Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230026, China.
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China.
Psychiatry Res. 2023 Dec;330:115598. doi: 10.1016/j.psychres.2023.115598. Epub 2023 Nov 8.
Major depressive disorder (MDD) is a heterogeneous syndrome, and understanding its neural mechanisms is crucial for the advancement of personalized medicine. However, conventional subtyping studies often categorize MDD patients into a single subgroup, neglecting the continuous interindividual variations. This implies a pressing need for a dimensional approach. 230 first-episode drug-naïve MDD patients and 395 healthy controls were obtained from 5 sites via the Rest-meta-MDD project. A Bayesian model was used to decompose the resting-state functional connectivity (RSFC) into multiple distinct RSFC patterns (refer to as "factors"), and each individual was allowed to express multiple factors to varying degrees (dimensional subtyping). The associations between demographic and clinical variables with the identified factors were calculated. We identified three latent factors with distinct but partially overlapping hypo- and hyper-RSFC patterns. Most participants co-expressed multiple latent factors. All factors shared abnormal RSFC involving the default mode network and frontoparietal network, but the directionality partially differed across factors. All factors were not significantly associated with demographic and clinical variables. These findings shed light on the interindividual variability in MDD and could form the basis for developing novel therapeutic approaches that capitalize on the heterogeneity of MDD.
重度抑郁症（MDD）是一种异质性综合征，理解其神经机制对于推进个性化医学至关重要。然而，传统的亚分型研究通常将 MDD 患者分为单一亚组，忽略了个体间的连续变化。这意味着迫切需要一种维度方法。通过 Rest-meta-MDD 项目，从 5 个地点获得了 230 名首发、未经药物治疗的 MDD 患者和 395 名健康对照者。使用贝叶斯模型将静息态功能连接（RSFC）分解为多个不同的 RSFC 模式（称为“因素”），并允许每个个体以不同程度表达多个因素（维度亚分型）。计算了人口统计学和临床变量与识别因素之间的关联。我们确定了三个具有不同但部分重叠的低和高 RSFC 模式的潜在因素。大多数参与者共同表达了多个潜在因素。所有因素都存在异常的 RSFC，涉及默认模式网络和额顶网络，但在不同因素之间存在部分差异。所有因素均与人口统计学和临床变量无显著相关性。这些发现揭示了 MDD 个体间的可变性，可能为开发利用 MDD 异质性的新型治疗方法奠定基础。
