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Electroencephalographia as Predictor of Effectiveness HD-tDCS in Neuropathic Pain: Machine Learning Approach

NCT04852536 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 30

Last updated 2021-04-21

No results posted yet for this study

Summary

Contextualization: Neuropathic pain is a complication present in the clinical picture of patients with traumatic Brachial Plexus injury (BPI). It is characterized by high intensity, severity and refractoriness to clinical treatments, resulting in high disability and loss of quality of life. Due to loss of afferent entry, it causes cortical and subcortical alterations and changes in somatotopic representation, from inadequate plastic adaptations in the Central and Peripheral Nervous System, one of the therapies with potential benefit in this population is the Transcranial High Definition Continuous Current Stimulation (HD-tDCS). Thus, by using connectivity-based response prediction and machine learning, it will allow greater assurance of efficiency and optimization of the application of this therapy, being directed to patients with greater potential to benefit from the application of this approach. Objective: Using connectivity-based prediction and machine learning, this study aims to assess whether baseline EEG related characteristics predict the response of patients with neuropathic pain after BPI to the effectiveness of HD-tDCS treatment. Materials and methods: A quantitative, applied, exploratory, open-label response prediction study will be conducted from data acquired from a pilot, triple-blind, cross-over, placebo-controlled, randomized clinical trial investigating the efficacy of applying HD-tDCS to patients with neuropathic brachial plexus trauma pain. Participants will be evaluated for eligibility and then randomly allocated into two groups to receive the active HD-tDCS or simulated HD-tDCS. The primary outcome will be pain intensity as measured by the numerical pain scale. Participants will be invited to participate in an EEG study before starting treatment. Clinical improvement labels used for machine learning classification will be determined based on data obtained from the clinical trial (baseline and post-treatment evaluations). The hypothesis adopted in this study is that the response prediction model constructed from EEG frequency band pattern data collected at baseline will be able to identify responders and non-responders to HD-tDCS treatment.

Conditions

Interventions

DEVICE

Neurostimulation (High Definition - transcranial Direct Current Stimulation) HD-tDCS

5 consecutive sessions lasting 20 minutes of HD-tDCS4x1, based on previous publications (VILLAMAR et al., 2013). A list will be provided current of 2 mA, placing a central electrode (anode) on the M1 contralateral to the painful limb and the four return electrodes within a radius of 7.5 cm around, corresponding approximately to Cz, F3, T7 and P3 if the stimulation is on the side left, and Cz, F4, T8 and P4 if it is on the right, according to the International 10/20 System.

Sponsors & Collaborators

  • Federal University of Paraíba

    lead OTHER

Principal Investigators

  • Suellen Andrade · Federal University of Paraiba

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-06-15
Primary Completion
2021-12-15
Completion
2022-06-15

Countries

  • Brazil

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04852536 on ClinicalTrials.gov