Comparison of the Association Between Goal-Directed Planning and Self-reported Compulsivity vs Obsessive-Compulsive Disorder Diagnosis

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.

A hierarchical reinforcement learning model explains individual differences in attentional set shifting

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks, such as the widely used CANTAB IED, reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED, with one including additional psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. These data showcase a new methodology to analyse data from the CANTAB IED task, as well as suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.

Lower confidence and increased error sensitivity in OCD patients while learning under volatility

A decoupling between confidence and action could relate to compulsive behaviour as seen in obsessive-compulsive disorder (OCD). The link between confidence and action in OCD has been investigated in clinical case-control studies and in the general population with discrepant findings. The generalizability of findings from highly-compulsive general population samples to clinical OCD samples has been questioned. Here, we investigate action-confidence coupling for 38 OCD patients compared to 37 healthy controls (HC), using a predictive inference task. We compared those results to a comparison between matched high and low compulsive individuals from the general population. Action-updating, confidence and their coupling were compared between the groups. Moreover, computational modeling was performed to compare groups on error sensitivity and environmental parameters. OCD patients showed lower confidence and higher learning rates in reaction to (small) prediction errors than HC, signaling hyperactive error signaling and lower confidence estimation. No evidence was found for differences in action-confidence coupling between groups. In contrast high the compulsive group showed higher confidence and stronger decoupling than the low compulsive group, both of which were related to symptoms. The underlying mechanisms of obsessive-compulsive behaviour might differ between clinical and highly-compulsive general population samples, resulting in different (meta)cognitive profiles.

On the effects of impulsivity and compulsivity on neural correlates of model-based performance

Impaired goal-directed behavior is associated with a range of mental disorders, implicating underlying transdiagnostic factors. While compulsivity has been linked to reduced model-based (MB) control, impulsivity has rarely been studied in the context of reinforcement learning despite its links to reward processing and cognitive control. This study investigated the neural mechanisms underlying MB control and the influence of impulsivity and compulsivity, using EEG data from 238 individuals during a two-step decision making task. Single-trial analyses revealed a modulation of the feedback-related negativity (FRN), where amplitudes were higher after common transitions and positive reward prediction error (RPE), indicating a valence effect. Meanwhile, enhanced P3 amplitudes after rare transitions and both positive and negative RPE possibly reflect surprise. In a second step, we regressed the mean b values of the effect of RPE on the EEG signals onto self-reported impulsivity and compulsivity and behavioral MB control (w). The effect of RPE on FRN-related activity was mainly associated with higher w scores, linking the FRN to MB control. Crucially, the modulation of the P3 by RPE was negatively associated with compulsivity, pointing to a deficient mental model in highly compulsive individuals.

Disorders of compulsivity: Deficits in arbitrating learning strategies

While previous research has shown that compulsivity is related to an imbalance between goal-directed and habitual learning systems, very little is known about whether this effect is due to the impairment of a single system or the impairment of the arbitration mechanism that determines which system controls behaviour at any given moment; the current study aims to address this disagreement. Nineteen alcohol use disorder, 30 obsessive-compulsive disorder (OCD) and 20 major depressive disorder patients and corresponding sex- and age-matched controls performed two-choice, three-stage Markov decision-making paradigm. Model-based and mode-free reinforcement learning models were used to independently fitted their behavioural data. Alcohol use disorder and OCD patients showed less model-based strategy choice than healthy controls in task conditions where the model-based strategy was optimal. Only OCD patients showed higher behavioural control system switching in task conditions where model-free use was optimal. Major depressive disorder patients did not differ from the matched control in both. These findings suggest that dysfunction in arbitration control between dual systems may be the basis for diverse disorders involving compulsivity.

Neurocomputational model of compulsivity: deviating from an uncertain goal-directed system

Despite a theory that an imbalance in goal-directed versus habitual systems serve as building blocks of compulsions, research has yet to delineate how this occurs during arbitration between the two systems in obsessive-compulsive disorder. Inspired by a brain model in which the inferior frontal cortex selectively gates the putamen to guide goal-directed or habitual actions, this study aimed to examine whether disruptions in the arbitration process via the fronto-striatal circuit would underlie imbalanced decision-making and compulsions in patients. Thirty patients with obsessive-compulsive disorder [mean (standard deviation) age = 26.93 (6.23) years, 12 females (40%)] and 30 healthy controls [mean (standard deviation) age = 24.97 (4.72) years, 17 females (57%)] underwent functional MRI scans while performing the two-step Markov decision task, which was designed to dissociate goal-directed behaviour from habitual behaviour. We employed a neurocomputational model to account for an uncertainty-based arbitration process, in which a prefrontal arbitrator (i.e. inferior frontal gyrus) allocates behavioural control to a more reliable strategy by selectively gating the putamen. We analysed group differences in the neural estimates of uncertainty of each strategy. We also compared the psychophysiological interaction effects of system preference (goal-directed versus habitual) on fronto-striatal coupling between groups. We examined the correlation between compulsivity score and the neural activity and connectivity involved in the arbitration process. The computational model captured the subjects' preferences between the strategies. Compared with healthy controls, patients had a stronger preference for the habitual system (t = -2.88, P = 0.006), which was attributed to a more uncertain goal-directed system (t = 2.72, P = 0.009). Before the allocation of controls, patients exhibited hypoactivity in the inferior frontal gyrus compared with healthy controls when this region tracked the inverse of uncertainty (i.e. reliability) of goal-directed behaviour (P = 0.001, family-wise error rate corrected). When reorienting behaviours to reach specific goals, patients exhibited weaker right ipsilateral ventrolateral prefronto-putamen coupling than healthy controls (P = 0.001, family-wise error rate corrected). This hypoconnectivity was correlated with more severe compulsivity (r = -0.57, P = 0.002). Our findings suggest that the attenuated top-down control of the putamen by the prefrontal arbitrator underlies compulsivity in obsessive-compulsive disorder. Enhancing fronto-striatal connectivity may be a potential neurotherapeutic approach for compulsivity and adaptive decision-making.

