Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Psychiatry, University of British Columbia, Vancouver, Canada.
Transl Psychiatry. 2021 Jan 14;11(1):48. doi: 10.1038/s41398-020-01169-7.
Bipolar disorder (BD) and major depressive disorder (MDD) have both common and distinct clinical features, that pose both conceptual challenges in terms of their diagnostic boundaries and practical difficulties in optimizing treatment. Multivariate machine learning techniques offer new avenues for exploring these boundaries based on clinical neuroanatomical features. Brain structural data were obtained at 3 T from a sample of 90 patients with BD, 189 patients with MDD, and 162 healthy individuals. We applied sparse partial least squares discriminant analysis (s-PLS-DA) to identify clinical and brain structural features that may discriminate between the two clinical groups, and heterogeneity through discriminative analysis (HYDRA) to detect patient subgroups with reference to healthy individuals. Two clinical dimensions differentiated BD from MDD (area under the curve: 0.76, P < 0.001); one dimension emphasized disease severity as well as irritability, agitation, anxiety and flight of ideas and the other emphasized mostly elevated mood. Brain structural features could not distinguish between the two disorders. HYDRA classified patients in two clusters that differed in global and regional cortical thickness, the distribution proportion of BD and MDD and positive family history of psychiatric disorders. Clinical features remain the most reliable discriminant attributed of BD and MDD depression. The brain structural findings suggests that biological partitions of patients with mood disorders are likely to lead to the identification of subgroups, that transcend current diagnostic divisions into BD and MDD and are more likely to be aligned with underlying genetic variation. These results set the foundation for future studies to enhance our understanding of brain-behavior relationships in mood disorders.
双相情感障碍（BD）和重度抑郁症（MDD）既有共同的又有不同的临床特征，这在诊断边界方面提出了概念上的挑战，在优化治疗方面也存在实际困难。多元机器学习技术为探索这些边界提供了新的途径，其依据是临床神经解剖特征。从 90 名 BD 患者、189 名 MDD 患者和 162 名健康个体的样本中获得了 3T 脑结构数据。我们应用稀疏偏最小二乘判别分析（s-PLS-DA）来识别可能区分两种临床组的临床和脑结构特征，并通过鉴别分析（HYDRA）检测参考健康个体的患者亚组。两个临床维度将 BD 与 MDD 区分开来（曲线下面积：0.76，P < 0.001）；一个维度强调疾病严重程度以及易激惹、激越、焦虑和思维奔逸，另一个维度则主要强调情绪升高。脑结构特征无法区分两种疾病。HYDRA 将患者分为两个聚类，它们在全脑和区域皮质厚度、BD 和 MDD 的分布比例以及精神障碍阳性家族史方面存在差异。临床特征仍然是 BD 和 MDD 抑郁最可靠的鉴别属性。脑结构研究结果表明，情绪障碍患者的生物学分类可能导致亚组的识别，这些亚组超越了当前的诊断划分，更有可能与潜在的遗传变异相吻合。这些结果为未来的研究奠定了基础，以增强我们对情绪障碍中脑-行为关系的理解。
