Response prediction for repetitive transcranial magnetic stimulation treatment.

Abstract

While rTMS is a safe therapeutic option, its efficacy remains to be improved. Patients with treatment-resistant depression show 50-60% response rates and 30-40% remission rates to standard 10 Hz rTMS protocols. Response prediction is a promising option to improve rTMS efficacy.Most studies test response prediction in patients with depression, schizophrenia, and OCD. Clinical data and structural MRI are primarily used for patient stratification, fMRI is employed to determine the optimal localization, and EEG is utilized for fine-tuning rTMS parameters to achieve the best efficacy. Employing magnetic resonance spectroscopy, PET, and measuring cortical excitability may also be helpful. However, only a few studies tested these methods. Furthermore, a crucial new task is to connect theta-burst accelerated protocols with response prediction, an approach applied in some recent studies.We propose planning and carrying out multicentre studies to confirm existing results and provide a definitive conclusion for clinicians. Primarily, individual alpha peak (IAPF)-based response prediction results should be replicated in large-sample, multicentre trials, as this approach is the most robust and has the best chance of being implemented in clinical practice. Structural MRI-based patient stratification and fMRI-guided stimulation are possible add-ons.