Beliefs, compulsive behavior and reduced confidence in control

OCD has been conceptualized as a disorder arising from dysfunctional beliefs, such as overestimating threats or pathological doubts. Yet, how these beliefs lead to compulsions and obsessions remains unclear. Here, we develop a computational model to examine the specific beliefs that trigger and sustain compulsive behavior in a simple symptom-provoking scenario. Our results demonstrate that a single belief disturbance-a lack of confidence in the effectiveness of one's preventive (harm-avoiding) actions-can trigger and maintain compulsions and is directly linked to compulsion severity. This distrust can further explain a number of seemingly unrelated phenomena in OCD, including the role of not-just-right feelings, the link to intolerance to uncertainty, perfectionism, and overestimation of threat, and deficits in reversal and state learning. Our simulations shed new light on which underlying beliefs drive compulsive behavior and highlight the important role of perceived ability to exert control for OCD.

Association of temporal discounting with transdiagnostic symptom dimensions

Temporal discounting (TD), the tendency to devalue future rewards as a function of delay until receipt, is aberrant in many mental disorders. Identifying symptom patterns and transdiagnostic dimensions associated with TD could elucidate mechanisms responsible for clinically impaired decision-making and facilitate identifying intervention targets. Here, we tested in a general population sample (N = 731) the extent to which TD was related to different symptom patterns and whether effects of time framing (dates/delay units) and monetary magnitude (large/small) had particularly strong effects in people scoring higher on specific symptom patterns. Analyses revealed that TD was related to symptom patterns loading on anxious-depression and inattention-impulsivity-overactivity dimensions. Moreover, TD was lower in the date than the delay version and with higher magnitudes, especially in people scoring higher on the inattention-impulsivity-overactivity dimension. Overall, this study provides evidence for TD as a transdiagnostic process across affective and impulsivity-related dimensions. Future studies should test framing interventions in clinical populations characterized by impulsivity.Preregistration: This research was preregistered at https://osf.io/fg9sc .

Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits

BACKGROUND

Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD.

METHODS

Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects. Linked independent component analysis (LICA) and correlation analysis were applied to identify associations between local neuroimaging patterns across modalities (including gray matter volume, white matter integrity, and spontaneous functional activity) and clinical factors.

RESULTS

LICA identified eight multimodal neuroimaging patterns related to phenotypic variations, including three related to symptoms and diagnosis. One imaging pattern (IC9) that included both the amplitude of low-frequency fluctuation measure of spontaneous functional activity and white matter integrity measures correlated negatively with OCD diagnosis and diagnostic scales. Two imaging patterns (IC10 and IC27) correlated with compulsion symptoms: IC10 included primarily anatomical measures and IC27 included primarily functional measures. In addition, we identified imaging patterns associated with age, gender, and emotional expression across subjects.

CONCLUSIONS

We established that data fusion techniques can identify local multimodal neuroimaging patterns associated with OCD phenotypes. The results inform our understanding of the neurobiological underpinnings of compulsive behaviors and OCD diagnosis.

Differences in metacognitive functioning between obsessive-compulsive disorder patients and highly compulsive individuals from the general population

BACKGROUND

Our confidence, a form of metacognition, guides our behavior. Confidence abnormalities have been found in obsessive-compulsive disorder (OCD). A first notion based on clinical case-control studies suggests lower confidence in OCD patients compared to healthy controls. Contrarily, studies in highly compulsive individuals from general population samples showed that obsessive-compulsive symptoms related positively or not at all to confidence. A second notion suggests that an impairment in confidence estimation and usage is related to compulsive behavior, which is more often supported by studies in general population samples. These opposite findings call into question whether findings from highly compulsive individuals from the general population are generalizable to OCD patient populations.

METHODS

To test this, we investigated confidence at three hierarchical levels: local confidence in single decisions, global confidence in task performance and higher-order self-beliefs in 40 OCD patients (medication-free, no comorbid diagnoses), 40 controls, and 40 matched highly compulsive individuals from the general population (HComp).

RESULTS

In line with the first notion we found that OCD patients exhibited relative underconfidence at all three hierarchical levels. In contrast, HComp individuals showed local and global overconfidence and worsened metacognitive sensitivity compared with OCD patients, in line with the second notion.

CONCLUSIONS

Metacognitive functioning observed in a general highly compulsive population, often used as an analog for OCD, is distinct from that in a clinical OCD population, suggesting that OC symptoms in these two groups relate differently to (meta)cognitive processes. These findings call for caution in generalizing (meta)cognitive findings from general population to clinical samples.

Using smartphones to optimise and scale-up the assessment of model-based planning

Model-based planning is thought to protect against over-reliance on habits. It is reduced in individuals high in compulsivity, but effect sizes are small and may depend on subtle features of the tasks used to assess it. We developed a diamond-shooting smartphone game that measures model-based planning in an at-home setting, and varied the game's structure within and across participants to assess how it affects measurement reliability and validity with respect to previously established correlates of model-based planning, with a focus on compulsivity. Increasing the number of trials used to estimate model-based planning did remarkably little to affect the association with compulsivity, because the greatest signal was in earlier trials. Associations with compulsivity were higher when transition ratios were less deterministic and depending on the reward drift utilised. These findings suggest that model-based planning can be measured at home via an app, can be estimated in relatively few trials using certain design features, and can be optimised for sensitivity to compulsive symptoms in the general population.

Contingency learning in zebrafish exposed to apomorphine- and levetiracetam

Cognitive rigidity (CR) refers to inadequate executive adaptation in the face of changing circumstances. Increased CR is associated with a number of psychiatric disorders, for example, obsessive-compulsive disorder, and improving cognitive functioning by targeting CR in these conditions, may be fruitful. Levetiracetam (LEV), clinically used to treat epilepsy, may have pro-cognitive effects by restoring balance to neuronal signalling. To explore this possibility, we applied apomorphine (APO) exposure in an attempt to induce rigid cue-directed responses following a cue (visual pattern)-reward (social conspecifics) contingency learning phase and to assess the effects of LEV on such behaviours. Briefly, zebrafish were divided into four different 39-day-long exposure groups ( n  = 9-10) as follows: control (CTRL), APO (100 µg/L), LEV (750 µg/L) and APO + LEV (100 µg/L + 750 µg/L). The main findings of this experiment were that 1) all four exposure groups performed similarly with respect to reward- and cue-directed learning over the first two study phases, 2) compared to the CTRL group, all drug interventions, but notably the APO + LEV combination, lowered the degree of reward-directed behaviour during a dissociated presentation of the cue and reward, and 3) temporal and spatial factors influenced the manner in which zebrafish responded to the presentation of the reward. Future studies are needed to explore the relevance of these findings for our understanding of the potential cognitive effects of LEV.

