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Updated: Sep 15, 2025

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
Published on: August 2, 2021
Polygenic scores of subcortical brain volumes as possible modulators of treatment response in depression
Vincenzo Oliva1,2,3,4, Alfonso Martone1, Giuseppe Fanelli1,5
1Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
Abstract:
A significant proportion of patients with major depressive disorder (MDD) do not experience remission after one or more pharmacological treatments. Research has explored brain structural measures, particularly hippocampal volume, as potential predictors of treatment response, as well as genetic factors. This study investigated the association of polygenic scores (PGSs) for seven subcortical brain volumes (including the hippocampus, nucleus accumbens, amygdala, and caudate nucleus) with treatment non-response and non-remission in MDD. Patients with MDD were recruited in the context of five clinical studies, including a total of 3637 individuals. PGSs were estimated using a Bayesian framework and continuous shrinkage priors (PRS-CS-auto) after standard genotype quality control and imputation. Logistic regressions were performed between PGSs and non-response or non-remission in each sample, adjusting for age, sex, baseline symptom severity, recruitment sites, and population stratification. Results were meta-analysed across samples, using a random-effect model. No association was significant in the meta-analysis after Bonferroni correction. The top finding was found for the caudate volume PGS and non-remission (OR = 1.09, 95% CI = 1.01-1.19, p = 0.036), with no evidence of heterogeneity. Leave-one-out sensitivity analyses showed that this result was influenced by the two largest samples in the meta-analysis. This result should be considered as preliminary as it did not reach the Bonferroni-adjusted significance threshold. Future studies with greater statistical power may enhance the predictive performance of PGSs and contribute to the identification of polygenic predictors of treatment outcomes in MDD, contributing to precision psychiatry.
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