境中的临床实践尚不清楚。由于传统或非机器学习 MRI 研究分析了组间差异，因此不可能直接将研究结果转化为临床实践；所获得的知识与该疾病有关，但与个体无关。另一方面，机器学习方法可以实现个体水平的分类。在过去的二十年中，许多研究从诊断和治疗反应的角度报告了基于机器学习的神经影像学研究的分类准确性。然而，对于基于机器学习的脑 MRI 方法在现实临床环境中的应用，需要考虑几个主要问题。疾病持续时间和药物、临床亚型和异质性、合并症以及成本效益的继发性变化限制了当前机器学习发现的推广。需要对临床和诊断亚型进行精细分类。此外，由于该方法不可避免地受到样本量的限制，未来需要多地点参与和数据共享。