From Computation to Clinic

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

The Effect of Symptom-Provocation on Inhibitory Control in Obsessive-Compulsive Disorder Patients Is Contingent upon Chronotype and Time of Day

Studies have shown that alertness can affect inhibitory control, the mechanism responsible for stopping behaviors, thoughts, or emotions. Inhibitory control is particularly important for helping individuals with Obsessive-Compulsive Disorder (OCD) resisting their symptoms. Chronotype is the mechanism governing an individual's fluctuation of alertness throughout the day. Previous studies have shown that individuals with a 'morning' chronotype have worse OCD symptoms in the evening and vice versa. We administered a novel 'symptom-provocation stop signal task' (SP-SST), in which individually tailored OCD triggers were presented and inhibitory control was measured. Twenty-five treatment-seeking OCD patients completed the SP-SST three times per day for seven consecutive days. Stop signal reaction time (SSRT), which measures inhibitory control, was calculated separately for symptom-provocation trials and for neutral trials. Results yielded that: (a) stopping was significantly harder in the symptom-provocation compared to neutral trials, and (b) the chronotype by time-of-day interaction predicts inhibition for both symptom-provocation and neutral trials, indicating better inhibition in the optimal time of day. Furthermore, we concluded that individually tailored OCD triggers have a detrimental effect on inhibitory control. Most importantly, higher alertness levels, which can be predicted by the interaction of chronotype and time of day, affect inhibitory control, both in general and for OCD triggers specifically.

Anxiety as a disorder of uncertainty: implications for understanding maladaptive anxiety, anxious avoidance, and exposure therapy

In cognitive-behavioral conceptualizations of anxiety, exaggerated threat expectancies underlie maladaptive anxiety. This view has led to successful treatments, notably exposure therapy, but is not consistent with the empirical literature on learning and choice alterations in anxiety. Empirically, anxiety is better described as a disorder of uncertainty learning. How disruptions in uncertainty lead to impairing avoidance and are treated with exposure-based methods, however, is unclear. Here, we integrate concepts from neurocomputational learning models with clinical literature on exposure therapy to propose a new framework for understanding maladaptive uncertainty functioning in anxiety. Specifically, we propose that anxiety disorders are fundamentally disorders of uncertainty learning and that successful treatments, particularly exposure therapy, work by remediating maladaptive avoidance from dysfunctional explore/exploit decisions in uncertain, potentially aversive situations. This framework reconciles several inconsistencies in the literature and provides a path forward to better understand and treat anxiety.

Impairment of arbitration between model-based and model-free reinforcement learning in obsessive-compulsive disorder

Introduction

Obsessive-compulsive disorder (OCD) is characterized by an imbalance between goal-directed and habitual learning systems in behavioral control, but it is unclear whether these impairments are due to a single system abnormality of the goal-directed system or due to an impairment in a separate arbitration mechanism that selects which system controls behavior at each point in time.

Methods

A total of 30 OCD patients and 120 healthy controls performed a 2-choice, 3-stage Markov decision-making paradigm. Reinforcement learning models were used to estimate goal-directed learning (as model-based reinforcement learning) and habitual learning (as model-free reinforcement learning). In general, 29 high Obsessive-Compulsive Inventory-Revised (OCI-R) score controls, 31 low OCI-R score controls, and all 30 OCD patients were selected for the analysis.

Results

Obsessive-compulsive disorder (OCD) patients showed less appropriate strategy choices than controls regardless of whether the OCI-R scores in the control subjects were high (p = 0.012) or low (p < 0.001), specifically showing a greater model-free strategy use in task conditions where the model-based strategy was optimal. Furthermore, OCD patients (p = 0.001) and control subjects with high OCI-R scores (H-OCI-R; p = 0.009) both showed greater system switching rather than consistent strategy use in task conditions where model-free use was optimal.

Conclusion

These findings indicated an impaired arbitration mechanism for flexible adaptation to environmental demands in both OCD patients and healthy individuals reporting high OCI-R scores.

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.

Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective

A central goal of computational psychiatry is to identify systematic relationships between transdiagnostic dimensions of psychiatric symptomatology and the latent learning and decision-making computations that inform individuals' thoughts, feelings, and choices. Most psychiatric disorders emerge prior to adulthood, yet little work has extended these computational approaches to study the development of psychopathology. Here, we lay out a roadmap for future studies implementing this approach by developing empirically and theoretically informed hypotheses about how developmental changes in model-based control of action and Pavlovian learning processes may modulate vulnerability to anxiety and addiction. We highlight how insights from studies leveraging computational approaches to characterize the normative developmental trajectories of clinically relevant learning and decision-making processes may suggest promising avenues for future developmental computational psychiatry research.

Disrupted state transition learning as a computational marker of compulsivity

BACKGROUND

Disorders involving compulsivity, fear, and anxiety are linked to beliefs that the world is less predictable. We lack a mechanistic explanation for how such beliefs arise. Here, we test a hypothesis that in people with compulsivity, fear, and anxiety, learning a probabilistic mapping between actions and environmental states is compromised.

METHODS

In Study 1 (n = 174), we designed a novel online task that isolated state transition learning from other facets of learning and planning. To determine whether this impairment is due to learning that is too fast or too slow, we estimated state transition learning rates by fitting computational models to two independent datasets, which tested learning in environments in which state transitions were either stable (Study 2: n = 1413) or changing (Study 3: n = 192).

RESULTS

Study 1 established that individuals with higher levels of compulsivity are more likely to demonstrate an impairment in state transition learning. Preliminary evidence here linked this impairment to a common factor comprising compulsivity and fear. Studies 2 and 3 showed that compulsivity is associated with learning that is too fast when it should be slow (i.e. when state transition are stable) and too slow when it should be fast (i.e. when state transitions change).

CONCLUSIONS

Together, these findings indicate that compulsivity is associated with a dysregulation of state transition learning, wherein the rate of learning is not well adapted to the task environment. Thus, dysregulated state transition learning might provide a key target for therapeutic intervention in compulsivity.

