Systematic Review of rTMS and dTMS in Treatment-Resistant Obsessive-Compulsive Disorder: Target- and...

To evaluate the efficacy of transcranial magnetic stimulation (TMS), including repetitive TMS (rTMS) and deep TMS (dTMS), in patients with severe, treatment-resistant obsessive-compulsive disorder (OCD), specifically examining target- and phenotype-specific outcomes as well as predictors of treatment response. A systematic review was conducted in accordance with PRISMA guidelines (PROSPERO: CRD420251003027) searching PubMed and ProQuest (January 2017-April 2024). Methodological quality was appraised using design-specific tools, and certainty of evidence was evaluated using the GRADE framework. Twenty-seven articles representing 23 distinct clinical studies were included (14 high, 12 moderate, and 1 low quality). Active TMS significantly reduced OCD severity on the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), with secondary gains in depressive and anxiety symptoms assessed by Hamilton Depression Rating Scale (HAM-D) and Hamilton Anxiety Scale (HAM-A). Evidence certainty varied substantially by anatomical target: Deep TMS (mPFC/ACC) and prefrontal targets (OFC/DLPFC) demonstrated moderate certainty, supported by multiple randomized controlled trials (RCTs). Pre-supplementary motor area (pre-SMA) showed low-to-moderate certainty due to RCT outcome heterogeneity and genetic moderation (SLC6A4 genotype). Supplementary motor area (SMA) evidence was limited to uncontrolled open-label, while VMPFC and PPC targets exhibited very low certainty. Key predictors of enhanced therapeutic response included higher baseline OCD severity (Y-BOCS[Formula: see text]28), older age, lower functional disability, and baseline amygdala hyper-activation during symptom provocation. TMS represents a promising neuromodulation approach for treatment-resistant OCD, with the most robust evidence supporting dTMS and prefrontal targets. Tailoring protocol parameters and stratifying patients based on clinical, genetic, and neurophysiological predictors are critical to optimizing individualized outcomes.