Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Abstract

Background: The past decades of research have seen an increase in statistical tools to explore the complex dynamics of mental health from patient data, yet the application of these tools in clinical practice remains uncommon. This is surprising, given that clinical reasoning, e.g., case conceptualizations, largely coincides with the dynamical system approach. We argue that the gap between statistical tools and clinical practice can partly be explained by the fact that current estimation techniques disregard theoretical and practical considerations relevant to psychotherapy. To address this issue, we propose that case conceptualizations should be formalized. We illustrate this approach by introducing a computational model of functional analysis, a framework commonly used by practitioners to formulate case conceptualizations and design patient-tailored treatment.

Methods: We outline the general approach of formalizing idiographic theories, drawing on the example of a functional analysis for a patient suffering from panic disorder. We specified the system using a series of differential equations and simulated different scenarios; first, we simulated data without intervening in the system to examine the effects of avoidant coping on the development of panic symptomatic. Second, we formalized two interventions commonly used in cognitive behavioral therapy (CBT; exposure and cognitive reappraisal) and subsequently simulated their effects on the system.

Results: The first simulation showed that the specified system could recover several aspects of the phenomenon (panic disorder), however, also showed some incongruency with the nature of panic attacks (e.g., rapid decreases were not observed). The second simulation study illustrated differential effects of CBT interventions for this patient. All tested interventions could decrease panic levels in the system.

Conclusions: Formalizing idiographic theories is promising in bridging the gap between complexity science and clinical practice and can help foster more rigorous scientific practices in psychotherapy, through enhancing theory development. More precise case conceptualizations could potentially improve intervention planning and treatment outcomes. We discuss applications in psychotherapy and future directions, amongst others barriers for systematic theory evaluation and extending the framework to incorporate interactions between individual systems, relevant for modeling social learning processes. With this report, we hope to stimulate future efforts in formalizing clinical frameworks.

Keywords: Complex systems; Computational modeling; Dynamical systems; Formalizing theories; Functional analysis; Idiographic approach; Network analysis; Ordinary differential equations; Process-based psychotherapy; Theory development.

Fig. 1

The role of computational modeling in bridging the scientist-practitioner gap. Schematic illustration of computational modeling (the product of formalizing a theory), at the intersection of clinical practice and mental health research. Computational models allow us to evaluate case conceptualizations in clinical practice (a–d), and bring clinical theories closer to empirical studies through guiding choices crucial to the estimation of and inferences drawn from data models (b, e–g)

Fig. 2

Functional analysis of hypothetical patient suffering from panic disorder. Case conceptualization of our example patient using the framework of functional analysis, as commonly documented in clinical practice. A discriminant stimulus leads to cognitive, emotional, and behavioral reactions (Rc, Re, Rb, respectively). The behavioral reaction has perceived benefits and costs, reinforcing or inhibiting the behavior

Fig. 3

Schematic representation of the functional analysis. Theoretical relations adapted from the patient’s functional analysis as a basis for deriving the system equations. Anxiety (Re) is reduced through applying avoidance behavior (Rb). In addition, avoidance behavior is reinforced through perceived benefits and inhibited through perceived costs. Persistent avoidant behavior increases the credibility of catastrophic interpretations, in turn leading to more catastrophizing during exposure. We formalized and tested three interventions, exposure, cognitive reappraisal, and their combination, represented through the red boxes

Fig. 4

Simulation results of scenario 1 (no intervention). The top and middle parts show the simulated time-series for the discriminant stimulus, panic, and avoidant coping along with a catastrophizing and the credibility of the catastrophic interpretation and b perceived benefits and costs. The bottom part of the figure (c) shows the three-dimensional phase portrait for panic, avoidant coping, and the credibility of the catastrophic interpretation, where the white box indicates the start and the black box the end of the trajectory

Fig. 5

Simulation results of scenario 2 (exposure; behavioral therapy). The top and middle part show the simulated time-series for the discriminant stimulus, panic, and avoidant coping along with catastrophizing and the credibility of the catastrophic interpretation (a) and perceived benefits and costs (b). The bottom part of the figure (c) shows the three-dimensional phase portrait for panic, avoidant coping, and the credibility of the catastrophic interpretation, where the white box indicates the start and the black box the end of the trajectory

Fig. 6

Simulation results of scenario 3 (cognitive reappraisal; cognitive therapy). The top and middle part show the simulated time-series for the discriminant stimulus, panic, and avoidant coping along with catastrophizing and the credibility of the catastrophic interpretation (a) and perceived benefits and costs (b). The bottom part of the figure (c) shows the three-dimensional phase portrait for panic, avoidant coping, and the credibility of the catastrophic interpretation, where the white box indicates the start and the black box the end of the trajectory

Fig. 7

Simulation results of scenario 4 (exposure and cognitive reappraisal; CBT). The top and middle parts show the simulated time-series for the discriminant stimulus, panic, and avoidant coping along with catastrophizing and the credibility of the catastrophic interpretation (a) and perceived benefits and costs (b). The bottom part of the figure (c) shows the three-dimensional phase portrait for panic, avoidant coping, and the credibility of the catastrophic interpretation, where the white box indicates the start and the black box the end of the trajectory