个体化 fMRI 连接可定义重度抑郁症中抗抑郁药和安慰剂反应的特征。
Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression.
机构信息
Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA.
出版信息
Mol Psychiatry. 2023 Jun;28(6):2490-2499. doi: 10.1038/s41380-023-01958-8. Epub 2023 Feb 2.
Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.
尽管舍曲林常用于治疗重度抑郁症(MDD)患者,但它与安慰剂相比只有很小的优势。这部分是由于个体的神经生物学异质性。描述个体独特的大脑功能结构可能有助于更好地剖析这种异质性,从而定义治疗预测特征以指导个性化用药。在这项研究中,我们研究了个体大脑功能连接(FC)是否可以定义 MDD 中更可预测的抗抑郁药和安慰剂治疗的特征。本研究使用了 EMBARC 研究的数据。患者(N=296)被随机分配接受抗抑郁药舍曲林或安慰剂双盲治疗 8 周。从预处理静息态功能磁共振成像(rs-fMRI)构建全脑 FC 网络。然后,通过从原始基线 FC 中去除共同成分来个体化 FC,以训练基于回归的连接预测模型。使用个体化 FC 特征,建立的预测模型成功地识别了 22%的舍曲林组和 31%的安慰剂组在预测 HAMD 变化中的可解释方差的特征。与基于原始 FC 的模型相比,个体化 FC 定义的特征通过交叉验证显著提高了预测性能。对于舍曲林治疗,预测性 FC 指标主要位于左中颞叶和右岛叶。对于安慰剂,预测性 FC 指标主要位于双侧扣带回和左颞上叶。我们的研究结果表明,通过去除共同 FC 成分,与原始 FC 相比,FC 指标的个体化增强了预测性能。与以前的 MDD 临床研究相关,我们确定的预测生物标志物为抗抑郁药和安慰剂治疗的神经病理学提供了新的见解。