Details

Autor(en) / Beteiligte

• 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
• Galea, Sandro

Titel

Towards a network control theory of electroconvulsive therapy response

Ist Teil von

• PNAS nexus, 2023-02, Vol.2 (2), p.pgad032-pgad032

Ort / Verlag

England: Oxford University Press

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.