Emotions as computations

Emotions ubiquitously impact action, learning, and perception, yet their essence and role remain widely debated. Computational accounts of emotion aspire to answer these questions with greater conceptual precision informed by normative principles and neurobiological data. We examine recent progress in this regard and find that emotions may implement three classes of computations, which serve to evaluate states, actions, and uncertain prospects. For each of these, we use the formalism of reinforcement learning to offer a new formulation that better accounts for existing evidence. We then consider how these distinct computations may map onto distinct emotions and moods. Integrating extensive research on the causes and consequences of different emotions suggests a parsimonious one-to-one mapping, according to which emotions are integral to how we evaluate outcomes (pleasure & pain), learn to predict them (happiness & sadness), use them to inform our (frustration & content) and others' (anger & gratitude) actions, and plan in order to realize (desire & hope) or avoid (fear & anxiety) uncertain outcomes.

Predicting normative data in healthy individuals on the computerized wisconsin card sorting test using regression models

BACKGROUND

Computerized neuropsychological tests provide advantages to clinicians with cost, administration, and time. However, studies have pointed out performance differences between manual and computerized versions of some neuropsychological tests. One of these is the Wisconsin Card Sorting Test (WCST). Due to the performance difference, the normative data of manual tests cannot be used for their computerized versions. Therefore, normative data searches are needed for computerized versions.

OBJECTIVE

This study aimed to determine the norm values of WCST-CV in a healthy sample.

METHODS

422 healthy adults aged 18-78 participated in this study. WCST-CVsub-scores are modeled by Regression Analysis based on Age and Education level to generate normative data. Among the 13 WCST scores, the regression models for WCST 2, WCST 3, WCST 4, WCST 10, and WCST 11 are significant. WCST 2, WCST 4, and WCST 11 scores are estimated with Ordinary Least Squares (OLS). However, WCST 3 and WCST 10 scores are estimated with Weighted Least Squares (WLS) due to the violation of the homoscedasticity assumption.

RESULTS

The regression results show that p-values calculated from error increase as age and education level increase.

CONCLUSION

As a result of our research, norm values between 18-78 years of age were produced using RA. It was determined that gender was not significant for any sub-score. Therefore, only age and education level from socio-demographic variables were included in the model.

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Human behaviours are guided by how confident we feel in our abilities. When confidence does not reflect objective performance, this can impact critical adaptive functions and impair life quality. Distorted decision-making and confidence have been associated with mental health problems. Here, utilising advances in computational and transdiagnostic psychiatry, we sought to map relationships between psychopathology and both decision-making and confidence in the general population across two online studies (N's = 344 and 473, respectively). The results revealed dissociable decision-making and confidence signatures related to distinct symptom dimensions. A dimension characterised by compulsivity and intrusive thoughts was found to be associated with reduced objective accuracy but, paradoxically, increased absolute confidence, whereas a dimension characterized by anxiety and depression was associated with systematically low confidence in the absence of impairments in objective accuracy. These relationships replicated across both studies and distinct cognitive domains (perception and general knowledge), suggesting that they are reliable and domain general. Additionally, whereas Big-5 personality traits also predicted objective task performance, only symptom dimensions related to subjective confidence. Domain-general signatures of decision-making and metacognition characterise distinct psychological dispositions and psychopathology in the general population and implicate confidence as a central component of mental health.

Gambling Environment Exposure Increases Temporal Discounting but Improves Model-Based Control in Regular Slot-Machine Gamblers

Gambling disorder is a behavioral addiction that negatively impacts personal finances, work, relationships and mental health. In this pre-registered study (https://osf.io/5ptz9/) we investigated the impact of real-life gambling environments on two computational markers of addiction, temporal discounting and model-based reinforcement learning. Gambling disorder is associated with increased temporal discounting and reduced model-based learning. Regular gamblers (n = 30, DSM-5 score range 3-9) performed both tasks in a neutral (café) and a gambling-related environment (slot-machine venue) in counterbalanced order. Data were modeled using drift diffusion models for temporal discounting and reinforcement learning via hierarchical Bayesian estimation. Replicating previous findings, gamblers discounted rewards more steeply in the gambling-related context. This effect was positively correlated with gambling related cognitive distortions (pre-registered analysis). In contrast to our pre-registered hypothesis, model-based reinforcement learning was improved in the gambling context. Here we show that temporal discounting and model-based reinforcement learning are modulated in opposite ways by real-life gambling cue exposure. Results challenge aspects of habit theories of addiction, and reveal that laboratory-based computational markers of psychopathology are under substantial contextual control.

Socioemotional deficit and HPA axis time response in high compulsive rats selected by schedule-induced polydipsia

Compulsivity is a failure to stop an ongoing behavior that has become inappropriate to the situation and is recognized as a transdiagnostic trait present in different neuropsychiatric disorders. The implication of motivation and emotion, as well as the stress response in compulsive population has not been fully understood. We assessed the motivation to reward and cues, the emotional response in different contexts and the hypothalamic-pituitary-adrenal (HPA) axis response in rats selected by a preclinical model of compulsive behavior. Firstly, high (HD) or low (LD) drinkers were selected according to their drinking behavior on schedule-induced polydipsia (SIP). Then, we assessed motivation by the propensity to attribute incentive salience to rewards on Pavlovian Conditioned Approach (PavCA) and motivation to gain reward on Progressive Ratio Schedule of Reinforcement (PRSR). Emotion was measured by Social Dominance on the Tube Test (SDTT) and emotional memory on Passive Avoidance (PA). Plasma corticosterone (CORT) levels in response to SIP were assessed. HD rats showed a socioemotional deficit by fewer victories on the SDTT, and an increased latency to enter the dark compartment on the PA. No differences were found between groups regarding to motivational assessment. Moreover, HD rats revealed a blunted time response in the increase of CORT levels at 45 min after SIP compared to LD rats. The findings show that the compulsive phenotype of HD rats exhibit less social dominance, more resistance to extinction and a differential CORT time response to SIP. These findings may contribute to highlight the relevance of assessing socioemotional behaviors and stress response for a better characterization of the vulnerability to compulsive spectrum disorders.

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.

