Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variability in clinical outcome. We examined changes in electroencephalographic FC during the first rTMS treatment in 109 subjects treated with 10 Hz stimulation to left dorsolateral prefrontal cortex. All subjects subsequently received 30 treatments and clinical response was defined as ≥40% improvement in the inventory of depressive symptomatology-30 SR score at treatment 30. Connectivity change was assessed with coherence, envelope correlation, and a novel measure, alpha spectral correlation (αSC). Machine learning was used to develop predictive models of outcome for each connectivity measure, which were compared with prediction based upon early clinical improvement. Significant connectivity changes were associated with clinical outcome (P < 0.001). Machine learning models based on αSC yielded the most accurate prediction (area under the curve, AUC = 0.83), and performance improved when combined with early clinical improvement measures (AUC = 0.91). The initial rTMS treatment session produced robust changes in FC, which were significant predictors of clinical outcome of a full course of treatment for MDD.

中文翻译：

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功能连接性的变化预测了重度抑郁症的经颅经颅磁刺激重复治疗的结果。

重度抑郁症（MDD）的反复经颅磁刺激（rTMS）治疗与脑功能连通性（FC）的改变有关。这些变化可能与rTMS的作用机制有关，并解释了临床结果的变异性。我们检查了109名受试者的第一个rTMS治疗期间脑电图FC的变化，该受试者接受了10 Hz刺激左背外侧前额叶皮层。所有受试者随后接受了30种治疗，临床反应被定义为治疗30时抑郁症状--30 SR评分清单改善≥40％。连通性变化通过相干性，包膜相关性和新方法α光谱相关性（αSC）进行评估）。机器学习被用来为每种连通性度量开发结果的预测模型，将其与基于早期临床改善的预测进行比较。显着的连通性改变与临床结果相关（P <0.001）。基于αSC的机器学习模型提供了最准确的预测（曲线下面积，AUC = 0.83），并且与早期的临床改进措施结合使用时，性能得到了改善（AUC = 0.91）。最初的rTMS治疗会议产生了FC的强劲变化，这是MDD整个治疗过程的临床结果的重要预测指标。与早期的临床改善措施相结合，其性能得到改善（AUC = 0.91）。最初的rTMS治疗会议产生了FC的强劲变化，这是MDD整个治疗过程的临床结果的重要预测指标。与早期的临床改善措施相结合，其性能得到改善（AUC = 0.91）。最初的rTMS治疗会议产生了FC的强劲变化，这是MDD整个治疗过程的临床结果的重要预测指标。