Prediction of rTMS Efficacy in Patients With Essential Tremor: Biomarkers From Individual Resting-State EEG Network

Abstract

The pathogenesis of essential tremor (ET) remains unclear, and the efficacy of related drug treatment is inadequate for proper tremor control. Hence, in the current study, consecutive low-frequency repetitive transcranial magnetic stimulation (rTMS) modulation on cerebellum was accomplished in a population of ET patients, along with pre- and post-treatment resting-state electroencephalogram (EEG) networks being constructed. The results primarily clarified the decreasing of resting-state network interactions occurring in ET, especially the weaker frontal-parietal connectivity, compared to healthy individuals. While after the rTMS stimulation, promotions in both network connectivity and properties, as well as clinical scales, were identified. Furthermore, significant correlations between network characteristics and clinical scale scores enabled the development of predictive models for assessing rTMS intervention efficacy. Using a multivariable linear model, clinical scales after one-month rTMS treatment were accurately predicted, underscoring the potential of brain networks in evaluating rTMS effectiveness for ET. The findings consistently demonstrated that repetitive low-frequency rTMS neuromodulation on cerebellum can significantly improve the manifestations of ET, and individual networks will be reliable tools for evaluating the rTMS efficacy, thereby guiding personalized treatment strategies for ET patients.

Keywords

• Essential tremor
• multivariable linear predicting model
• neuromodulation
• repetitive transcranial magnetic stimulation
• resting-state network