Background: While the Yale-Brown Obsessive-Compulsive Scale Second Edition (Y-BOCS-II) is the gold-standard for measurement of obsessive-compulsive (OC) symptom severity, its factor structure is still a matter of debate and, most importantly, criterion validity for diagnosis of OC disorder (OCD) has not been tested. This study aimed to clarify factor structure and criterion validity of the Y-BOCS-II.Methods: We first validated and quantified the psychometric properties of a culturally adapted Portuguese translation of the Y-BOCS-II (PY-BOCS-II). The PY-BOCS-II and other psychometric instruments, including the OCD subscale of the Structured Clinical Interview for the DSM-IV, used to define OCD diagnosis, were administered to 187 participants (52 patients with OCD, 18 with other mood and anxiety disorders and 117 healthy subjects). In a subsample of 20 OCD patients and the 18 patients with other diagnoses, PY-BOCS-II was applied by clinicians blinded to diagnosis.Results: PY-BOCS-II had excellent internal consistency (Cronbach's α = 0.96) and very good test-retest reliability (Pearson's r = 0.94). Exploratory factor analysis revealed a two-factor structure with loadings consistent with the Obsessions and Compulsions subscales, and there was good to acceptable convergent and divergent validity. Importantly, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve suggested elevated accuracy in discriminating between patients with OCD and control subjects (AUC = 0.96; 95% confidence interval [CI]: 0.92–0.99), that was retained in comparisons with age, gender and education matched controls (AUC = 0.95; 95% CI: 0.91–0.99), as well as with patients with other mood and anxiety disorders (AUC = 0.93; 95% CI: 0.84–1). Additionally, a cut-off score of 13 had optimal discriminatory ability for the diagnosis of OCD, with sensitivity ranging between 85 and 90%, and specificity between 94 and 97%, respectively when all samples or only the clinical samples were considered.Conclusion: The PY-BOCS-II has excellent psychometric properties to assess the severity of obsessive-compulsive symptoms, reflecting obsessive, and compulsive dimensions, compatible with currently defined subscales. Furthermore, we found that a cut-off of 13 for the Y-BOCS-II total score has good to excellent sensitivity and specificity for the diagnosis of OCD.

The understanding of obsessive-compulsive disorder (OCD) has evolved with the knowledge of behavior, the brain, and their relationship. Modern views of OCD as a neuropsychiatric disorder originated from early lesion studies, with more recent models incorporating detailed neuropsychological findings, such as perseveration in set-shifting tasks, and findings of altered brain structure and function, namely of orbitofrontal corticostriatal circuits and their limbic connections. Interestingly, as neurobiological models of OCD evolved from cortical and cognitive to sub-cortical and behavioral, the focus of OCD phenomenology also moved from thought control and contents to new concepts rooted in animal models of action control. Most recently, the proposed analogy between habitual action control and compulsive behavior has led to the hypothesis that individuals suffering from OCD may be predisposed to rely excessively on habitual rather than on goal-directed behavioral strategies. Alternatively, compulsions have been proposed to result either from hyper-valuation of certain actions and/or their outcomes, or from excessive uncertainty in the monitoring of action performance, both leading to perseveration in prepotent actions such as washing or checking. In short, the last decades have witnessed a formidable renovation in the pathophysiology, phenomenology, and even semantics, of OCD. Nevertheless, such progress is challenged by several caveats, not least psychopathological oversimplification and overgeneralization of animal to human extrapolations. Here we present an historical overview of the understanding of OCD, highlighting converging studies and trends in neuroscience, psychiatry and neuropsychology, and how they influenced current perspectives on the nosology and phenomenology of this disorder.

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 novel variant of a computationally more complex decision-making task, before and after providing information about task structure, both in healthy volunteers and individuals suffering from obsessive-compulsive (OCD) or other disorders. Initial behaviour was modelfree, with rewards directly reinforcing preceding actions. Model-based control, employing predictions of states resulting from each action, emerged with experience in a minority of subjects, and less in OCD.Providing task structure information strongly increased model-based control, similarly across all groups.Thus, explicit task structural knowledge determines human use of model-based reinforcement learning, and is most readily acquired from instruction rather than experience..

Ketamine is an N-methyl-d-aspartate antagonist which is increasingly being researched and used as a treatment for depression. In low doses, it can cause a transitory modification in consciousness which was classically labelled as ‘dissociation’. However, ketamine is also commonly classified as an atypical psychedelic and it has been recently reported that ego dissolution experiences during ketamine administration are associated with greater antidepressant response. Neuroimaging studies have highlighted several similarities between the effects of ketamine and those of serotonergic psychedelics in the brain; however, no unified account has been proposed for ketamine’s multi-level effects – from molecular to network and psychological levels. Here, we propose that the fast, albeit transient, antidepressant effects observed after ketamine infusions are mainly driven by its acute modulation of reward circuits and sub-acute increase in neuroplasticity, while its dissociative and psychedelic properties are driven by dose- and context-dependent disruption of large-scale functional networks. Computationally, as nodes of the salience network (SN) represent high-level priors about the body (‘minimal’ self) and nodes of the default-mode network (DMN) represent the highest-level priors about narrative self-experience (‘biographical’ self), we propose that transitory SN desegregation and disintegration accounts for ketamine’s ‘ dissociative’ state, while transitory DMN desegregation and disintegration accounts for ketamine’s ‘ psychedelic’ state. In psychedelic-assisted psychotherapy, a relaxation of the highest-level beliefs with psychotherapeutic support may allow a revision of pathological self-representation models, for which neuroplasticity plays a permissive role. Our account provides a multi-level rationale for using the psychedelic properties of ketamine to increase its long-term benefits.

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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 novel variant of a computationally more complex decision-making task, before and after providing information about task structure, both in healthy volunteers and individuals suffering from obsessive-compulsive (OCD) 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 subjects, and less in OCD. Providing task structure information strongly increased model-based control, similarly across all groups. Thus, explicit task structural knowledge determines human use of model-based reinforcement learning, and is most readily acquired from instruction rather than experience .

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