Pragmatic Trial of Remote tDCS and Somatosensory Training for Phantom Limb Pain with Machine Learning to Predict Treatment Response

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT: Phantom limb pain (PLP) is considered an extremely hard-to-treat disorder, given that traditional treatments are not effective in targeting the maladaptive neuronal circuits associated with chronic pain. Transcranial direct current stimulation (tDCS) is a non-invasive, safe brain stimulation technique that has been shown to revert maladaptive plasticity as well as reduce pain in neuropathic pain and other pain syndromes. Our previous R01 on this topic has demonstrated the efficacy of tDCS combined with somatosensory training in a controlled setting to improve pain and that this intervention changes PLP associated cortical plasticity. Our previous R01 also shown pain phenotypes based on PLP characteristics that are more responsive to this treatment. The objective of this renewal is to provide novel data to address critical knowledge gaps such as (i) testing a portable device that would reach underrepresented populations; (ii) validation of this therapy in a more pragmatic setting; (iii) confirmation and testing of predictors of response with statistical and machine learning techniques; and (iv) testing the parasympathetic tone changes (with the remote assessment) as a biomarker of neuropathic pain relief. The central hypothesis is that a combination of home-based tDCS and somatosensory therapy will reduce pain in PLP patients. Our long-term goal is to develop a cheap, efficacious, safe, and practical treatment for PLP. Our rationale is that understanding the effects of tDCS in a real-life setting will validate this treatment for PLP and identify predictors of response to this treatment will help health professionals better target and more precisely treat individuals with this condition. Our specific aims will test the following hypotheses: (Aim 1) tDCS combined with somatosensory therapy will be associated with a significantly larger effect size (of at least 1) compared to the control condition in pain reduction; (Aim 2) identifying predictors of response of this combined treatment using machine learning algorithms will help identify different pain phenotypes in patients with PLP and improve their target treatment; (Aim 3) combined treatment will bolster the parasympathetic tone (as indexed by higher heart rate variability) and reduce sympathetic activation, changes which will be correlated with PLP decreases. This contribution is significant because, although several studies have tested the efficacy of tDCS for chronic pain, there is a need to evaluate its effectiveness in a real-world setting, and this proposal provides critical data to develop a safe and unique intervention to be applied at home, which can therefore increase its access to underrepresented populations and decrease therapeutic costs. This investigation will also provide mechanistic data on predictors of response and changes in parasympathetic tone associated with this intervention. The proposed research is innovative because it offers a pragmatic trial design for PLP treatment and aims to validate a home-based tDCS device that is feasible and able to provide longer treatments remotely. This proposal is also investigating HRV as a surrogate marker for reducing PLP and assessing its feasibility in a real-life setting. Finally, this proposal validates predictors of response to PLP treatment using machine learning algorithms.