Network Control Theory in Personalized Intervention Selection: A Proof-of-Concept Approach to Simulating Therapeutic Intervention Effects in Clinical Time-Series Data

Network Control Theory (NCT) is concerned with the control of dynamic complex systems, allowing the modeling and assessing of (theoretical) intervention effects. This study presents a proof-of-concept approach for applying NCT in a process-oriented manner to ecological momentary assessment (EMA) data from patients. We applied a linear dynamic system model (LDS) using ridge regression to pretreatment time-series data of 20 patients with depression or anxiety (Δdata points/person = 96). Effects of 31 therapeutic interventions were simulated based on nomothetic knowledge and expert group consensus. Two NCT metrics were applied: average controllability (AC), indexing an intervention's theoretical effect over the system, and cumulative impulse response (CIR), indexing theoretical magnitude and direction of change per variable. Certain nodes exhibited elevated controllability. In exemplar cases, NCT-derived recommendations seemed to differ from the interventions actually received. Theoretical controllability varied across interventions, with some showing consistently higher controllability across patients. This proof-of-concept approach demonstrates how NCT can, in principle, be applied via process-oriented intervention simulations. However, its current application rests on idealized assumptions. Whether identified interventions are clinically meaningful needs empirical testing. We highlight opportunities for NCT research and discuss critical next steps toward empirically refining estimations of intervention effects.