Back

Modelling and Preliminary Clinical Validation of Home-based Menstrual Neuromodulation Therapy

Radyte, E.; Stankeviciute, L.; Bernatavicius, E.; Cook, A.; Rodrigues, Y. T.; Lima de Alves Silva, T. C.; Albuquerque Barbosa Cabral Micussi, M. T.; Pegado, R.

2024-02-04 obstetrics and gynecology
10.1101/2024.02.02.24302224 medRxiv
Show abstract

Primary dysmenorrhea (PD), characterised by chronic pelvic pain during menstruation, significantly impairs the quality of life for many women. This paper presents a modelling and clinical validation study of the novel Nettle device for at-home transcranial direct current stimulation (tDCS) for alleviating PD symptoms. Specifically, we aimed to investigate the electric field patterns induced by Nettle and its immediate efficacy in reducing menstrual pain and improving functionality. Finite element method (FEM) simulations, using realistic head models, assessed the electric field distribution, targeting key brain regions involved in pain processing. A single-centre triple-blinded, sham-controlled study involving 34 women was conducted to compare the effects of active and sham tDCS. Results demonstrated a clinically meaningful decline in menstrual pain symptoms in the active group, with medium effect sizes for both pain reduction (Cohens d=0.53) and functionality (Cohens d=0.47), based on Nettles protocol focused on the electric field within the medial prefrontal cortex. Limitations include the use of generalised brain models and small sample size, highlighting the need for further research with comprehensive modelling and larger clinical trials to validate and understand the effects of Nettle as a menstrual neuromodulation therapy. Clinical RelevanceThis study underscores Nettles potential as a non-invasive, cost-efficient intervention for PD, with implications for broader applications in womens health.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.