Conflict and competition between model-based and model-free control

A large literature has accumulated suggesting that human and animal decision making is driven by at least two systems, and that important functions of these systems can be captured by reinforcement learning algorithms. The "model-free" system caches and uses stimulus-value or stimulus-response associations, and the "model-based" system implements more flexible planning using a model of the world. However, it is not clear how the two systems interact during deliberation and how a single decision emerges from this process, especially when they disagree. Most previous work has assumed that while the systems operate in parallel, they do so independently, and they combine linearly to influence decisions. Using an integrated reinforcement learning/drift-diffusion model, we tested the hypothesis that the two systems interact in a non-linear fashion similar to other situations with cognitive conflict. We differentiated two forms of conflict: action conflict, a binary state representing whether the systems disagreed on the best action, and value conflict, a continuous measure of the extent to which the two systems disagreed on the difference in value between the available options. We found that decisions with greater value conflict were characterized by reduced model-based control and increased caution both with and without action conflict. Action conflict itself (the binary state) acted in the opposite direction, although its effects were less prominent. We also found that between-system conflict was highly correlated with within-system conflict, and although it is less clear a priori why the latter might influence the strength of each system above its standard linear contribution, we could not rule it out. Our work highlights the importance of non-linear conflict effects, and provides new constraints for more detailed process models of decision making. It also presents new avenues to explore with relation to disorders of compulsivity, where an imbalance between systems has been implicated.

Damage to the dorsolateral prefrontal cortex is associated with repetitive compulsive behaviors in patients with penetrating brain injury

Background

Damage to cortico-striato-thalamo-cortical (CSTC) circuits is associated with the development of repetitive behaviours in animals and humans. However, the types of repetitive behaviours that are developed after injury to these structures are poorly defined. This study examines the effect of damage to separate elements of CSTC circuits sustained by veterans of the Vietnam War on obsessions, compulsions, and tics.

Methods

We performed partial correlations (correcting for cognition, age, education, and global brain damage) between volume loss from traumatic brain injury in specific elements of CSTC circuits (lateral and medial orbitofrontal and dorsolateral prefrontal cortices, anterior cingulate cortex, thalamus, and basal ganglia) and scores on a modified version of the Yale-Brown Obsessive Compulsive Scale Symptom Checklist and the Yale Global Tic Severity Scale in 83 Vietnam war veterans with penetrating brain injuries at different sites throughout the brain.

Results

We found that volume loss in the left dorsolateral prefrontal cortex was associated with the development of compulsive behaviours (r=0.32, padj<0.05) whereas volume loss in the basal ganglia was associated with the development of tics (r=0.33, padj<0.05).

Conclusion

Our findings indicate that damage to specific CSTC elements can be associated with the development of compulsive behaviours and tics that are not necessarily accompanied by obsessions.

A double-blind study assessing the impact of orbitofrontal theta burst stimulation on goal-directed behavior

Patients with disorders of compulsivity show impairments in goal-directed behavior, which have been linked to orbitofrontal cortex (OFC) dysfunction. We recently showed that continuous theta burst stimulation (cTBS), which reduces OFC activity, had a beneficial effect on compulsive behaviors both immediately and at 1 week follow-up compared with inhibitory TBS (iTBS). In this same sample, we investigated whether two behavioral measures of goal-directed control (devaluation success on a habit override task; model-based planning on the two-step task) were also affected by acute modulation of OFC activity. Overall, model-based planning and devaluation success were significantly related to each other and (for devaluation success) to symptoms in our transdiagnostic clinical sample. These measures were moderately to highly stable across time. In individuals with low levels of model-based planning, active cTBS improved devaluation success. Analogous to previously reported clinical effects, this effect was specific to cTBS and not iTBS. Overall, results suggested that measures of goal directed behavior are reliable but less affected by cTBS than clinical self-report. Future research should continue to examine longitudinal changes in behavioral measures to determine their temporal relationship with symptom improvement after treatment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The effect of apathy and compulsivity on planning and stopping in sequential decision-making

Real-life decision-making often comprises sequences of successive decisions about whether to take opportunities as they are encountered or keep searching for better ones instead. We investigated individual differences related to such sequential decision-making and link them especially to apathy and compulsivity in a large online sample (discovery sample: n = 449 and confirmation sample: n = 756). Our cognitive model revealed distinct changes in the way participants evaluated their environments and planned their own future behaviour. Apathy was linked to decision inertia, i.e., automatically persisting with a sequence of searches for longer than appropriate given the value of searching. Thus, despite being less motivated, they did not avoid the effort associated with longer searches. In contrast, compulsivity was linked to self-reported insensitivity to the cost of continuing with a sequence of searches. The objective measures of behavioural cost insensitivity were clearly linked to compulsivity only in the discovery sample. While the confirmation sample showed a similar effect, it did not reach significance. Nevertheless, in both samples, participants reported awareness of such bias (experienced as "overchasing"). In addition, this awareness made them report preemptively avoiding situations related to the bias. However, we found no evidence of them actually preempting more in the task, which might mean a misalignment of their metacognitive beliefs or that our behavioural measures were incomplete. In summary, individual variation in distinct, fundamental aspects of sequential decision-making can be linked to variation in 2 measures of behavioural traits associated with psychological illness in the normal population.

Computational validity: using computation to translate behaviours across species

We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Psychedelic Therapy's Transdiagnostic Effects: A Research Domain Criteria (RDoC) Perspective

Accumulating clinical evidence shows that psychedelic therapy, by synergistically combining psychopharmacology and psychological support, offers a promising transdiagnostic treatment strategy for a range of disorders with restricted and/or maladaptive habitual patterns of emotion, cognition and behavior, notably, depression (MDD), treatment resistant depression (TRD) and addiction disorders, but perhaps also anxiety disorders, obsessive-compulsive disorder (OCD), Post-Traumatic Stress Disorder (PTSD) and eating disorders. Despite the emergent transdiagnostic evidence, the specific clinical dimensions that psychedelics are efficacious for, and associated underlying neurobiological pathways, remain to be well-characterized. To this end, this review focuses on pre-clinical and clinical evidence of the acute and sustained therapeutic potential of psychedelic therapy in the context of a transdiagnostic dimensional systems framework. Focusing on the Research Domain Criteria (RDoC) as a template, we will describe the multimodal mechanisms underlying the transdiagnostic therapeutic effects of psychedelic therapy, traversing molecular, cellular and network levels. These levels will be mapped to the RDoC constructs of negative and positive valence systems, arousal regulation, social processing, cognitive and sensorimotor systems. In summarizing this literature and framing it transdiagnostically, we hope we can assist the field in moving toward a mechanistic understanding of how psychedelics work for patients and eventually toward a precise-personalized psychedelic therapy paradigm.

