Does cognitive-behavioral therapy affect goal-directed planning in obsessive-compulsive disorder?

Compulsive avoidance in youths and adults with OCD: an aversive pavlovian-to-instrumental transfer study

Compulsive behaviour may often be triggered by Pavlovian cues. Assessing how Pavlovian cues drive instrumental behaviour in obsessive-compulsive disorder (OCD) is therefore crucial to understand how compulsions develop and are maintained. An aversive Pavlovian-to-Instrumental transfer (PIT) paradigm, particularly one involving avoidance/cancellation of negative outcomes, can enable such investigation and has not previously been studied in clinical-OCD. Forty-one participants diagnosed with OCD (21 adults; 20 youths) and 44 controls (21 adults; 23 youths) completed an aversive PIT task. Participants had to prevent the delivery of unpleasant noises by moving a joystick in the correct direction. They could infer these correct responses by learning appropriate response-outcome (instrumental) and stimulus-outcome (Pavlovian) associations. We then assessed whether Pavlovian cues elicited specific instrumental avoidance responses (specific PIT) and induced general instrumental avoidance (general PIT). We investigated whether task learning and confidence indices influenced PIT strength differentially between groups. There was no overall group difference in PIT performance, although youths with OCD showed weaker specific PIT than youth controls. However, urge to avoid unpleasant noises and preference for safe over unsafe stimuli influenced specific and general PIT respectively in OCD, while PIT in controls was more influenced by confidence in instrumental and Pavlovian learning. Thus, in OCD, implicit motivational factors, but not learnt knowledge, may contribute to the successful integration of aversive Pavlovian and instrumental cues. This implies that compulsive avoidance may be driven by these automatic processes. Youths with OCD show deficits in specific PIT, suggesting cue integration impairments are only apparent in adolescence. These findings may be clinically relevant as they emphasise the importance of targeting such implicit motivational processes when treating OCD.

Resting-state functional connectivity of goal-directed and habitual-learning systems: The efficacy of cognitive-behavioral therapy for obsessive-compulsive disorder

BACKGROUND

There is an imbalance between goal-directed and habitual-learning system in patients with obsessive-compulsive disorder (OCD). At present, the relationship between cognitive behavior therapy (CBT) as a first-line therapy and goal-directed and habitual-learning disorder is still unclear. We attempted to discuss the effect of CBT treatment in patients with OCD, using abnormalities in goal-directed and habitual-learning-related brain regions at baseline as predictive factors.

METHODS

A total of 71 subjects, including 35 OCD patients and 36 healthy controls, were recruited. The OCD patients underwent 8 weeks of CBT. These patients were divided into two groups based on treatment response (Nresponders = 18, Nnonresponders = 17). Further subgroup analysis was conducted based on disease duration (Nshort = 17, Nlong = 18) and age of onset (Nearly = 14, Nlate = 21). We collected resting-state ROI-ROI functional connectivity data and apply repeated-measures linear mixed-effects models to investigate the differences of different subgroups.

RESULTS

CBT led to symptom improvement in OCD patients, with varying degrees of effectiveness across subgroups. The orbitofrontal cortex (OFC) and insula, key regions for goal-directed behavior and habitual-learning, respectively, showed significant impacts on CBT efficacy in subgroups with different disease durations and ages of onset.

CONCLUSION

The findings suggest that the goal-directed system may influence the efficacy of CBT through goal selection, maintenance, and emotion regulation. Furthermore, we found that disease duration and age of onset may affect treatment outcomes by modulating functional connectivity between goal-directed and habitual-learning brain regions.

Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.

An Integrative Model for Understanding Obsessive-Compulsive Disorder: Merging Cognitive Behavioral Theory with Insights from Clinical Neuroscience

Several models have been proposed for the emergence and maintenance of obsessive-compulsive disorder (OCD). Although these models have provided important insights and inspired treatment development, no single model has yet sufficiently accounted for the complexed phenotype of the disorder. In the current paper, we propose a novel model that integrates elements from cognitive behavioral models of OCD with neurocognitive approaches to the disorder. This Reciprocal Interaction Model (RIM) for OCD is based on two assumptions: (a) similar observed symptoms can stem from different etiological processes; and (b) neuropsychological deficits (such as reduced response inhibition and overreliance on the habit formation system) and cognitive behavioral processes (such as temporary reduction in anxiety after engaging in compulsive behaviors) mutually affect each other such that abnormalities in one system influence the second system and vice-versa-creating a vicious cycle of pathological processes. Indeed, the bidirectional inhibitory connection between anxiety/obsessions and executive control is at the heart of the model. We begin by briefly reviewing the current models for OCD. We then move on to describe the RIM, the supporting evidence for the model, the model's predictions, and potential clinical implications.

