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Network Control Theory in Personalized Intervention Selection: A Proof-of-Concept Approach to Simulating Therapeutic
Lucie Pahlen1, Janik Fechtelpeter2,3, Bettina Hufschmidt1
1Department of Clinical Psychology and Psychotherapy, Goethe University Frankfurt, Frankfurt am Main, Germany.
Network Control Theory (NCT) can model intervention effects in patient data. This study shows NCT can simulate therapeutic effects, but clinical relevance requires further testing.
Area of Science:
- Complex Systems Science
- Psychiatry
- Computational Neuroscience
Background:
- Network Control Theory (NCT) models dynamic complex systems and intervention effects.
- Ecological Momentary Assessment (EMA) collects real-time patient data.
- Applying NCT to EMA data offers a novel approach to understanding therapeutic interventions.
Purpose of the Study:
- To present a proof-of-concept for applying NCT to EMA data in a process-oriented manner.
- To simulate the effects of therapeutic interventions using NCT metrics.
- To explore the theoretical controllability of different interventions.
Main Methods:
- A linear dynamic system model (LDS) with ridge regression was applied to EMA data from 20 patients with depression or anxiety.
- Thirty-one therapeutic interventions were simulated based on expert consensus.
- Network Control Theory metrics, average controllability (AC) and cumulative impulse response (CIR), were calculated.
Main Results:
- Specific system nodes showed elevated controllability.
- NCT-derived intervention recommendations sometimes differed from actual patient interventions.
- Intervention controllability varied, with some interventions consistently showing higher effects across patients.
Conclusions:
- NCT can be applied to simulate process-oriented interventions in patient data.
- Current NCT application relies on idealized assumptions.
- Clinical meaningfulness of NCT-identified interventions requires empirical validation.
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