Liang Wenjia, Hou Lanwei, Wang Wenjun, Wang Bao, Sun Chenxi, Zhang Yuan, Li Zhuoran, Shi Rong, Zhou Wenjuan, Tang Yuchun, Wang Wei, Yang Lejin, Liu Shuwei
Department of Anatomy and Neurobiology, Shandong Key Laboratory of Mental Disorders, Institute for Sectional Anatomy and Digital Human, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Institute of Brain and Brain-Inspired Science, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China.
Mol Psychiatry. 2025 Jul 9. doi: 10.1038/s41380-025-03102-0.
Magnetic resonance imaging (MRI) has been recognized as a valuable tool for achieving 'reification of clinical diagnosis' of major depressive disorder (MDD). However, the reliability and validity of MRI results are often compromised by genetic, environmental, and clinical heterogeneity within test samples. Here, we combined MRI with other clinical findings using multimodal MRI fusion algorithm to construct a data-driven, bottom-up diagnostic approach. The covariation patterns between the multimodal MRI features and differential expression of exosomal microRNA (miRNA) were identified on a subset of 70 MDD patients and 71 healthy controls (HCs) (served as a training set) as classification features, whereas data from the other 45 MDD patients and 43 HCs served as a test set. Furthermore, longitudinal data from 28 MDD patients undergoing antidepressant treatment for six months were utilized to validate the identified biomarkers, and related signaling pathways were initially explored in depression-like mice. Plasma exosome-derived miR-151a-3p levels were found to be significantly lower in MDD patients compared to HCs and correlated with abnormal changes in functional MRI (fMRI) metrics in the anterior cingulate cortex (ACC), visual cortex, and default mode network, etc. Then, these multimodal MRI features associated with miR-151a-3p expression distinguished MDD patients from HCs with high classification accuracy of 92.05% in support vector machine (SVM) model, outperforming the diagnostic rate when only multimodal MRI features with intergroup differences were entered (70.45%). Furthermore, 10 out of 28 MDD patients exhibited a clinically significant response to the treatment (a reduction of over 50% in Hamilton Rating Scale for Depression (HAMD) score). The significant upregulation of plasma exosomal miR-151a-3p levels and changes of fMRI indicators were also observed in these 10 patients after treatment of six months. Animal experiments have shown that reducing the expression of miR-151-3p in ACC induces depression-like behaviors in mice, while elevating hsa-miR-151a-3p expression in ACC alleviates the depression-like behaviors of mice exposed to chronic unpredictable mild stress. Our study proposed an innovative diagnostic model of MDD by combining the plasma exosome-derived miR-151a-3p expression with its associated multimodal MRI patterns, potentially serving as a novel diagnostic tool.
磁共振成像(MRI)已被公认为是实现重度抑郁症(MDD)“临床诊断具体化”的一项重要工具。然而,测试样本中的遗传、环境和临床异质性常常会影响MRI结果的可靠性和有效性。在此,我们使用多模态MRI融合算法将MRI与其他临床发现相结合,构建了一种数据驱动、自下而上的诊断方法。在70例MDD患者和71例健康对照(HCs)(作为训练集)的子集中,确定了多模态MRI特征与外泌体微小RNA(miRNA)差异表达之间的协变模式作为分类特征,而另外45例MDD患者和43例HCs的数据则作为测试集。此外,利用28例接受了六个月抗抑郁治疗的MDD患者的纵向数据来验证所确定的生物标志物,并在抑郁样小鼠中初步探索了相关信号通路。结果发现,与HCs相比,MDD患者血浆外泌体来源的miR-151a-3p水平显著降低,且与前扣带回皮质(ACC)、视觉皮质和默认模式网络等功能磁共振成像(fMRI)指标的异常变化相关。然后,这些与miR-151a-3p表达相关的多模态MRI特征在支持向量机(SVM)模型中以92.05%的高分类准确率区分了MDD患者和HCs,优于仅输入组间差异的多模态MRI特征时的诊断率(70.45%)。此外,28例MDD患者中有10例在治疗后表现出临床上显著的反应(汉密尔顿抑郁量表(HAMD)评分降低超过50%)。在这10例患者接受六个月治疗后,还观察到血浆外泌体miR-151a-3p水平显著上调以及fMRI指标的变化。动物实验表明,降低ACC中miR-151-3p的表达会诱导小鼠出现抑郁样行为,而提高ACC中hsa-miR-151a-3p的表达则可减轻暴露于慢性不可预测轻度应激的小鼠的抑郁样行为。我们的研究通过将血浆外泌体来源的miR-151a-3p表达与其相关的多模态MRI模式相结合,提出了一种创新的MDD诊断模型,有望成为一种新型诊断工具。