Network destabilization and transition in depression: New methods for studying the dynamics of therapeutic change

Abstract

The science of dynamic systems is the study of pattern formation and system change. Dynamic systems theory can provide a useful framework for understanding the chronicity of depression and its treatment. We propose a working model of therapeutic change with potential to organize findings from psychopathology and treatment research, suggest new ways to study change, facilitate comparisons across studies, and stimulate treatment innovation. We describe a treatment for depression that we developed to apply principles from dynamic systems theory and then present a program of research to examine the utility of this application. Recent methodological and technological developments are also discussed to further advance the search for mechanisms of therapeutic change.

Keywords: Chronic depression; Cognitive change in depression; Depression; Dynamic systems theory; Emotional processing.

Figure 1. Network Destabilization and Transition (NDT) Model

Figure 2. Positive and depressive network activation in a Gridware state space grid

This example depicts network activation scores for a participant during one of three phases of therapy. Depressive and positive network activation scores are plotted for each of eight sessions in a phase of treatment. A node (cognitive, emotional, behavioral functioning coded using the CHANGE rating system) of the depressive or positive network is considered “activated” if it is coded as occurring at a moderate (rating of 2) to high (3) level. For example, the first circle on the far left of Panel A depicts a session narrative with a score of 2 nodes activated in the depressive network and 0 in the positive network (2,0 is plotted). Behavior follows the direction of the arrows across the sessions in that treatment phase. The behavior of networks can be mapped and quantified across the course of treatment. From these data, network dispersion (above) and extent of negative and positive network region activation (Figure 3) can be calculated. Panel A depicts low dispersion across the grid, whereas Panel B depicts higher dispersion. Dispersion scores can be used to approximate network stability or destabilization.

Figure 3. Depressive and positive regions of activation

Areas of state-space grids were used to determine depressive and positive network activation for participants within each treatment phase. Behavior in the depressive region (solid line) was defined as any session in which the total depressive network activation score was moderate to high (two or more nodes were activated in the narrative from that session). Behavior in the positive region (dashed line) was calculated in the same way, using the positive network activation scores. Extent of network activation in the depressive and positive regions was calculated by dividing the number of sessions a participant spent in the depressive or positive regions of the state-space grid by the total number of sessions during that phase. In this example, 75% of the scores in that treatment phase fell in the depressive region and 50% in the positive region. Such a pattern can characterize a system in transition, as depressive and positive network activation are co-occurring. It is also possible to define nonoverlapping regions, but this co-occurence is of interest in our research.