Home-based transcranial direct current stimulation (tDCS) and motor imagery for phantom limb pain using statistical learning to predict treatment response: an open-label study protocol

Abstract

Background: Phantom limb pain (PLP) management has been a challenge due to its response heterogeneity and lack of treatment access. This study will evaluate the feasibility of a remotely home-based M1 anodal tDCS combined with motor imagery in phantom limb patients and assess the preliminary efficacy, safety, and predictors of response of this therapy. Methods: This is a pilot, single-arm, open-label trial in which we will recruit 10 subjects with phantom limb pain. The study will include 20 sessions. All participants will receive active anodal M1 tDCS combined with phantom limb motor imagery training. Our primary outcome will be the acceptability and feasibility of this combined intervention. Moreover, we will assess preliminary clinical (pain intensity) and physiological (motor inhibition tasks and heart rate variability) changes after treatment. Finally, we will implement a supervised statistical learning (SL) model to identify predictors of treatment response (to tDCS and phantom limb motor imagery) in PLP patients. We will also use data from our previous clinical trial (total observations=224 [n=112 x timepoints = 2)) for our statistical learning algorithms. The new prospective data from this open-label study will be used as an independent test dataset. Discussion: This protocol proposes to assess the feasibility of a novel, neuromodulatory combined intervention that will allow the design of larger remote clinical trials, thus increasing access to safe and effective treatments for PLP patients. Moreover, this study will allow us to identify possible predictors of pain response and PLP clinical endotypes.

Keywords: phantom limb pain, home-based tDCS, motor imagery, statistical learning, remote trial.