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The current study examined the relationship between early onset cannabis use (before age 16) and different schizotypy dimensions, and whether gender moderates these associations. Participants were 162 cannabis users, aged 15–24 years, who completed an online assessment examining alcohol and other drug use, psychological distress, and schizotypy. Participants were divided according to whether or not they had started using cannabis before the age of 16 (early onset = 47; later onset = 115) and gender (males = 66; females = 96). The interaction between gender and onset group was significantly associated with the dimension of introvertive anhedonia. Follow-up analyses showed that early onset cannabis use was associated with higher levels of introvertive anhedonia in females only. The current findings suggest that gender is an important moderator in the association between early onset cannabis use, schizotypy, and possibly, psychosis risk. [ABSTRACT FROM AUTHOR]

Background: Both impulsivity and compulsivity have been identified as risk factors for problematic use of the internet (PUI). Yet little is known about the relationship between impulsivity, compulsivity and individual PUI symptoms, limiting a more precise understanding of mechanisms underlying PUI., Aims: The current study is the first to use network analysis to (a) examine the unique association among impulsivity, compulsivity and PUI symptoms, and (b) identify the most influential drivers in relation to the PUI symptom community., Method: We estimated a Gaussian graphical model consisting of five facets of impulsivity, compulsivity and individual PUI symptoms among 370 Australian adults (51.1% female, mean age = 29.8, s.d. = 11.1). Network structure and bridge expected influence were examined to elucidate differential associations among impulsivity, compulsivity and PUI symptoms, as well as identify influential nodes bridging impulsivity, compulsivity and PUI symptoms., Results: Results revealed that four facets of impulsivity (i.e. negative urgency, positive urgency, lack of premeditation and lack of perseverance) and compulsivity were related to different PUI symptoms. Further, compulsivity and negative urgency were the most influential nodes in relation to the PUI symptom community due to their highest bridge expected influence., Conclusions: The current findings delineate distinct relationships across impulsivity, compulsivity and PUI, which offer insights into potential mechanistic pathways and targets for future interventions in this space. To realise this potential, future studies are needed to replicate the identified network structure in different populations and determine the directionality of the relationships among impulsivity, compulsivity and PUI symptoms.

Objective: "Subsyndromal" obsessive-compulsive disorder symptoms (OCDSs) are common and cause impaired psychosocial functioning. OCDSs are better captured by dimensional models of psychopathology, as opposed to categorical diagnoses. However, such dimensional approaches require a deep understanding of the underlying neurocognitive drivers and impulsive and compulsive traits (ie, neurocognitive phenotypes) across symptoms. This study investigated inhibitory control and self-monitoring across impulsivity, compulsivity, and their interaction in individuals (n = 40) experiencing mild-moderate OCDSs., Methods: EEG recording concurrent with the stop-signal task was used to elicit event-related potentials (ERPs) indexing inhibitory control (ie, N2 and P3) and self-monitoring (ie, error-related negativity and correct-related negativity (CRN): negativity following erroneous or correct responses, respectively)., Results: During unsuccessful stopping, individuals high in both impulsivity and compulsivity displayed enhanced N2 amplitude, indicative of conflict between the urge to respond and need to stop ( F (3, 33) = 1.48, P < .05, 95% Cl [-0.01, 0.001]). Individuals high in compulsivity and low in impulsivity showed reduced P3 amplitude, consistent with impairments in monitoring failed inhibitory control ( F (3, 24) = 2.033, P < .05, 95% CI [-0.002, 0.045]). Following successful stopping, high compulsivity (independent of impulsivity) was associated with lower CRN amplitude, reflecting hypo-monitoring of correct responses ( F (4, 32) = 4.76, P < .05, 95% CI [0.01, 0.02]), and with greater OCDS severity ( F (3, 36) = 3.32, P < .05, 95% CI [0.03, 0.19])., Conclusion: The current findings provide evidence for differential, ERP-indexed inhibitory control and self-monitoring profiles across impulsive and compulsive phenotypes in OCDSs.

Background: The present study explored the influence of romantic love on the expression of several obsessive-compulsive disorder (OCD) characteristics, including symptom severity, symptom dimensions, age at onset, sensory phenomena (SP), and developmental course, as well as other related comorbid disorders. It was hypothesized that love-precipitated OCD would be associated with a set of distinct characteristics and exhibit greater rates of comorbid disorders., Methods: The analyses were performed using a large sample (n = 981) of clinical patients with a primary diagnosis of OCD (Females = 67.3%, M age = 35.31)., Results: Love-precipitated OCD was associated with greater severity of SP and later age at onset of obsessions. However, symptom severity, symptom dimension, developmental course, and psychiatric comorbidities were not associated with love-precipitated OCD., Conclusion: It was concluded that romantic love does shape the expression of OCD, especially with regard to SP and onset age. These findings encourage further exploration to determine its clinical significance as a phenotype.