Explicit knowledge of task structure is a primary determinant of human model-based action

Explicit information obtained through instruction profoundly shapes human choice behaviour. However, this has been studied in computationally simple tasks, and it is unknown how model-based and model-free systems, respectively generating goal-directed and habitual actions, are affected by the absence or presence of instructions. We assessed behaviour in a variant of a computationally more complex decision-making task, before and after providing information about task structure, both in healthy volunteers and in individuals suffering from obsessive-compulsive or other disorders. Initial behaviour was model-free, with rewards directly reinforcing preceding actions. Model-based control, employing predictions of states resulting from each action, emerged with experience in a minority of participants, and less in those with obsessive-compulsive disorder. Providing task structure information strongly increased model-based control, similarly across all groups. Thus, in humans, explicit task structural knowledge is a primary determinant of model-based reinforcement learning and is most readily acquired from instruction rather than experience.

Advances in the computational understanding of mental illness

Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.

Deficient Goal-Directed Control in a Population Characterized by Extreme Goal Pursuit

Research in computational psychiatry has sought to understand the basis of compulsive behavior by relating it to basic psychological and neural mechanisms: specifically, goal-directed versus habitual control. These psychological categories have been further identified with formal computational algorithms, model-based and model-free learning, which helps to provide quantitative tools to distinguish them. Computational psychiatry may be particularly useful for examining phenomena in individuals with anorexia nervosa (AN), whose self-starvation appears both excessively goal directed and habitual. However, these laboratory-based studies have not aimed to examine complex behavior, as seen outside the laboratory, in contexts that extend beyond monetary rewards. We therefore assessed (1) whether behavior in AN was characterized by enhanced or diminished model-based behavior, (2) the domain specificity of any abnormalities by comparing learning in a food-specific (i.e., illness-relevant) context as well as in a monetary context, and (3) whether impairments were secondary to starvation by comparing learning before and after initial treatment. Across all conditions, individuals with AN, relative to healthy controls, showed an impairment in model-based, but not model-free, learning, suggesting a general and persistent contribution of habitual over goal-directed control, across domains and time points. Thus, eating behavior in individuals with AN that appears very goal-directed may be under more habitual than goal-directed control, and this is not remediated by achieving weight restoration.

The effects of selective serotonin reuptake inhibitors on brain functional networks during goal-directed planning in obsessive-compulsive disorder

Whether brain network connectivity during goal-directed planning in patients with obsessive–compulsive disorder (OCD) is abnormal and restored by treatment with selective serotonin reuptake inhibitors (SSRIs) remains unknown. This study investigated whether the disrupted network connectivity during the Tower of London (ToL) planning task in medication-free OCD patients could be restored by SSRI treatment. Seventeen medication-free OCD patients and 21 matched healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) while performing the ToL task at baseline and again after 16 weeks of SSRI treatment. Internetwork connectivity was compared across the groups and treatment statuses (pretreatment versus posttreatment). At baseline, compared with the HCs, the OCD patients showed lower internetwork connectivity between the dorsal attention network and the default-mode network during the ToL planning task. After 16 weeks of SSRI treatment, the OCD patients showed improved clinical symptoms accompanied by normalized network connectivity, although their improved behavioral performance in the ToL task did not reach that of the HCs. Our findings support the conceptualization of OCD as a network disease characterized by an imbalance between brain networks during goal-directed planning and suggest that internetwork connectivity may serve as an early biomarker of the effects of SSRIs on goal-directed planning.

Improving the Reliability of Computational Analyses: Model-Based Planning and Its Relationship With Compulsivity

BACKGROUND

Computational models show great promise in mapping latent decision-making processes onto dissociable neural substrates and clinical phenotypes. One prominent example in reinforcement learning is model-based planning, which specifically relates to transdiagnostic compulsivity. However, the reliability of computational model-derived measures such as model-based planning is unclear. Establishing reliability is necessary to ensure that such models measure stable, traitlike processes, as assumed in computational psychiatry. Although analysis approaches affect validity of reinforcement learning models and reliability of other task-based measures, their effect on reliability of reinforcement learning models of empirical data has not been systematically studied.

METHODS

We first assessed within- and across-session reliability and effects of analysis approaches (model estimation, parameterization, and data cleaning) of measures of model-based planning in patients with compulsive disorders (n = 38). The analysis approaches affecting test-retest reliability were tested in 3 large generalization samples (healthy participants: n = 541 and 111; people with a range of compulsivity: n = 1413).

RESULTS

Analysis approaches greatly influenced reliability: reliability of model-based planning measures ranged from 0 (no concordance) to above 0.9 (acceptable for clinical applications). The largest influence on reliability was whether model-estimation approaches were robust and accounted for the hierarchical structure of estimated parameters. Improvements in reliability generalized to other datasets and greatly reduced the sample size needed to find a relationship between model-based planning and compulsivity in an independent dataset.

CONCLUSIONS

These results indicate that computational psychiatry measures such as model-based planning can reliably measure latent decision-making processes, but when doing so must assess the ability of methods to estimate complex models from limited data.