Under the Hood: Using Computational Psychiatry to Make Psychological Therapies More Mechanism-Focused

Abstract

Psychological therapies, such as CBT, are an important part of the treatment of a range of psychiatric disorders such as depression and anxiety. There is a growing desire to understand the mechanisms by which such therapies effect change so as to improve treatment outcomes. Here we argue that adopting a computational framework may be one such approach. Computational psychiatry aims to provide a theoretical framework for moving between higher-level psychological states (like emotions, decisions and beliefs) to neural circuits, by modeling these constructs mathematically. These models are explicit hypotheses that contain quantifiable variables and parameters derived from each individual's behavior. This approach has two advantages. Firstly, some of the variables described by these models appears to reflect the neural activity of specific brain regions. Secondly, the parameters estimated by these models may offer a unique description of a patient's symptoms which can be used to both tailor therapy and track its effect. In doing so this approach may offer some additional granularity in understanding how psychological therapies, such as CBT, are working. Although this field shows significant promise, we also highlight several of the key hurdles that must first be overcome before clinical translation of computational insights can be realized.

Keywords: CBT (cognitive-behavioral therapy); computational neuroscience; computational psychiatry; decision-making; mechanisms; psychological therapies; reinforcement learning (RL); reward (healthcare).

Figure 1

An example of a computational problem—learning which actions are the most and least rewarding. In typical tasks, participants choose between stimuli which differ in terms of reward probabilities. Through trial and error participants learn to choose and to avoid the best and worst options, respectively. This type of behavior is well-captured by simple computational models like the one described and variables from these models, such as the prediction error, correlated with brain activity. For therapy, failure to learn from rewarding or punishing experiences may limit the capacity to change behavior.

Figure 2

An example of a patient journey through psychological therapy that incorporates computational assessments. Alongside psychological formulation, task-based assessments output individual parameters for a range of computational processes–such as reward learning or effort discounting. From this analysis, a tailored computational profile could be used to tailor components of therapy (for example attention and learning during behavioral activation versus cognitive restructuring of beliefs around effort). These computational parameters provide objective markers for evaluating change and, by virtue of being closer to the underlying mechanisms that generate or maintain symptoms, may provide an earlier readout of change that precedes symptom improvement. Tasks exist in adapted forms for use online or on smart devices that may also improve patient access and engagement.