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Background: Widely used psychotropic medications for obsessive-compulsive disorder (OCD) may change the volumes of subcortical brain structures, and differently in children vs. adults. We measured subcortical volumes cross-sectionally in patients finely stratified for age taking various common classes of OCD drugs., Methods: The ENIGMA-OCD consortium sample (1081 medicated/1159 unmedicated OCD patients and 2057 healthy controls aged 6-65) was divided into six successive 6-10-year age-groups. Individual structural MRIs were parcellated automatically using FreeSurfer into 8 regions-of-interest (ROIs). ROI volumes were compared between unmedicated and medicated patients and controls, and between patients taking serotonin reuptake inhibitors (SRIs), tricyclics (TCs), antipsychotics (APs), or benzodiazepines (BZs) and unmedicated patients., Results: Compared to unmedicated patients, volumes of accumbens, caudate, and/or putamen were lower in children aged 6-13 and adults aged 50-65 with OCD taking SRIs (Cohen's d = -0.24 to -0.74). Volumes of putamen, pallidum (d = 0.18-0.40), and ventricles (d = 0.31-0.66) were greater in patients aged 20-29 receiving APs. Hippocampal volumes were smaller in patients aged 20 and older taking TCs and/or BZs (d = -0.27 to -1.31)., Conclusions: Results suggest that TCs and BZs could potentially aggravate hippocampal atrophy of normal aging in older adults with OCD, whereas SRIs may reduce striatal volumes in young children and older adults. Similar to patients with psychotic disorders, OCD patients aged 20-29 may experience subcortical nuclear and ventricular hypertrophy in relation to APs. Although cross-sectional, present results suggest that commonly prescribed agents exert macroscopic effects on subcortical nuclei of unknown relation to therapeutic response., Competing Interests: Conflict of Interest Dr. Arnold reported holding the Alberta Innovates Translational Health Chair in Child and Youth Mental Health outside the submitted work. Prof. Mataix-Cols receives royalties for contributing articles to UpToDate, Wolters Kluwer Health and fees from Elsevier for editorial tasks (all unrelated to the submitted work). Dr. Narayanaswamy reported Government of India grants DST INSPIRE faculty grant IFA12-LSBM-26 and BT/06/IYBA/2012 outside the submitted work. Dr. Reddy reported Government of India grants SR/S0/HS/0016/2011 and BT/PR13334/Med/30/259/2009 outside the submitted work. Dr. Venkatasubramanian reported Wellcome-DBT India Alliance grant 500236/Z/11/Z outside the submitted work. Dr. Simpson reported Biohaven Research support for a clinical trial and royalties from UpToDate, Inc. and Cambridge University Press outside the submitted work. Dr. Soreni reported support from Lundbeck-IIT outside the submitted work. Dr. Walitza has received in the last 3 years royalties from Thieme Hogrefe, Kohlhammer, Springer, Beltz; Her work was supported in the last 3 years by the Swiss National Science Foundation (SNF), diff. EU FP7s, HSM Hochspezialisierte Medizin of the Kanton Zurich, Switzerland, Bfarm Germany, ZInEP, Hartmann Müller Stiftung, Olga Mayenfisch, Gertrud Thalmann Fonds (all unrelated to the submitted work). Dr. Thompson has received a research grant from Biogen, Inc., unrelated to the topic of this paper., (Copyright © 2022. Published by Elsevier B.V.)

Background: Recently, machine learning methods have been used to discriminate, on an individual basis, patients from healthy controls through brain structural magnetic resonance imaging (MRI). However, the application of these methods to predict the severity of psychiatric symptoms is less common., Methods: Herein, support vector regression (SVR) was employed to evaluate whether gray matter volumes encompassing cortical-subcortical loops contain discriminative information to predict obsessive-compulsive disorder (OCD) symptom severity in 37 treatment-naïve adult OCD patients., Results: The Pearson correlation coefficient between predicted and observed symptom severity scores was 0.49 (p=0.002) for total Dimensional Yale-Brown Obsessive-Compulsive Scale (DY-BOCS) and 0.44 (p=0.006) for total Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). The regions that contained the most discriminative information were the left medial orbitofrontal cortex and the left putamen for both scales., Limitations: Our sample is relatively small and our results must be replicated with independent and larger samples., Conclusions: These results indicate that machine learning methods such as SVR analysis may identify neurobiological markers to predict OCD symptom severity based on individual structural MRI datasets., (© 2013 Elsevier B.V. All rights reserved.)

Background: Obsessive-compulsive disorder (OCD) is a chronic condition that normally presents high rates of psychiatric comorbidity. Depression, tic disorders and other anxiety disorders are among the most common comorbidities in OCD adult patients. There is evidence that the higher the number of psychiatric comorbidities, the worse the OCD treatment response. However, little is known about the impact of OCD treatment on the outcome of the psychiatric comorbidities usually present in OCD patients. The aim of this study was to investigate the impact of exclusive, conventional treatments for OCD on the outcome of additional psychiatric disorders of OCD patients, detected at baseline., Methods: Seventy-six patients with primary OCD admitted to the treatment protocols of the Obsessive-Compulsive Spectrum Disorders Program between July 2007 and December 2009 were evaluated at pre-treatment and after 12 months. Data were analyzed to verify possible associations between OCD treatment response and the outcome of psychiatric comorbidities., Results: Results showed a significant association between OCD treatment response and improvement of major depression and dysthymia (p-value=0.002), other anxiety disorders (generalized anxiety disorder, social phobia, specific phobia, posttraumatic stress disorder, panic disorder, agoraphobia and anxiety disorder not otherwise specified) (p-value=0.054) and tic disorders (p-value=0.043)., Limitations: This is an open, non-blinded study, without rating scales for comorbid conditions. Further research is necessary focusing on the possible mechanisms by which OCD treatment could improve these specific disorders., Conclusions: Our results suggest that certain comorbid disorders may benefit from OCD-targeted treatment., (Copyright © 2012 Elsevier B.V. All rights reserved.)