SUMMARY Chronic pain is one of the most prevalent diseases and the leading cause of disability worldwide. Given its high inter-patient variability to response to treatment, ineffectiveness of available pain medication to completely relieve chronic pain, and unequal availability of pain treatments for underrepresented populations, it remains a burdensome, difficult to treat condition. In this context, neuroplasticity-targeted interventions (NTIs) are being investigated as a potential solution for the current issues with chronic pain. They are a group of diversified therapies that modulate neuroplasticity of pain-related pathways through bottom-up or top-down approaches. Although significant effects of electricity-based techniques such as transcranial direct current stimulation (tDCS) have been evidenced for pain reduction in different chronic pain conditions, there is high inter-trial variability regarding these results with different combined NTIs. Therefore, given the hard-to-treat nature of chronic pain and the unknown optimal combination of NTIs, this proposed project suggests a multiphase optimization strategy to assess the direct and indirect comparative effects of NTIs (and their components) in chronic pain, and to develop a treatment hierarchy using living Bayesian network meta-analyses (Aim 1). These meta-analyses will provide the framework for the development of supervised machine learning models to predict analgesic response to NTIs in chronic pain (Aim 2). Aim 2 will be carried out with the use of secondary, clinical, and neurophysiological data from 14 clinical trials with 669 patients to implement multilevel modeling considering different chronic pain predictors shared among different chronic pain conditions and types of interventions. Both evidence bodies (network meta-analyses and predictive models) will be deployed in an open access web-based platform, which will facilitate the constant update (monthly) and data sharing to a broad user population. My expertise and background in meta-research, data science, neuromodulation, and chronic pain research will allow me to fulfill this project’s aims with no foreseeable obstacles. Moreover, I will be working with collaborators from Spaulding Neuromodulation Center and University of Sao Paulo that have been significantly involved in studies conveying the promising effects of NTIs for the reduction of chronic pain. Therefore, the R03 award will provide preliminary data for further funding application and promote the precision pain management approach via data sharing and fusion approaches.