Liang Qunjun, Zhou Zhifeng, Chen Shengli, Lin Shiwei, Lin Xiaoshan, Li Ying, Zhang Yingli, Peng Bo, Hou Gangqiang, Qiu Yingwei
Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, 518060, People's Republic of China.
Department of Radiology, Shenzhen Nanshan People's Hospital, Taoyuan AVE 89, Nanshan district, Shenzhen, 518000, People's Republic of China.
Transl Psychiatry. 2025 Jan 28;15(1):33. doi: 10.1038/s41398-025-03238-1.
At least 227 combinations of symptoms meet the criteria for Major Depressive Disorder (MDD). However, in clinical practice, patients consistently present symptoms in a regular rather than random manner, and the neural basis underlying the MDD subtypes remains unclear. To help clarify the neural basis, patients with MDD were clustered by symptom combinations to investigate the neural underpinning of each subtype using functional resonance imaging (fMRI). Four symptom-based subtypes of MDD were identified using latent profile analysis according to the clinical scales. Subsequently, brain dynamics were evaluated using fMRI, and the dysregulations in attention and limbic network were observed among the subtypes. Correlation between brain dynamics and symptom combinations was then assessed via canonical correlation analysis (CCA). The brain-symptom correlation was higher when evaluated in subtypes (r = 0.77 to 0.92) compared to the entire group (r = 0.5). The loading weight in CCA showed that dynamics in transmodal networks contributed the most to the correlation in the subtypes characterized by typical depression symptoms, whereas unimodal networks contributed the most to subtypes characterized by anxiety and insomnia. Finally, gene expression underlying the CCA model, along with its biological encoding process, performed using a postmortem gene expression atlas revealed distinct gene enrichments for different subtypes. These findings highlight that distinct symptom clusters in MDD have specific neural correlates, providing insights into depression's heterogeneous diagnosis and precision medicine opportunities.
至少227种症状组合符合重度抑郁症（MDD）的标准。然而，在临床实践中，患者的症状呈现往往具有规律性而非随机性，且MDD各亚型背后的神经基础仍不清楚。为了帮助阐明神经基础，研究人员根据症状组合对MDD患者进行聚类，运用功能磁共振成像（fMRI）探究各亚型的神经支撑机制。依据临床量表，通过潜在类别分析确定了四种基于症状的MDD亚型。随后，利用fMRI评估脑动力学，观察到各亚型间存在注意力和边缘系统网络的调节异常。接着，通过典型相关分析（CCA）评估脑动力学与症状组合之间的相关性。与整个组相比（r = 0.5），在亚型中评估时脑 - 症状相关性更高（r = 0.77至0.92）。CCA中的负荷权重显示，跨模态网络的动力学对以典型抑郁症状为特征的亚型相关性贡献最大，而单模态网络对以焦虑和失眠为特征的亚型贡献最大。最后，利用死后基因表达图谱对CCA模型的潜在基因表达及其生物学编码过程进行分析，发现不同亚型有明显的基因富集现象。这些发现突出表明，MDD中不同的症状群具有特定的神经关联，为抑郁症的异质性诊断和精准医疗提供了思路。
