School of Biological Science & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, Jiangsu Province, 210096, China.
Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China.
Eur Radiol. 2023 Jan;33(1):645-655. doi: 10.1007/s00330-022-09004-x. Epub 2022 Aug 18.
Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision.
The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor.
The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032).
Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies.
• The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found.
确定抑郁症患者的临床同质和异质集是促进个体治疗决策的关键。
使用 62 例重性抑郁障碍(MDD)患者和 39 名健康对照的弥散张量成像(DTI)数据构建潜在狄利克雷分配(LDA)贝叶斯模型。另外 48 例 MDD 患者用于验证该模型的稳健性。使用 LDA 模型识别两种典型抗抑郁药物(SSRIs 和 SNRIs)靶向治疗的 MDD 患者的共享和独特影像衍生因素。此外,我们还应用典型相关分析(CCA)将每个因子负荷与汉密尔顿抑郁评定量表(HAMD)子量表得分进行关联,以探索每个因子的潜在神经生理意义。
结果表明,所有患者的影像衍生连接指纹可沿三个潜在因子维度定位;这一结果也通过样本外数据集得到验证。因子 1 仅由 SNRIs 靶向治疗的患者表达,与迟滞(r = 0.4,p = 0.037)相关,其特征是默认模式网络和认知控制网络之间的耦合模式。因子 3 仅由 SSRIs 靶向治疗的患者表达,与认知障碍(r = 0.36,p = 0.047)相关,其特征是认知控制和注意网络内的耦合模式,以及威胁和奖励网络之间的连接。共享因子 2 ,其特征是默认模式网络内的耦合模式,与焦虑(r = 0.54,p = 0.005)和睡眠障碍(r = 0.37,p = 0.032)相关。
我们的研究结果表明,对 MDD 患者的同质性和异质性进行定量分析,可能有助于合理设计药物治疗。
引导抗抑郁药物治疗的抑郁患者的同质性和异质性表现是由大脑潜在结构连接的同质性和异质性引起的。
在不同的抗抑郁药物靶向治疗的患者中都发现了共享和独特的因子负荷。
因子负荷与 HAMD 子量表得分之间存在显著相关性。