Imbalance in Functional and Structural Connectivity Underlying Goal-Directed and Habitual Learning Systems in Obsessive-Compulsive Disorder

An imbalance between the goal-directed and habitual learning systems has been proposed to underlie compulsivity in obsessive-compulsive disorder (OCD). In addition, the overall balance between these systems may be influenced by stress hormones. We examined the multimodal networks underlying these dual learning systems. Both functional and structural measures indicated reduced connectivity within the goal-directed subnetwork (FC: P = 0.042; SC-FN: P = 0.014) and reduced connectivity between the goal-directed and habitual subnetworks (FC: P = 0.014; SC-FA: P = 0.052), but no differences within the habitual subnetwork in patients with OCD compared with controls. Path modeling indicated that anatomical connectivity in the goal-directed subnetwork influenced compulsive symptoms (R2 = 0.41), whereas functional connectivity within the habit subnetwork and between goal-directed and habitual subnetworks influenced obsessive symptoms (R2 = 0.63). In addition, the relationship between anatomical connectivity in the goal-directed subnetwork and compulsion was moderated by the stress hormone ACTH (adrenocorticotropic hormone), such that at low levels of ACTH greater connectivity resulted in lower compulsion, but at high levels of ACTH this relationship was reversed. These results provide new insights into the neural correlates of the imbalance between dual learning systems, and their relationship with symptom dimensions in patients with OCD. It may further support the reconceptualization of OCD as "compulsive-obsessive disorder," with a greater focus on the transdiagnostic dimension of compulsivity.

Examining the effect of depressive symptoms on habit formation and habit-breaking

BACKGROUND AND OBJECTIVES

Dysfunction in reward processing is a hallmark feature of depression. In the context of reinforcement learning, previous research has linked depression with reliance on simple habit-driven ('model-free') learning strategies over more complex, goal-directed ('model-based') strategies. However, the relationship between depression and habit-breaking remains an under-explored research area. The current study sought to bridge this gap by investigating the effect of depressive symptoms on habit formation and habit-breaking under monetary and social feedback conditions. Additionally, we examined whether spontaneous eyeblink rate (EBR), an indirect marker for striatal dopamine levels, would modulate such effects.

METHODS

Depressive symptoms were operationalized using self-report measures. To examine differences in habit formation and habit breaking, undergraduate participants (N = 156) completed a two-stage reinforcement learning task with a devaluation procedure using either monetary or social feedback.

RESULTS

Regression results showed that in the monetary feedback condition, spontaneous EBR moderated the relationship between depressive symptoms and model-free strategies; individuals with more depressive symptomatology and high EBR (higher dopamine levels) exhibited increased reliance on model-free strategies. Depressive symptoms negatively predicted devaluation sensitivity, indicative of difficulty in habit-breaking, in both monetary and social feedback contexts.

LIMITATIONS

Social feedback relied on fixed feedback rather than real-time peer evaluations; depressive symptoms were measured using self-report rather than diagnostic criteria for Major Depressive Disorder; dopaminergic functioning was measured using EBR rather than PET imaging; potential confounds were not controlled for.

CONCLUSIONS

These findings have implications for identifying altered patterns of habit formation and deficits in habit-breaking among those experiencing depressive symptoms.

Assigning the right credit to the wrong action: compulsivity in the general population is associated with augmented outcome-irrelevant value-based learning

Compulsive behavior is enacted under a belief that a specific act controls the likelihood of an undesired future event. Compulsive behaviors are widespread in the general population despite having no causal relationship with events they aspire to influence. In the current study, we tested whether there is an increased tendency to assign value to aspects of a task that do not predict an outcome (i.e., outcome-irrelevant learning) among individuals with compulsive tendencies. We studied 514 healthy individuals who completed self-report compulsivity, anxiety, depression, and schizotypal measurements, and a well-established reinforcement-learning task (i.e., the two-step task). As expected, we found a positive relationship between compulsivity and outcome-irrelevant learning. Specifically, individuals who reported having stronger compulsive tendencies (e.g., washing, checking, grooming) also tended to assign value to response keys and stimuli locations that did not predict an outcome. Controlling for overall goal-directed abilities and the co-occurrence of anxious, depressive, or schizotypal tendencies did not impact these associations. These findings indicate that outcome-irrelevant learning processes may contribute to the expression of compulsivity in a general population setting. We highlight the need for future research on the formation of non-veridical action-outcome associations as a factor related to the occurrence and maintenance of compulsive behavior.

How Local and Global Metacognition Shape Mental Health

Metacognition is the ability to reflect on our own cognition and mental states. It is a critical aspect of human subjective experience and operates across many hierarchical levels of abstraction-encompassing local confidence in isolated decisions and global self-beliefs about our abilities and skills. Alterations in metacognition are considered foundational to neurologic and psychiatric disorders, but research has mostly focused on local metacognitive computations, missing out on the role of global aspects of metacognition. Here, we first review current behavioral and neural metrics of local metacognition that lay the foundation for this research. We then address the neurocognitive underpinnings of global metacognition uncovered by recent studies. Finally, we outline a theoretical framework in which higher hierarchical levels of metacognition may help identify the role of maladaptive metacognitive evaluation in mental health conditions, particularly when combined with transdiagnostic methods.

Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy

IMPORTANCE

Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets.

OBJECTIVE

To determine associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment.

DESIGN, SETTING, AND PARTICIPANTS

In this mixed cross-sectional-cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021.

MAIN OUTCOMES AND MEASURES

Computational model-based analyses of participants' choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT).

RESULTS

Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model-based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression β = -0.14; 95% credible interval [CrI], -0.12 to -0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = -2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression β = -0.11; 95% CrI, -0.20 to -0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = -0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09 to 0.77).

CONCLUSIONS AND RELEVANCE

In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.

