Department of Psychology, University of Haifa, Mount Carmel, Haifa, Israel.
Department of Psychiatry, Columbia University, New York, New York; New York State Psychiatric Institute, Columbia University Medical Center, New York, New York.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2020 Jul;5(7):688-696. doi: 10.1016/j.bpsc.2020.03.010. Epub 2020 Apr 11.
Comorbidity between posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) has been commonly overlooked by studies examining resting-state functional connectivity patterns in PTSD. The current study used a data-driven approach to identify resting-state functional connectivity biomarkers to 1) differentiate individuals with PTSD (with or without MDD) from trauma-exposed healthy control subjects (TEHCs), 2) compare individuals with PTSD alone with those with comorbid PTSD+MDD, and 3) explore the clinical utility of the identified biomarkers by testing their associations with clinical symptoms and treatment response.
Resting-state magnetic resonance images were obtained from 51 individuals with PTSD alone, 52 individuals with PTSD+MDD, and 76 TEHCs. Of the 103 individuals with PTSD, 55 were enrolled in prolonged exposure treatment. A support vector machine model was used to identify resting-state functional connectivity biomarkers differentiating individuals with PTSD (with or without MDD) from TEHCs and differentiating individuals with PTSD alone from those with PTSD+MDD. The associations between the identified features and symptomatology were tested with Pearson correlations.
The support vector machine model achieved 70.6% accuracy in discriminating between individuals with PTSD and TEHCs and achieved 76.7% accuracy in discriminating between individuals with PTSD alone and those with PTSD+MDD for out-of-sample prediction. Within-network connectivity in the executive control network, prefrontal network, and salience network discriminated individuals with PTSD from TEHCs. The basal ganglia network played an important role in differentiating individuals with PTSD alone from those with PTSD+MDD. PTSD scores were inversely correlated with within-executive control network connectivity (p < .001), and executive control network connectivity was positively correlated with treatment response (p < .001).
Results suggest that unique brain-based abnormalities differentiate individuals with PTSD from TEHCs, differentiate individuals with PTSD from those with PTSD+MDD, and demonstrate clinical utility in predicting levels of symptomatology and treatment response.
在研究 PTSD 的静息态功能连接模式时,许多研究都忽略了 PTSD 与重度抑郁障碍(MDD)之间的共病现象。本研究采用数据驱动的方法来确定静息态功能连接的生物标志物,以:1)区分 PTSD(伴或不伴 MDD)患者与创伤后健康对照者(TEHC),2)比较单纯 PTSD 患者与共病 PTSD+MDD 患者,以及 3)通过测试这些生物标志物与临床症状和治疗反应的相关性,探索其临床应用价值。
从 51 名单纯 PTSD 患者、52 名 PTSD+MDD 患者和 76 名 TEHC 中获取静息态磁共振图像。在 103 名 PTSD 患者中,有 55 名患者入组接受延长暴露治疗。采用支持向量机模型来识别区分 PTSD(伴或不伴 MDD)患者与 TEHC 以及单纯 PTSD 患者与共病 PTSD+MDD 患者的静息态功能连接生物标志物。采用 Pearson 相关分析来检验所识别特征与症状学之间的相关性。
支持向量机模型在区分 PTSD 患者与 TEHC 以及单纯 PTSD 患者与共病 PTSD+MDD 患者方面的准确率分别为 70.6%和 76.7%。在执行控制网络、前额叶网络和突显网络中,网络内连接可区分 PTSD 患者与 TEHC 患者。基底节网络在区分单纯 PTSD 患者与共病 PTSD+MDD 患者方面发挥着重要作用。PTSD 评分与执行控制网络内连接呈负相关(p<0.001),而执行控制网络内连接与治疗反应呈正相关(p<0.001)。
研究结果表明,独特的基于大脑的异常可区分 PTSD 患者与 TEHC 患者,区分单纯 PTSD 患者与共病 PTSD+MDD 患者,并在预测症状学和治疗反应水平方面具有临床应用价值。