Combining Transcranial Magnetic Stimulation with Antidepressants: A Systematic Review and Meta-Analysis.
Rakesh, G.; Cordero, P.; Khanal, R.; Himelhoch, S. S.; Rush, C. R.
Show abstract
Major depressive disorder (MDD) imposes significant disability on patients. In addition to antidepressants, brain stimulation modalities such as electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) have been helpful in treatment of MDD. Novel TMS paradigms like theta burst stimulation (TBS) have rapidly become popular due to their effectiveness. Given that both antidepressants and TMS are commonly used together and affect neuroplasticity, we reviewed studies that administered both these as treatments for MDD. Unlike ECT wherein previous trials have shown that continuing pharmacotherapy is useful while giving ECT, there are no consensus guidelines on what to do with antidepressants when starting TMS. So, we reviewed two groups of studies - 1) those that administered TMS and antidepressant pharmacotherapy concurrently and 2) those wherein TMS augmented antidepressants or were an adjunctive intervention to antidepressants. We performed a meta-analysis for randomized clinical trials (RCTs) that administered TMS and antidepressants concurrently. We found ten RCTs fulfilling criteria 1 and compared uniformly titrated antidepressant regimens combined with active versus sham TMS. We also found twenty studies fulfilling criterion 2, that used TMS as an augmenting or adjunctive intervention. Both groups of studies showed TMS combined with antidepressants had greater efficacy for treatment of MDD. We advocate for laboratory studies examining the interaction between TMS and antidepressants in a parametric fashion; in addition to randomized controlled trials that examine this combination to expedite remission in MDD.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Oral Ketamine for the Treatment of Depression: A randomized controlled trial and meta-analysis 95%
- Increased neurotoxicity due to activated immune-inflammatory and nitro-oxidative stress pathways in patients with suicide attempts: a systematic review and meta-analysis. 93%
- Electroconvulsive therapy effects on anhedonia and reward circuitry anatomy: a dimensional structural neuroimaging approach 92%
Similar papers in this journal
- Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data 94%
- Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis 93%
- Meta-analysis of Reward Processing in Major Depressive Disorder Reveals Distinct Abnormalities within the Reward Circuit 93%
Similar papers in this journal
- Unilateral and Bilateral Theta Burst Stimulation for Treatment-Resistant Depression: Follow up on a Naturalistic Observation Study 95%
- Estimating heterogeneity of treatment effect in psychiatric clinical trials 92%
- Bilateral Sequential Theta Burst Stimulation for Multiple-Therapy-ResistantDepression: a naturalistic observation study 91%
Similar papers in this journal
- Evidence for a serotonergic subtype of major depressive disorder: A NeuroPharm-1 study. 93%
- Decoding Treatment Choice: Genetic and Phenotypic Analyses of Long-term Antidepressant Acceptability 93%
- Investigating the potential effect of antihypertensive medication on psychiatric disorders: a mendelian randomisation study 91%
Similar papers in this journal
- Sequential Bilateral Accelerated Theta Burst Stimulation in Adolescents With Suicidal Ideation Associated With Major Depressive Disorder: Protocol for a Randomized Controlled Trial 95%
- Mindfulness-Based Cognitive Therapy for Treatment-Resistant Depression: A protocol for a systematic review and meta-analysis 95%
- Interventions Promoting Recovery from Depression for Patients Transitioning from Outpatient Mental Health Services to Primary Care: Protocol for a Scoping Review 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.