Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder.
Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder.
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DOI：
10.3389/fnhum.2021.689488
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发表时间：
2021
影响因子：
2.9
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Dini H;Sendi MSE;Sui J;Fu Z;Espinoza R;Narr KL;Qi S;Abbott CC;van Rooij SJH;Riva-Posse P;Bruni LE;Mayberg HS;Calhoun VD
背景：电休克治疗（ECT）是治疗重度抑郁症最有效的治疗方法之一。近年来，ECT对静息态功能磁共振成像（rs-fMRI）的影响越来越受到关注。本研究旨在比较抑郁症（depression disorder，DEP）患者和健康人的rs-fMRI，探讨ECT前患者rs-fMRI估计的动态功能网络连接网络（dynamic functional network connectivity network，dFNC）是否与最终ECT结果相关，以及ECT对脑网络状态的影响。方法：静息态功能磁共振成像（fMRI）数据收集自119例抑郁症或抑郁障碍（DEP）患者（76名女性）和61例健康（HC）参与者（34名女性），平均年龄为52.25岁（N = 180）。ECT治疗前后汉密尔顿抑郁量表（HDRS）评分分别为25.59 ± 6.14和11.48 ± 9.07。采用组独立成分分析方法，从ECT前后的rs-fMRI数据中提取默认模式（DMN）和认知控制网络（CCN）的24个独立成分。然后，使用滑动窗口方法来估计每个受试者ECT前后的dFNC。接下来，将k均值聚类分别应用于ECT前dFNC和ECT后dFNC，以评估每个参与者的三种不同状态。我们计算了每个受试者在每个状态下花费的时间，称为“占用率”或OCR。接下来，我们比较了HC和DEP参与者之间的OCR值。我们还计算了ECT前OCR和HDRS变化之间的部分相关性，同时控制年龄，性别和部位。最后，我们通过比较DEP和HC参与者的ECT前和ECT后OCR来评估ECT的有效性。结果如下：主要发现包括：（1）在状态2下，抑郁症（DEP）患者的OCR值显着低于HC组，其中认知控制网络（CCN）和默认模式网络（DMN）之间的连接性相对高于其他状态（校正p = 0.015），（2）状态的ECT前OCR，CCN和DMN分量之间具有更多的负连接性，与HDRS变化相关（R = 0.23，校正p = 0.03）。这意味着那些在这种状态下花费较少时间的DEP患者显示出更多的HDRS变化，并且（3）ECT后OCR分析表明ECT增加了DEP患者在状态2下花费的时间量（校正p = 0.03）。结论：我们的研究结果表明，动态功能网络连接（dFNC）的功能，估计从CCN和DMN，显示承诺作为DEP患者的ECT结果的预测生物标志物。此外，这项研究确定了一个可能的潜在机制与ECT对DEP患者的影响。
Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states. Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants. Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03). Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.
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DOI：
10.1016/j.nicl.2020.102375
发表时间：
2020
期刊：
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影响因子：
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作者：
Du Y;Fu Z;Sui J;Gao S;Xing Y;Lin D;Salman M;Abrol A;Rahaman MA;Chen J;Hong LE;Kochunov P;Osuch EA;Calhoun VD;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
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影响因子：
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通讯作者：
Gunning, Faith M.
影响因子：
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通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
Calhoun, Vince D.