Model-Based Planning Deficits in Compulsivity Are Linked to Faulty Neural Representations of Task Structure

Compulsive individuals have deficits in model-based planning, but the mechanisms that drive this have not been established. We examined two candidates-that compulsivity is linked to (1) an impaired model of the task environment and/or (2) an inability to engage cognitive control when making choices. To test this, 192 participants performed a two-step reinforcement learning task with concurrent EEG recordings, and we related the neural and behavioral data to their scores on a self-reported transdiagnostic dimension of compulsivity. To examine subjects' internal model of the task, we used established behavioral and neural responses to unexpected events [reaction time (RT) slowing, P300 wave, and parietal-occipital alpha band power] measured when an unexpected transition occurred. To assess cognitive control, we probed theta power at the time of initial choice. As expected, model-based planning was linked to greater behavioral (RT) and neural (alpha power, but not P300) sensitivity to rare transitions. Critically, the sensitivities of both RT and alpha to task structure were weaker in those high in compulsivity. This RT-compulsivity effect was tested and replicated in an independent pre-existing dataset (N = 1413). We also found that mid-frontal theta power at the time of choice was reduced in highly compulsive individuals though its relation to model-based planning was less pronounced. These data suggest that model-based planning deficits in compulsive individuals may arise, at least in part, from having an impaired representation of the environment, specifically how actions lead to future states.SIGNIFICANCE STATEMENT Compulsivity is linked to poorer performance on tasks that require model-based planning, but it is unclear what precise mechanisms underlie this deficit. Do compulsive individuals fail to engage cognitive control at the time of choice? Or do they have difficulty in building and maintaining an accurate representation of their environment, the foundation needed to behave in a goal-directed manner? With reaction time and EEG measures in 192 individuals who performed a two-step decision-making task, we found that compulsive individuals are less sensitive to surprising action-state transitions, where they slow down less and show less alpha band suppression following a rare transition. These findings implicate failures in maintaining an accurate model of the world in model-based planning deficits in compulsivity.

Reliability of web-based affective auditory stimulus presentation

Web-based experimental testing has seen exponential growth in psychology and cognitive neuroscience. However, paradigms involving affective auditory stimuli have yet to adapt to the online approach due to concerns about the lack of experimental control and other technical challenges. In this study, we assessed whether sounds commonly used to evoke affective responses in-lab can be used online. Using recent developments to increase sound presentation quality, we selected 15 commonly used sound stimuli and assessed their impact on valence and arousal states in a web-based experiment. Our results reveal good inter-rater and test-retest reliabilities, with results comparable to in-lab studies. Additionally, we compared a variety of previously used unpleasant stimuli, allowing us to identify the most aversive among these sounds. Our findings demonstrate that affective sounds can be reliably delivered through web-based platforms, which help facilitate the development of new auditory paradigms for affective online experiments.

Transdiagnostic dimensions in obsessive-compulsive and related disorders: associations with internalizing and externalizing symptoms

BACKGROUND

Hierarchical structural models of psychopathology rarely extend to obsessive-compulsive spectrum disorders. The current study sought to examine the higher-order structure of the obsessive-compulsive and related disorders (OCRDs) in DSM-5: obsessive-compulsive disorder (OCD), hoarding disorder (HD), body dysmorphic disorder (BDD), trichotillomania (hair-pulling disorder; HPD) and excoriation (skin-picking) disorder (SPD).

METHODS

Adult patients in a partial hospital program (N = 532) completed a dimensional measure of the five OCRDs. We used confirmatory factor analysis to identify the optimal model of the comorbidity structure. We then examined the associations between the transdiagnostic factors and internalizing and externalizing symptoms (i.e. depression, generalized anxiety, neuroticism, and drug/alcohol cravings).

RESULTS

The best fitting model included two correlated higher-order factors: an obsessions-compulsions (OC) factor (OCD, BDD, and HD), and a body-focused repetitive behavior (BFRB) factor (HPD and SPD). The OC factor, not the BFRB factor, had unique associations with internalizing symptoms (standardized effects = 0.42-0.66) and the BFRB factor, not the OC factor, had small marginally significant unique association with drug/alcohol cravings (standardized effect = 0.22, p = 0.088).

CONCLUSIONS

The results mirror findings from twin research and indicate that OCD, BDD, and HD share liability that is significantly associated with internalizing symptoms, but this liability may be relatively less important for BFRBs. Further research is needed to better examine the associations between BFRBs and addictive disorders.

Fronto-striatal structures related with model-based control as an endophenotype for obsessive-compulsive disorder

Recent theories suggest a shift from model-based goal-directed to model-free habitual decision-making in obsessive–compulsive disorder (OCD). However, it is yet unclear, whether this shift in the decision process is heritable. We investigated 32 patients with OCD, 27 unaffected siblings (SIBs) and 31 healthy controls (HCs) using the two-step task. We computed behavioral and reaction time analyses and fitted a computational model to assess the balance between model-based and model-free control. 80 subjects also underwent structural imaging. We observed a significant ordered effect for the shift towards model-free control in the direction OCD > SIB > HC in our computational parameter of interest. However less directed analyses revealed no shift towards model-free control in OCDs. Nonetheless, we found evidence for reduced model-based control in OCDs compared to HCs and SIBs via 2nd stage reaction time analyses. In this measure SIBs also showed higher levels of model-based control than HCs. Across all subjects these effects were associated with the surface area of the left medial/right dorsolateral prefrontal cortex. Moreover, correlations between bilateral putamen/right caudate volumes and these effects varied as a function of group: they were negative in SIBs and OCDs, but positive in HCs. Associations between fronto-striatal regions and model-based reaction time effects point to a potential endophenotype for OCD.

The relationship between apathy and impulsivity in large population samples

Apathy and impulsivity are debilitating conditions associated with many neuropsychiatric conditions, and expressed to variable degrees in healthy people. While some theories suggest that they lie at different ends of a continuum, others suggest their possible co-existence. Surprisingly little is known, however, about their empirical association in the general population. Here, gathering data from six large studies (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = 3755$$\end{document}n=3755), we investigated the relationship between measures of apathy and impulsivity in young adults. The questionnaires included commonly used self-assessment tools—Apathy Evaluation Scale, Barratt Impulsiveness Scale (BIS-11) and UPPS-P Scale—as well as a more recent addition, the Apathy Motivation Index (AMI). Remarkably, across datasets and assessment tools, global measures of apathy and impulsivity correlated positively. However, analysis of sub-scale scores revealed a more complex relationship. Although most dimensions correlated positively with one another, there were two important exceptions revealed using the AMI scale. Social apathy was mostly negatively correlated with impulsive behaviour, and emotional apathy was orthogonal to all other sub-domains. These results suggest that at a global level, apathy and impulsivity do not exist at distinct ends of a continuum. Instead, paradoxically, they most often co-exist in young adults. Processes underlying social and emotional apathy, however, appear to be different and dissociable from behavioural apathy and impulsivity.