Background: Poor mental health is a state of psychological distress that is influenced by lifestyle factors such as sleep, diet, and physical activity. Compulsivity is a transdiagnostic phenotype cutting across a range of mental illnesses including obsessive-compulsive disorder, substance-related and addictive disorders, and is also influenced by lifestyle. Yet, how lifestyle relates to compulsivity is presently unknown, but important to understand to gain insights into individual differences in mental health. We assessed (a) the relationships between compulsivity and diet quality, sleep quality, and physical activity, and (b) whether psychological distress statistically contributes to these relationships., Methods: We collected harmonized data on compulsivity, psychological distress, and lifestyle from two independent samples (Australian n = 880 and US n = 829). We used mediation analyses to investigate bidirectional relationships between compulsivity and lifestyle factors, and the role of psychological distress., Results: Higher compulsivity was significantly related to poorer diet and sleep. Psychological distress statistically mediated the relationship between poorer sleep quality and higher compulsivity, and partially statistically mediated the relationship between poorer diet and higher compulsivity., Conclusions: Lifestyle interventions in compulsivity may target psychological distress in the first instance, followed by sleep and diet quality. As psychological distress links aspects of lifestyle and compulsivity, focusing on mitigating and managing distress may offer a useful therapeutic approach to improve physical and mental health. Future research may focus on the specific sleep and diet patterns which may alter compulsivity over time to inform lifestyle targets for prevention and treatment of functionally impairing compulsive behaviors.

Objective: To (1) confirm whether the Habit, Reward, and Fear Scale is able to generate a 3-factor solution in a population of obsessive-compulsive disorder and alcohol use disorder (AUD) patients; (2) compare these clinical groups in their habit, reward, and fear motivations; and (3) investigate whether homogenous subgroups can be identified to resolve heterogeneity within and across disorders based on the motivations driving ritualistic and drinking behaviors., Methods: One hundred and thirty-four obsessive-compulsive disorder (n = 76) or AUD (n = 58) patients were assessed with a battery of scales including the Habit, Reward, and Fear Scale, the Yale-Brown Obsessive-Compulsive Scale, the Alcohol Dependence Scale, the Behavioral Inhibition/Activation System Scale, and the Urgency, (lack of ) Premeditation, (lack of ) Perseverance, Sensation Seeking, and Positive Urgency Impulsive Behavior Scale., Results: A 3-factor solution reflecting habit, reward, and fear subscores explained 56.6% of the total variance of the Habit, Reward, and Fear Scale. Although the habit and fear subscores were significantly higher in obsessive-compulsive disorder (OCD) and the reward subscores were significantly greater in AUD patients, a cluster analysis identified that the 3 clusters were each characterized by differing proportions of OCD and AUD patients., Conclusions: While affective (reward- and fear-driven) and nonaffective (habitual) motivations for repetitive behaviors seem dissociable from each other, it is possible to identify subgroups in a transdiagnostic manner based on motivations that do not match perfectly motivations that usually described in OCD and AUD patients.

Background: The extent to which obsessive-compulsive and related disorders (OCRDs) are impulsive, compulsive, or both requires further investigation. We investigated the existence of different clusters in an online nonclinical sample and in which groups DSM-5 OCRDs and other related psychopathological symptoms are best placed., Methods: Seven hundred and seventy-four adult participants completed online questionnaires including the Cambridge-Chicago Compulsivity Trait Scale (CHI-T), the Barratt Impulsiveness Scale (BIS-15), and a series of DSM-5 OCRDs symptom severity and other psychopathological measures. We used K-means cluster analysis using CHI-T and BIS responses to test three and four factor solutions. Next, we investigated whether different OCRDs symptoms predicted cluster membership using a multinomial regression model., Results: The best solution identified one "healthy" and three "clinical" clusters (ie, one predominantly "compulsive" group, one predominantly "impulsive" group, and one "mixed"-"compulsive and impulsive group"). A multinomial regression model found obsessive-compulsive, body dysmorphic, and schizotypal symptoms to be associated with the "mixed" and the "compulsive" clusters, and hoarding and emotional symptoms to be related, on a trend level, to the "impulsive" cluster. Additional analysis showed cognitive-perceptual schizotypal symptoms to be associated with the "mixed" but not the "compulsive" group., Conclusions: Our findings suggest that obsessive-compulsive disorder; body dysmorphic disorder and schizotypal symptoms can be mapped across the "compulsive" and "mixed" clusters of the compulsive-impulsive spectrum. Although there was a trend toward hoarding being associated with the "impulsive" group, trichotillomania, and skin picking disorder symptoms did not clearly fit to the demarcated clusters.

