Towards a Network Control Theory of Electroconvulsive Therapy Response

• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: 14-12-2021
• Subjects: Computer Science - Systems and Control; Quantitative Biology - Neurons and Cognition
• DOI: 10.48550/arxiv.2112.07408

Electroconvulsive Therapy (ECT) is arguably the most effective intervention
for treatment-resistant depression. While large interindividual variability
exists, a theory capable of predicting 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.