The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, 610066, China.
J Psychiatr Res. 2023 Nov;167:23-31. doi: 10.1016/j.jpsychires.2023.10.004. Epub 2023 Oct 6.
Comorbidity has been frequently observed between generalized anxiety disorder (GAD) and major depressive disorder (MDD), however, common and distinguishable alterations in the topological organization of functional brain networks remain poorly understood. We sought to determine a robust and sensitive functional connectivity marker for diagnostic classification and symptom severity prediction. Multi-layered dynamic functional connectivity including whole brain, network-node and node-node layers via graph theory and gradient analyses were applied to functional MRI resting-state data obtained from 31 unmedicated GAD and 34 unmedicated MDD patients as well as 33 age and education matched healthy controls (HC). GAD and MDD symptoms were assessed using Penn State Worry Questionnaire and Beck Depression Inventory II, respectively. Three network measures including global properties (i.e., global efficiency, characteristic path length), regional nodal property (i.e., degree) and connectivity gradients were computed. Results showed that both patient groups exhibited abnormal dynamic cortico-subcortical topological organization compared to healthy controls, with MDD > GAD > HC in degree of randomization. Furthermore, our multi-layered dynamic functional connectivity network model reached 77% diagnostic accuracy between GAD and MDD and was highly predictive of symptom severity, respectively. Gradients of functional connectivity for superior frontal cortex-subcortical regions, middle temporal gyrus-subcortical regions and amygdala-cortical regions contributed more in this model compared to other gradients. We found shared and distinct cortico-subcortical connectivity features in dynamic functional brain networks between GAD and MDD, which together can promote the understanding of common and disorder-specific topological organization dysregulations and facilitate early neuroimaging-based diagnosis.
广泛性焦虑障碍（GAD）和重度抑郁症（MDD）经常同时存在共病现象，然而，功能大脑网络的拓扑结构的常见和可区分改变仍知之甚少。我们旨在确定一种稳健且敏感的功能连接标记物，用于诊断分类和症状严重程度预测。通过图论和梯度分析，对 31 名未经药物治疗的 GAD 和 34 名未经药物治疗的 MDD 患者以及 33 名年龄和教育程度匹配的健康对照者（HC）的功能磁共振静息态数据进行了多层次动态功能连接研究。使用宾夕法尼亚州担忧问卷和贝克抑郁量表第二版评估 GAD 和 MDD 症状。计算了三种网络测量值，包括全局属性（即全局效率、特征路径长度）、区域节点属性（即度数）和连接梯度。结果表明，与健康对照组相比，两组患者均表现出异常的皮质下动态拓扑结构组织，而 MDD 的随机化程度> GAD > HC。此外，我们的多层次动态功能连接网络模型在 GAD 和 MDD 之间达到了 77%的诊断准确率，并且对症状严重程度具有高度预测性。与其他梯度相比，额上回皮质下区域、颞中回皮质下区域和杏仁核皮质区域的功能连接梯度对该模型的贡献更大。我们发现 GAD 和 MDD 之间的动态功能大脑网络中存在共享和独特的皮质下连接特征，它们共同促进了对常见和特定于疾病的拓扑结构失调的理解，并有助于基于早期神经影像学的诊断。
