Compulsion is one of core symptoms of obsessive–compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients. We then applied a computational lesion version of CPM to examine the importance of specific brain areas. We also compared the predictive network strength in OCD with unaffected first-degree relatives (UFDR) of patients and healthy controls. CPM successfully predicted individual level of compulsion and identified networks positively (primarily subcortical areas of the striatum and limbic regions of the hippocampus) and negatively (primarily frontoparietal regions) correlated with compulsion. The prediction power of the negative model significantly decreased when simulating lesions to the prefrontal cortex and cerebellum, supporting the importance of these regions for compulsion prediction. We found a similar pattern of network strength in the negative predictive network for OCD patients and their UFDR, demonstrating the potential of CPM to identify vulnerability markers for psychopathology.

Compulsive behaviors in obsessive–compulsive disorder (OCD) have been suggested to result from an imbalance in cortico-striatal connectivity. However, the nature of this impairment, the relative involvement of different striatal areas, their imbalance in genetically related but unimpaired individuals, and their relationship with cognitive dysfunction in OCD patients, remain unknown. In the current study, striatal (i.e., caudate and putamen) whole-brain connectivity was computed in a sample of OCD patients (OCD, n = 62), unaffected first-degree relatives (UFDR, n = 53) and healthy controls (HC, n = 73) by ROI-based resting-state functional magnetic resonance imaging (rs-fMRI). A behavioral task switch paradigm outside of the scanner was also performed to measure cognitive flexibility in OCD patients. There were significantly increased strengths (Z-transformed Pearson correlation coefficient) in caudate connectivity in OCD patients. A significant correlation between the two types of connectivity strengths in the relevant regions was observed only in the OCD patient group. Furthermore, the caudate connectivity of patients was negatively associated with their task-switch performance. The imbalance between the caudate and putamen connectivity, arising from the abnormal increase of caudate activity, may serve as a clinical characteristic for 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.

The sudden outbreak of COVID-19 had a great impact on the physical and mental health of people all over the world, especially for students whose physical and mental development was not yet mature. In order to understand the physical and mental conditions of students during the epidemic period and provide a theoretical basis for coping with psychological problems in public health emergencies, this study explored the mediating role of sleep disorders in the effect of the psychological stress response (PSR) on non-suicidal self-injury (NSSI), along with the moderating role of emotional management ability (EMA). The SRQ-20, Pittsburgh Sleep Quality Index, NSSI Behavior Questionnaire, and Emotional Management Questionnaire were used to investigate the mental health of Chinese students in April 10–20 (Time point 1, T1) and May 20–30 (Time point 2, T2), 2020. A total of 1,955 students (Mage = 19.64 years, 51.4% male) were examined at T1 and 342 students (Mage = 20.06 years, 48.2% male) were reassessed at T2. Overall, the detection rate of PSR and NSSI were 17.60% (n = 344) and 24.90% (n = 486) respectively in the T1 sample, and were 16.37% (n = 56) and 25.44% (n = 87), in the T2 sample. We also found that sleep disorders played a mediating role in the effect of PSR on NSSI in the T1 and T2 samples. In addition, EMA was shown to regulate the effect of PSR on sleep disorders and the effect of sleep disorders on NSSI in the T1 samples. We found that PSR resulting from public health emergency might lead to NSSI behaviors in individuals. PSR may also cause sleep disorders, which can bring about NSSI. However, these effects were also moderated by the EMA. This research expands our understanding of PSR and NSSI in students during the pandemic.

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.