Identification of first-episode unmedicated major depressive disorder using pretreatment features of dominant coactivation patterns
Identification of first-episode unmedicated major depressive disorder using pretreatment features of dominant coactivation patterns
复制标题
使用显性共激活模式的治疗前特征识别首发未经药物治疗的重度抑郁症
DOI：
10.1016/j.pnpbp.2020.110038
复制
发表时间：
2021-01-10
影响因子：
5.6
通讯作者：
Yuan, Yonggui
中科院分区：
文献类型：
--
作者：
Hou, Zhenghua;Kong, Youyong;Yuan, Yonggui
识别神经影像学特征诊断重度抑郁症（MDD）和预测治疗反应仍然具有挑战性。采用预处理显性共激活模式（dCAP）分析方法，我们旨在识别重度抑郁症患者并预测抗抑郁药物的疗效。研究招募了77名首发未服药的重度抑郁症患者和42名年龄和性别匹配的健康对照（hc）。对奖励和默认模式网络（DMN）进行dCAP分析，以从hc中识别重度抑郁症患者。MDD组左后DMN和双侧前DMN dCAP1均显著高于HC组（P < 0.001），且左后DMN dCAP1与基线抑郁严重程度呈正相关（rho = 0.248, P = 0.030）。MDD组右侧奖励网络的dCAP1显著高于HC组。进一步的相关分析表明，右侧奖励网络的转移概率与处理反应性呈正相关（r = 0.247, P = 0.030）。重要的是，综合上述四个子网络的dcap可以有效识别MDD患者（AUC = 0.920, P < 0.001）。dCAP在DMN和奖励网络的子网络中具有明显的预处理特征，可作为个体早期诊断和预测抗抑郁反应的潜在指标。
Identifying neuroimaging features to diagnose major depressive disorder (MDD) and predict treatment response remains challenging. Using the pretreatment dominant coactivation pattern (dCAP) analysis approach, we aimed to identify patients with MDD and predict antidepressant efficacy.Seventy-seven first-episode unmedicated MDD patients and forty-two age- and sex-matched healthy controls (HCs) were recruited in the study.The dCAP analysis was performed for the reward and default mode network (DMN) to identify the MDD patients from the HCs. The dCAP1 of the left posterior DMN and bilateral anterior DMN were significantly higher in the MDD group than in the HC group (P < .001), and the dCAP1 in the left posterior DMN was positively correlated with the baseline severity of depression (rho = 0.248, P = .030). Besides, the MDD group exhibited significantly higher dCAP1 in the right reward network than the HC group. Further correlation analyses revealed that the transfer probability in the right reward network was positively correlated with the treatment responsivity (r = 0.247, P = .030). Importantly, integrating the dCAPs of the above four subnetworks can effectively identify the patients with MDD (AUC = 0.920, P < .001).The distinct pretreatment features of the dCAP in the subnetwork of the DMN and reward network may serve as potential indicators for individual diagnosis and prediction of antidepressant response in the early stage.