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Robbins TW, Banca P, Belin D. From compulsivity to compulsion: the neural basis of compulsive disorders. Nat Rev Neurosci 2024; 25:313-333. [PMID: 38594324 DOI: 10.1038/s41583-024-00807-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/04/2024] [Indexed: 04/11/2024]
Abstract
Compulsive behaviour, an apparently irrational perseveration in often maladaptive acts, is a potential transdiagnostic symptom of several neuropsychiatric disorders, including obsessive-compulsive disorder and addiction, and may reflect the severe manifestation of a dimensional trait termed compulsivity. In this Review, we examine the psychological basis of compulsions and compulsivity and their underlying neural circuitry using evidence from human neuroimaging and animal models. Several main elements of this circuitry are identified, focused on fronto-striatal systems implicated in goal-directed behaviour and habits. These systems include the orbitofrontal, prefrontal, anterior cingulate and insular cortices and their connections with the basal ganglia as well as sensoriomotor and parietal cortices and cerebellum. We also consider the implications for future classification of impulsive-compulsive disorders and their treatment.
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Affiliation(s)
- Trevor W Robbins
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Cambridge, UK.
| | - Paula Banca
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Cambridge, UK
| | - David Belin
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Cambridge, UK
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Xu C, Hou G, He T, Ruan Z, Guo X, Chen J, Wei Z, Seger CA, Chen Q, Peng Z. Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits. Psychol Med 2024; 54:710-720. [PMID: 37642202 DOI: 10.1017/s0033291723002386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
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.
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Affiliation(s)
- Chuanyong Xu
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - Tingxin He
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Zhongqiang Ruan
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Xinrong Guo
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Jierong Chen
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zhen Wei
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Carol A Seger
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
- Department of Psychology, Colorado State University, Fort Collins, Colorado, USA
| | - Qi Chen
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Ziwen Peng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
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Ruan Z, Seger CA, Yang Q, Kim D, Lee SW, Chen Q, Peng Z. Impairment of arbitration between model-based and model-free reinforcement learning in obsessive-compulsive disorder. Front Psychiatry 2023; 14:1162800. [PMID: 37304449 PMCID: PMC10250695 DOI: 10.3389/fpsyt.2023.1162800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/05/2023] [Indexed: 06/13/2023] Open
Abstract
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.
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Affiliation(s)
- Zhongqiang Ruan
- Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Carol A. Seger
- Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
- Department of Psychology, Colorado State University, Fort Collins, CO, United States
| | - Qiong Yang
- Affective Disorder Center, Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Dongjae Kim
- Department of AI-based Convergence, College of Engineering, Dankook University, Yongin, Republic of Korea
| | - Sang Wan Lee
- Department of Bio and Brain Engineering, Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Qi Chen
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Ziwen Peng
- Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen University School of Medicine, Shenzhen, China
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Wu X, Yang Q, Xu C, Huo H, Seger CA, Peng Z, Chen Q. Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder. Cereb Cortex 2023; 33:1412-1425. [PMID: 35443038 DOI: 10.1093/cercor/bhac145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Xiangshu Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Qiong Yang
- Affiliated Brain Hospital of Guangzhou Medical University, 510370 Guangzhou, China
| | - Chuanyong Xu
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518047, China
| | - Hangfeng Huo
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Carol A Seger
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.,Department of Psychology, Colorado State University, Fort Collins, CO 80523, United States
| | - Ziwen Peng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.,Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen University School of Medicine, Shenzhen 518061, China
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
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