Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
CNS Neurosci Ther. 2018 Nov;24(11):1037-1052. doi: 10.1111/cns.13048. Epub 2018 Aug 23.
Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.
In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.
We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
重度抑郁症（MDD）是导致残疾和发病的最大单一原因，影响全球约 10%的人口。目前，尚无临床上有用的诊断生物标志物能够在早期抑郁发作时将 MDD 与双相障碍（BD）区分开来。因此，基于机器学习探索情绪障碍的转化生物标志物迫在眉睫，尽管具有挑战性，但具有极大的潜力来提高我们对这些疾病的理解。
在这项研究中，我们回顾了用于脑成像分类和预测的流行机器学习方法，并概述了使用磁共振成像数据的研究，特别是用于 MDD 的研究，这些研究用于：(a) 将 MDD 与对照组或其他情绪障碍区分开来，或 (b) 研究个体患者的治疗结果预测因子。最后，还讨论了与 MDD 生物标志物识别相关的挑战、未来方向和潜在局限性，旨在提供全面的概述，帮助读者更好地理解神经影像学数据挖掘在抑郁症中的应用。
我们希望这些努力能够突出治疗范式急需转变的必要性，以指导个性化的最佳临床护理。
