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

NCT ID: NCT04852536

Last Updated: 2021-04-21

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 30 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-06-15
• Study Completion Date: 2022-06-15

Brief 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.

Detailed Description

Using connectivity-based prediction and machine learning, the objective is to assess whether characteristics related to baseline EEG predict the response of patients with neuropathic pain after BPI to the effectiveness of HD-tDCS treatment. An observational, retrospective cohort study will be carried out, of predictive response with a quantitative approach, of an applied nature, of an exploratory and open-label type, related to the efficacy of HD-tDCS4x1 in patients with neuropathic pain due to BPI, from an analysis of data obtained from a pilot, placebo-controlled, triple-blind, randomized, crossover type clinical trial, in accordance with the CONSORT guidelines, which will investigate the effectiveness of treatment with HD-tDCS.

Conditions

Traumatic Brachial Plexus Lesion; Chronic Pain

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

HD-tDCS4x1

All data will be acquired from patients of the triple-blind clinical trial that will investigate the effectiveness of treatment for neuropathic pain after brachial plexus injury with HD-tDCS. There will be collection and analysis of EEG data before the clinical trial protocol, to later assess the prediction of response to the technique employed. At the end, they will be grouped into responders and non-responders to HD-tDCS, according to the numerical scale of pain, with assignments serving as targets for the analyzes with machine learning. The labels for clinical improvement used to classify machine learning will be determined based on the data obtained in the baseline and post-treatment assessments, according to similar studies. Thus, the EEG data of these patients will be retrospectively examined, identifying possible neurophysiological characteristics and biomarkers related to the frequency bands that allow predicting which patients are most likely to improve with this treatment.

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

Intervention Type: DEVICE

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.

Interventions

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.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

1. Age over 18 years;

2. Moderate to severe pain score according to the Numerical Pain Scale (4-10);

3. Persistent pain and refractory to clinical treatment for at least 3 months;

4. Appropriate pharmacological treatment for pain for at least 1 month before the start of the study;

5. Not presenting contraindications for Non-Invasive Brain Stimulation;

6. Absence of concomitant diseases of the Central Nervous Sistem or Peripheral Nervous Sistem.

Exclusion Criteria

1. Failure to sign the informed consent form;

2. Missing two consecutive or three alternate sessions during treatment;

3. Developing a disabling condition that prevents further participation in the study

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Federal University of Paraíba

OTHER

Sponsor Role: lead

Responsible Party

Suellen Marinho Andrade

Principal Investigator and Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Suellen Andrade

Role: PRINCIPAL_INVESTIGATOR

Federal University of Paraiba

Locations

Federal University of Paraíba,Department of Psychology

João Pessoa, Paraíba, Brazil

Countries

Brazil

Central Contacts

Suellen Andrade

Role: CONTACT

suellenandrade@gmail.com

+55 83 987772488

Carolina Carvalho

Role: CONTACT

carolinadiasdecarvalho@gmail.com

+55 83 999843614

Other Identifiers

EEGPain/hd-tDCS

Identifier Type: -

Identifier Source: org_study_id