Background: Compulsivity can be seen across various mental health conditions and refers to a tendency toward repetitive habitual acts that are persistent and functionally impairing. Compulsivity involves dysfunctional reward-related circuitry and is thought to be significantly heritable. Despite this, its measurement from a transdiagnostic perspective has received only scant research attention. Here we examine both the psychometric properties of a recently developed compulsivity scale, as well as its relationship with compulsive symptoms, familial risk, and reward-related attentional capture., Methods: Two-hundred and sixty individuals participated in the study (mean age = 36.0 [SD = 10.8] years; 60.0% male) and completed the Cambridge-Chicago Compulsivity Trait Scale (CHI-T), along with measures of psychiatric symptoms and family history thereof. Participants also completed a task designed to measure reward-related attentional capture (n = 177)., Results: CHI-T total scores had a normal distribution and acceptable Cronbach's alpha (0.84). CHI-T total scores correlated significantly and positively (all p < 0.05, Bonferroni corrected) with Problematic Usage of the Internet, disordered gambling, obsessive-compulsive symptoms, alcohol misuse, and disordered eating. The scale was correlated significantly with history of addiction and obsessive-compulsive related disorders in first-degree relatives of participants and greater reward-related attentional capture., Conclusions: These findings suggest that the CHI-T is suitable for use in online studies and constitutes a transdiagnostic marker for a range of compulsive symptoms, their familial loading, and related cognitive markers. Future work should more extensively investigate the scale in normative and clinical cohorts, and the role of value-modulated attentional capture across compulsive disorders.

Background: Although problematic pornography use (PPU) will soon be diagnosable through the International Classification for Diseases, 11th revision, its clinical profile remains contentious. The current study assessed whether PPU may be characterized by various symptoms sometimes observed among online recovery forums that currently lack empirical assessment, such as heightened cognitive-affective issues following pornography use and sexual dysfunction with partners as a result of escalating use., Method: Cross-sectional surveys were completed by male PPUs (N = 138, mean age = 31.75 years, standard deviation = 10.72) recruited via online recovery communities and Amazon Mechanical Turk. Multiple regression analysis was performed using the Problematic Pornography Use Scale as the dependent variable and variables of interest (Arizona Sexual Experiences Scales modified for partnered sex and pornography use, Brunel Mood Scale, Social Interaction Anxiety Scale, and the Tolerance subscale from the Problematic Pornography Consumption Scale) and potential confounders (eg, comorbid psychopathology) as independent variables., Results: Current levels of pornography use, indicators of tolerance and escalation, greater sexual functioning with pornography, and psychological distress were uniquely associated with PPU severity, while cognitive-affective issues after pornography use, impulsivity and compulsivity were not. Although sexual dysfunction did not predict PPU severity, nearly half the sample indicated sexual dysfunction with intimate partners., Conclusions: The present findings suggest that PPU may be characterized by tolerance and escalation (as per substance addiction models), greater sexual responsivity toward pornography, and psychological distress. Meanwhile, the high rate of partnered sexual dysfunction observed suggests that PPU might be somewhat separable from other forms of compulsive sexual behavior.

Objective: We assessed self-reported drives for alcohol use and their impact on clinical features of alcohol use disorder (AUD) patients. Our prediction was that, in contrast to "affectively" (reward or fear) driven drinking, "habitual" drinking would be associated with worse clinical features in relation to alcohol use and higher occurrence of associated psychiatric symptoms., Methods: Fifty-eight Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) alcohol abuse patients were assessed with a comprehensive battery of reward- and fear-based behavioral tendencies. An 18-item self-report instrument (the Habit, Reward and Fear Scale; HRFS) was employed to quantify affective (fear or reward) and non-affective (habitual) motivations for alcohol use. To characterize clinical and demographic measures associated with habit, reward, and fear, we conducted a partial least squares analysis., Results: Habitual alcohol use was significantly associated with the severity of alcohol dependence reflected across a range of domains and with lower number of detoxifications across multiple settings. In contrast, reward-driven alcohol use was associated with a single domain of alcohol dependence, reward-related behavioral tendencies, and lower number of detoxifications., Conclusion: These results seem to be consistent with a shift from goal-directed to habit-driven alcohol use with severity and progression of addiction, complementing preclinical work and informing biological models of addiction. Both reward-related and habit-driven alcohol use were associated with lower number of detoxifications, perhaps stemming from more benign course for the reward-related and lack of treatment engagement for the habit-related alcohol abuse group. Future work should further explore the role of habit in this and other addictive disorders, and in obsessive-compulsive related disorders.