Abstract：
Introduction: For decades, predicting response to the antidepressant medication has been a critical unmet need in depression treatment in clinic, and a technical challenge in depression research. Methods: In this study, a recently developed functional brain network controllability (fBNC) analysis approach was employed to identify the antidepressant treatment responders and nonresponders from depression patients at the pretreatment period. The fBNC, which captures the ability of brain regions to guide the brain\'s behavior from an initial state to a desired state with suitable choice of inputs, may provide valuable features for antidepressant response prediction. The performance of prediction was evaluated using resting-state functional magnetic resonance imaging data collected from a 6-week longitudinal clinical trial with escitalopram in treating unmedicated depression patients (n = 20). Treatment outcomes were assessed using the Hamilton Depression Rating Scale (HAMD) scores. Patients were considered as the treatment responders if their post-treatment HAMD scores were decreased by 50% or more at 6 weeks post-treatment. Results: Results showed significantly larger global average controllability and lower global modal controllability, greater regional average controllability, and smaller regional modal controllability of default mode network in treatment responders compared with the treatment nonresponders at the pretreatment period. By performing optimal control analysis, our results showed no significant difference of the neuromodulation effects between the treatment responders and nonresponders. Discussion: Our results suggest that the fBNC measures may be utilized as novel biomarkers to predict antidepressant response on depression and provide theoretical support to employ neuromodulation for treating antidepressant nonresponders. Impact statement In this study, by employing the novel functional brain controllability analysis on top of the brain connectivity network, we identified a set of biomarkers to identify the groups of depressive patients who responded to the antidepressant treatments from those who did not. We further provided the theoretical support to utilize neuromodulation for treating antidepressant nonresponders. These findings have clinical implications as accurate identification of antidepressant treatment response before starting the treatment may reduce patients\' suffering and costs and increase the treatment outcomes by adjusting and personalizing the treatment protocol.
摘要：
简介：几十年来，预测对抗抑郁药物的反应一直是临床治疗抑郁症的关键未满足需求，和抑郁症研究的技术挑战。方法:在本研究中,我们采用最近开发的功能性脑网络可控性(fBNC)分析方法,从治疗前的抑郁症患者中识别抗抑郁治疗的应答者和无应答者.fBNC,它捕获了大脑区域的能力，以引导大脑的行为从初始状态到期望的状态与适当的输入选择，可能为抗抑郁药反应预测提供有价值的特征。使用静息状态功能磁共振成像数据评估预测的性能，这些数据是从艾司西酞普兰治疗未用药抑郁症患者的为期6周的纵向临床试验中收集的（n=20）。使用汉密尔顿抑郁量表（HAMD）评分评估治疗结果。如果患者的治疗后HAMD评分在治疗后6周降低50%或更多,则将其视为治疗应答者。结果：结果显示明显较大的全局平均可控性和较低的全局模态可控性，更大的区域平均可控性，与预处理期的治疗无反应者相比，治疗反应者的默认模式网络的区域模态可控性较小。通过执行最优控制分析，我们的结果显示治疗应答者和无应答者之间的神经调节效应没有显著差异.讨论：我们的结果表明，fBNC措施可用作新的生物标志物来预测抑郁症的抗抑郁反应，并为采用神经调节治疗抗抑郁无反应者提供理论支持。
