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Zhang C, Zhang X, Li W, Zhang T, Zhang Z, Lu L, Didonna F, Fan Q. Pallidum volume as a predictor for the effectiveness of mindfulness-based cognitive therapy and psycho-education in unmedicated patients with obsessive-compulsive disorder. Compr Psychiatry 2024; 131:152462. [PMID: 38354586 DOI: 10.1016/j.comppsych.2024.152462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/03/2024] [Accepted: 02/09/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Mindfulness-based cognitive therapy (MBCT) has been documented to be effective in treating obsessive-compulsive disorder (OCD). However, the neurobiological basis of MBCT remains largely elusive, which makes it clinically challenging to predict which patients are more likely to respond poorly. Hence, identifying biomarkers for predicting treatment outcomes holds both scientific and clinical values. This prognostic study aims to investigate whether pre-treatment brain morphological metrics can predict the effectiveness of MBCT, compared with psycho-education (PE) as an active placebo, among patients with OCD. METHODS A total of 32 patients with OCD were included in this prognostic study. They received magnetic resonance imaging (MRI) brain scans before treatment. Subsequently, 16 patients received 10 weeks of MBCT, while the other 16 patients underwent a 10-week PE program. The effectiveness of the treatments was primarily assessed by the reduction rate of the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) total score before and after the treatment. We investigated whether several predefined OCD-associated brain morphological metrics, selected based on prior published studies by the ENIGMA Consortium, could predict the treatment effectiveness. RESULTS Both the MBCT and PE groups exhibited substantial reductions in Y-BOCS scores over 10 weeks of treatment, with the MBCT group showing a larger reduction. Notably, the pallidum total volume was associated with treatment effectiveness, irrespective of the intervention group. Specifically, a linear regression model utilizing the pre-treatment pallidum volume to predict the treatment effectiveness suggested that a one-cubic-centimeter increase in pallidum volume corresponded to a 22.3% decrease in the Y-BOCS total score reduction rate. CONCLUSIONS Pallidum volume may serve as a promising predictor for the effectiveness of MBCT and PE, and perhaps, other treatments with the shared mechanisms by MBCT and PE, among patients with OCD.
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Affiliation(s)
- Chen Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaochen Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenqing Li
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Tianran Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Child Development and Education, University of Amsterdam, Amsterdam, the Netherlands
| | - Zongfeng Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Psychiatry, Ningbo Kangning Hospital & Affiliated Mental Health Centre, Ningbo University, Ningbo, Zhejiang, China
| | - Lu Lu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Clinical Psychology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Qing Fan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China; Mental Health Branch, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.
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De la Peña-Arteaga V, Cano M, Porta-Casteràs D, Vicent-Gil M, Miquel-Giner N, Martínez-Zalacaín I, Mar-Barrutia L, López-Solà M, Andrews-Hanna JR, Soriano-Mas C, Alonso P, Serra-Blasco M, López-Solà C, Cardoner N. Mindfulness-based cognitive therapy neurobiology in treatment-resistant obsessive-compulsive disorder: A domain-related resting-state networks approach. Eur Neuropsychopharmacol 2024; 82:72-81. [PMID: 38503084 DOI: 10.1016/j.euroneuro.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/15/2024] [Accepted: 02/17/2024] [Indexed: 03/21/2024]
Abstract
Mindfulness-based cognitive therapy (MBCT) stands out as a promising augmentation psychological therapy for patients with obsessive-compulsive disorder (OCD). To identify potential predictive and response biomarkers, this study examines the relationship between clinical domains and resting-state network connectivity in OCD patients undergoing a 3-month MBCT programme. Twelve OCD patients underwent two resting-state functional magnetic resonance imaging sessions at baseline and after the MBCT programme. We assessed four clinical domains: positive affect, negative affect, anxiety sensitivity, and rumination. Independent component analysis characterised resting-state networks (RSNs), and multiple regression analyses evaluated brain-clinical associations. At baseline, distinct network connectivity patterns were found for each clinical domain: parietal-subcortical, lateral prefrontal, medial prefrontal, and frontal-occipital. Predictive and response biomarkers revealed significant brain-clinical associations within two main RSNs: the ventral default mode network (vDMN) and the frontostriatal network (FSN). Key brain nodes -the precuneus and the frontopolar cortex- were identified within these networks. MBCT may modulate vDMN and FSN connectivity in OCD patients, possibly reducing symptoms across clinical domains. Each clinical domain had a unique baseline brain connectivity pattern, suggesting potential symptom-based biomarkers. Using these RSNs as predictors could enable personalised treatments and the identification of patients who would benefit most from MBCT.
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Affiliation(s)
| | - Marta Cano
- Sant Pau Mental Health Research Group, Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain; Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Daniel Porta-Casteràs
- Sant Pau Mental Health Research Group, Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain; Mental Health Department, Unitat de Neurociència Traslacional, Parc Taulí University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Sabadell, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine Bellaterra, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Muriel Vicent-Gil
- Sant Pau Mental Health Research Group, Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain; Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Neus Miquel-Giner
- Mental Health Department, Unitat de Neurociència Traslacional, Parc Taulí University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Sabadell, Spain; Department of Mental Health, Parc Sanitari Sant Joan de Déu, Cornellà de Llobregat, Spain
| | - Ignacio Martínez-Zalacaín
- Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain; Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Lorea Mar-Barrutia
- Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Marina López-Solà
- Department of Medicine, School of Medicine and Health Sciences, Universitat de Barcelona - UB, Barcelona, Spain
| | - Jessica R Andrews-Hanna
- Department of Psychology - Cognitive Science, University of Arizona, Tucson, United States of America
| | - Carles Soriano-Mas
- Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain; Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona - UB, Barcelona, Spain
| | - Pino Alonso
- Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, School of Medicine, Universitat de Barcelona - UB, L'Hospitalet de Llobregat, Spain
| | - Maria Serra-Blasco
- Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; ICOnnecta't e-Health Program of the Institut Català d'Oncologia (ICO), L'Hospitalet de Llobregat, Spain; Psycho-oncology and Digital Health Group, Health Services Research in Cancer, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet del Llobregat, Spain.
| | - Clara López-Solà
- Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Mental Health Department, Unitat de Neurociència Traslacional, Parc Taulí University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Sabadell, Spain; Health Clinical Psychology Section, Department of Psychiatry & Clinical Psychology, Institut Clínic de Neurociències (ICN), Hospital Clínic, Barcelona, Spain.
| | - Narcís Cardoner
- Sant Pau Mental Health Research Group, Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain; Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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