Kyuragi Yusuke, Oishi Naoya, Yamasaki Shimpei, Hazama Masaaki, Miyata Jun, Shibata Mami, Fujiwara Hironobu, Fushimi Yasutaka, Murai Toshiya, Suwa Taro
Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto 606-8397, Japan.
J Affect Disord. 2023 May 1;328:141-152. doi: 10.1016/j.jad.2023.02.060. Epub 2023 Feb 18.
Electroconvulsive therapy is effectively used for treatment-resistant depression; however, its neural mechanism is largely unknown. Resting-state functional magnetic resonance imaging is promising for monitoring outcomes of electroconvulsive therapy for depression. This study aimed to explore the imaging correlates of the electroconvulsive therapy effects on depression using Granger causality analysis and dynamic functional connectivity analyses.
We performed advanced analyses of resting-state functional magnetic resonance imaging data at the beginning and intermediate stages and end of the therapeutic course to identify neural markers that reflect or predict the therapeutic effects of electroconvulsive therapy on depression.
We demonstrated that information flow between the functional networks analyzed by Granger causality changes during electroconvulsive therapy, and this change was correlated with the therapeutic outcome. Information flow and the dwell time (an index reflecting the temporal stability of functional connectivity) before electroconvulsive therapy are correlated with depressive symptoms during and after treatment.
First, the sample size was small. A larger group is needed to confirm our findings. Second, the influence of concomitant pharmacotherapy on our results was not fully addressed, although we expected it to be minimal because only minor changes in pharmacotherapy occurred during electroconvulsive therapy. Third, different scanners were used the groups, although the acquisition parameters were the same; a direct comparison between patient and healthy participant data was not possible. Thus, we presented the data of the healthy participants separately from that of the patients as a reference.
These results show the specific properties of functional brain connectivity.
电休克治疗有效地用于难治性抑郁症；然而，其神经机制在很大程度上尚不清楚。静息态功能磁共振成像有望用于监测抑郁症电休克治疗的效果。本研究旨在使用格兰杰因果分析和动态功能连接分析来探索电休克治疗对抑郁症影响的成像相关因素。
我们在治疗过程的开始、中期和结束时对静息态功能磁共振成像数据进行了高级分析，以识别反映或预测电休克治疗对抑郁症治疗效果的神经标志物。
我们证明，通过格兰杰因果分析所分析的功能网络之间的信息流在电休克治疗期间发生变化，并且这种变化与治疗结果相关。电休克治疗前的信息流和停留时间（反映功能连接时间稳定性的指标）与治疗期间及治疗后的抑郁症状相关。
第一，样本量小。需要更大的样本量来证实我们的发现。第二，尽管我们预计联合药物治疗对结果的影响最小，因为在电休克治疗期间药物治疗仅发生了微小变化，但联合药物治疗对我们结果的影响并未得到充分解决。第三，两组使用了不同的扫描仪，尽管采集参数相同；无法对患者和健康参与者的数据进行直接比较。因此，我们将健康参与者的数据与患者的数据分开呈现作为参考。
这些结果显示了大脑功能连接的特定特性。