Habitual Routines and Automatic Tendencies Differential Roles in Alcohol Misuse Among Undergraduates

There is a debate over whether actions that resist devaluation (i.e., compulsive alcohol consumption) are primarily habit- or goal-directed. The incentive habit account of compulsive actions has received support from behavioral paradigms and brain imaging. In addition, the self-reported Creature of Habit Scale (COHS) has been proposed to capture inter-individual differences in habitual tendencies. It is subdivided into two dimensions: routine and automaticity. We first considered a French version of this questionnaire for validation, based on a sample of 386 undergraduates. The relationship between two dimensions of habit and the risk of substance use disorder and impulsive personality traits was also investigated. COHS has good psychometric properties with both features of habits positively associated with an Obsessive-Compulsive Inventory score. Besides, the propensity to rely more on routines was associated with lower levels of alcohol abuse and nicotine use, suggesting that some degree of routine might act as a protective factor against substance use. In contrast, a high automaticity score was associated with an increased risk of harmful alcohol use. These results demonstrate that the COHS is a valid measure of habitual tendencies and represents a useful tool for capturing inter-individual variations in drug use problems in undergraduates.

Neurocognitive Endophenotypes of OCD

In this chapter, I address the concept of endophenotypes for obsessive-compulsive disorder (OCD). Endophenotypes are objective and heritable quantitative traits hypothesized to be more biologically tractable than distal clinical phenotypes. This approach has been adopted to gain a better understanding of psychiatric conditions in general. It is theorized that endophenotypes will particularly assist in clarifying both the diagnostic status and aetiological origins of complex neuropsychiatric conditions such as OCD. At the cognitive level, separable constructs of relevance for OCD have been identified. The prevailing model for OCD assumes the development of abnormalities within fronto-striatal neural circuits leading to impairment of executive functions and their neuropsychological subcomponents. Here, I address whether this model can guide towards the identification of endophenotypes for this condition and discuss possible implications.

Recent Developments in the Habit Hypothesis of OCD and Compulsive Disorders

This chapter aims to familiarise the reader with a diverse and fast-growing literature concerning the role that habits play in obsessive-compulsive disorder (OCD). Core concepts will be introduced, including how the balance between habits and a more deliberate form of action selection (goal-directed control) has traditionally been measured and how cross-species translation, neuroscience tools, and computational modelling have been used to build on these basic principles and reveal core mechanisms under study today. Next, the application of these methods to the study of OCD and related disorders will be detailed, converging on a theory that enhanced habit expression, and indeed compulsions in OCD, might arise from deficits in goal-directed control systems. These basic findings will be contextualised in terms of major tide changes in the field, including the shift from categorical disease frameworks to dimensional ones. Mechanistically, recent research concerning how goal-directed deficits arise, perhaps through failures in the construction of a mental model, are discussed along with studies critically evaluating our ability to measure habits in humans, in a laboratory setting. The chapter ends with a nod to the future, focusing on the need for clinically oriented, longitudinal, and intervention-based research that aim to translate what is now a wealth of cross-sectional mechanistic insights to the clinic.

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.

Towards a computational psychiatry of juvenile obsessive-compulsive disorder

Obsessive-Compulsive Disorder (OCD) most often emerges during adolescence, but we know little about the aberrant neural and cognitive developmental mechanisms that underlie its emergence during this critical developmental period. To move towards a computational psychiatry of juvenile OCD, we review studies on the computational, neuropsychological and neural alterations in juvenile OCD and link these findings to the adult OCD and cognitive neuroscience literature. We find consistent difficulties in tasks entailing complex decision making and set shifting, but limited evidence in other areas that are altered in adult OCD, such as habit and confidence formation. Based on these findings, we establish a neurocomputational framework that illustrates how cognition can go awry and lead to symptoms of juvenile OCD. We link these possible aberrant neural processes to neuroimaging findings in juvenile OCD and show that juvenile OCD is mainly characterised by disruptions of complex reasoning systems.

Neural mechanisms of persistent avoidance in OCD: A novel avoidance devaluation study

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We investigated goal-directed processing in OCD and healthy controls using a rule-based devaluation paradigm.

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OCD patients with longer illness duration showed poorer goal-directed performance.

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Neural differences were observed in orbitofrontal cortex, premotor cortex, inferior frontal gyrus and the caudate, relating to group and individual differences in devaluation performance.

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Together the findings identify neural correlates of goal-directed deficits in OCD, and support a dual-process model of actions and habits.

Obsessive-Compulsive Disorder (OCD) is characterized by repetitive avoidance behavior which is distressing and associated with marked impairment of everyday life. Recently, paradigms have been designed to explore the hypothesis that avoidance behavior in OCD is consistent with a formal conception of habit. Such studies have involved a devaluation paradigm, in which the value of a previously rewarded cue is altered so that avoidance is no longer necessary. We employed a rule-based avoidance task which included a devaluation, examining behavioral performance on the task and their neural correlates using functional MRI in groups of participants with OCD (n = 44) and healthy control participants (n = 46). Neuroimaging data were analyzed using a general linear model (GLM), modelling valued, devalued and control cues, as well as feedback events. First, while no overall effect of OCD was seen on devaluation performance, patients with longer illness duration showed poorer devaluation performance (χ² = 13.84, p < 0.001). Reduced devaluation was related to impaired learning on the overtraining phase of the task, and to enhanced feedback activation in the caudate and parietal lobe during within-scanner retraining (T = 5.52, p_FWE = 0.003), across all participants. Second, a significant interaction effect was observed in the premotor cortex (F = 29.03, p_FWE = 0.007) coupled to the devalued cue. Activations were divergent in participants with OCD (lower activation) and healthy controls (higher activation) who did not change responding to the devalued cue following devaluation, and intermediate in participants who did change responding (T = 5.39, p_FWE = 0.003). Finally, consistent with previous work, medial orbitofrontal cortex activation coupled to valued cues was reduced in OCD compared to controls (T = 3.49, p_FWE = 0.009). The findings are discussed in terms of a prediction error-based model of goal-directed and habitual control: specifically, how goal-directed control might be diminished in OCD in favor of habits. They suggest that illness duration might be significant determinant of variation in impaired goal-directed learning in OCD, and be a factor relevant for understanding discrepancies across studies. Overall, the study shows the potential of conceptual replication attempts to provide complementary insights into compulsive behavior and its associated neural circuitry in OCD.