Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes.
Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes.
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DOI：
10.3389/fphar.2023.1102413
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发表时间：
2023
影响因子：
5.6
通讯作者：
中科院分区：
文献类型：
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简介：电惊厥疗法（ECT）仍然是治疗难治性抑郁发作的最有效方法之一，尽管这种治疗可能会导致认知障碍。作为神经可塑性的有效刺激剂，ECT 可能通过形成新的神经连接来重建大脑，从而使异常的抑郁相关大脑功能正常化。多种证据表明，从静态和动态角度来看，功能连接（FC）变化是抗抑郁药疗效和认知变化的可靠指标。然而，之前没有研究直接确定 FC 的不同方面是否以及如何在基于神经影像的临床结果预测方面提供补充信息。 方法：在本研究中，我们对 ECT 数据集实施了全自动独立成分分析框架，受试者（n = 50，年龄 = 65.54 ± 8.92）被随机分配到三种治疗幅度（600、700 或 800 毫安 [mA]）。我们提取了静态功能网络连接（sFNC）和动态 FNC（dFNC）特征，并采用偏最小二乘回归来构建抗抑郁结果和认知变化的预测模型。 结果：我们发现 sFNC 的变化可以稳健地预测抗抑郁效果和记忆力变化（排列检验 p < 5.0 × 10−3）。更有趣的是，通过添加 dFNC 信息，该模型在预测汉密尔顿抑郁量表 24 项变化方面获得了更高的准确度（HDRS24，t = 9.6434，p = 1.5 × 10−21）。临床结果的预测图显示弱负相关，表明 ECT 诱导的抗抑郁结果和认知变化可能与不同的功能性大脑神经可塑性相关。 讨论：总体结果表明，动态 FC 并不是多余的，而是反映了静态 FC 无法捕获的 ECT 机制，特别是对于抗抑郁疗效的预测。跟踪静态和动态 FC 的预测特征将有助于通过个体化 ECT 剂量最大限度地提高抗抑郁效果和认知安全性。
Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain’s reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes. Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes. Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10−3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS24, t = 9.6434, p = 1.5 × 10−21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity. Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.
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