Hahn Tim, Jamalabadi Hamidreza, Nozari Erfan, Winter Nils R, Ernsting Jan, Gruber Marius, Mauritz Marco J, Grumbach Pascal, Fisch Lukas, Leenings Ramona, Sarink Kelvin, Blanke Julian, Vennekate Leon Kleine, Emden Daniel, Opel Nils, Grotegerd Dominik, Enneking Verena, Meinert Susanne, Borgers Tiana, Klug Melissa, Leehr Elisabeth J, Dohm Katharina, Heindel Walter, Gross Joachim, Dannlowski Udo, Redlich Ronny, Repple Jonathan
Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.
Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany.
PNAS Nexus. 2023 Feb 1;2(2):pgad032. doi: 10.1093/pnasnexus/pgad032. eCollection 2023 Feb.
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)-an ECT seizure quality index-and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.
电休克疗法（ECT）可以说是治疗难治性抑郁症最有效的干预措施。虽然个体间存在很大差异，但能够解释个体对ECT反应的理论仍然难以捉摸。为了解决这个问题，我们基于网络控制理论（NCT）提出了一个关于ECT反应的定量、机制框架。然后，我们通过实证检验我们的方法，并将其用于预测ECT治疗反应。为此，我们分别推导了发作后抑制指数（PSI）（一种ECT发作质量指数）与全脑模态可控性和平均可控性之间的形式关联，这两个NCT指标分别基于白质脑网络结构。利用已知的ECT反应与PSI之间的关联，我们随后假设我们的可控性指标与由PSI介导的ECT反应之间存在关联。我们在50名接受ECT治疗的抑郁症患者中正式检验了这一猜想。我们表明，基于ECT前结构连接组数据的全脑可控性指标按照我们的假设预测了ECT反应。此外，我们还展示了通过PSI产生的预期中介效应。重要的是，我们基于理论推导的指标至少与基于ECT前连接组数据的广泛机器学习模型相当。总之，我们推导并检验了一个基于个体脑网络结构能够预测ECT反应的控制理论框架。它对个体治疗反应做出了可检验的定量预测，这些预测得到了有力的实证证据的支持。我们的工作可能构成一个基于控制理论的个性化ECT干预综合定量理论的起点。
