GRAY MATTER CHANGES ASSOCIATED WITH CLINICAL SYMPTOM IMPROVEMENT FOLLOWING PHARMACOLOGICAL THERAPY IN PATIENTS WITH OBSESSIVE-COMPULSIVE DISORDER: A SOURCE-BASED MORPHOMETRY STUDY
Authors: Jung *Wi Hoon , Kim Euitae 
Published: February 2025

Abstract

Background
Selective serotonin reuptake inhibitors (SSRI) are commonly used to treat depression, anxiety, and obsessive-compulsive disorder (OCD). Investigations have demonstrated that SSRI treatment is associated with structural and functional changes in cortico-striatal regions (Gilbert et al., 2000; Han et al., 2011). However, to date, the effects of SSRI treatment on brain structure, particularly gray matter volume (GMV), and their association with clinical symptoms have been mostly investigated using univariate approaches [e.g., voxel-level analysis on whole-brain GMV maps generated from voxel- based morphometry (VBM)]. Source-based morphometry (SBM) is a multivariate extension of VBM that utilizes spatially independent component analysis (ICA) to obtain patterns of common GMV variation among participants (hereinafter referred to as “GMV covariance network”). Therefore, adoption of a multivariate approach like SBM may be more powerful in investigating the effects of SSRI on brain structures and their association with clinical symptoms (Xu et al., 2009; Kaš pá rek et al., 2010).

Aims & Objectives
Using the SBM method, we investigated whether there were any changes in the GMV covariance network before and after SSRI treatment as well as any group differences at each time. We also investigated whether there was an association between changes in the GMV covariance network and clinical symptom severity [i.e., total Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores] before and after SSRI treatment.

Method
This preliminary study included 15 drug-free patients with OCD and 15 healthy controls matched for sex, age, duration of education. All participants completed clinical assessments and brain scans at baseline and 16-week follow-up. The VBM method with the Computational Anatomy Toolbox (CAT) was performed to extract GMV images. Next, the SBM method was applied to the GMV images of all participants to extract 53 GMV covariance networks based on the minimum description length (MDL) algorithm. Based on the stability index (&gt; 0.95) for ICA estimate- clusters, 16 ICs were selected and used for the subsequent statistical analyses (Fig.1A). For the sixteen ICs, paired and independent T-tests were performed to investigate which networks’ loading coefficients differed significantly over time and between groups, respectively. We then performed correlation analyses to investigate the association between changes in loading coefficient for each network and changes in clinical symptom severity (i.e., total Y-BOCS score) before and after treatment in patients.

Results
We observed a significant improvement in patients’ symptoms after SSRI treatment. We observed a significant group difference only in the loading coefficients of IC 3, composed of cerebellar vermis, at each time (Fig.1B). However, we didn’ t observe significant time effects (baseline versus follow-up) for each group and group by time interaction effects. The change in total Y-BOCS score before and after treatment in patients was positively associated with the change in the loading coefficients of IC 1, composed of thalamus (Fig.1C).

Discussion & Conclusion
The results of this study provide evidence that the use of SSRI leads to improvement in clinical symptoms and changes in brain structures in OCD patients. These results also suggest that patterns of the GMV covariance network may serve as a biomarker to predict the effectiveness of SSRI treatment in patients.

References
1.Gilbert AR, Moore GJ, Keshavan MS, Paulson LA, Narula V, Mac Master FP, Stewart CM, Rosenberg DR. Decrease in thalamic volumes of pediatric patients with obsessive-compulsive disorder who are taking paroxetine. Arch Gen Psychiatry. 2000;57(5):449-456.
2.Han JY, Kang DH, Gu BM, Jung WH, Choi JS, Choi CH, Jang JH, Kwon JS. Altered brain activation in ventral frontal-striatal regions following a 16-week pharmacotherapy in unmedicated obsessive- compulsive disorder. J Korean Med Sci. 2011;26(5):665-674.
3.Xu L, Groth KM, Pearlson G, Schretlen DJ, Calhoun VD. Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Hum Brain Mapp. 2009;30:711-724.
4.Kaspá rek T, Marecek R, Schwarz D, Prikryl R, Vaní cek J, Mikl M, Cesková E. Source-based morphometry of gray matter volume in men with first-episode schizophrenia. Hum Brain Mapp. 2010;31(2):300-310.