NCT04819061

Predicting Outcomes From tDCS Intervention in Parkinson' Disease Using Electroencephalographic Biomarkers and Machine Learning Approach: the PREDICT Study Protocol

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 56 (estimated)
• Sponsor: Federal University of Paraíba · Academic / Other
• Sex: All
• Age: 40 Years – 70 Years
• Healthy volunteers: Not accepted

Summary

Parkinson's disease (PD) is a progressive and disabling neurodegenerative disease, clinically characterized by motor and non-motor symptoms. The potential of the "Transcranial direct current stimulation" (tDCS) for symptomatic improvement in these patients has been demonstrated, but the factors associated with the best therapeutic response are not known. The electroencephalogram (EEG) is considered as a diagnostic and prognostic biomarker of PD, and has been used in recent studies associated with machine-learning methods to identify predictors of responses in neurological and psychiatric conditions. Using connectivity-based prediction and machine-learning, the investigators intend to identify and compare characteristics related to baseline resting EEG between PD responders and non-responders to tDCS treatment. The recruited participants will be randomized to treatment with active tDCS associated with dual-task motor therapy or motor therapy with visual cues. A resting-state electroencephalography (EEG) will be recorded prior to the start of the treatment. The investigators will determine clinical improvement labels used for machine learning classification, in baseline and posttreatment assessments and will use three different methods to categorize the data into two classes (low or high improvement): Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Extreme Learning Machine (ELM). The functional label will be based on the Timed Up and Go Test recorded at baseline and posttreament of tDCS treatment.

Conditions

• Parkinson Disease
• Electroencephalogram
• Transcranial Direct Current Stimulation

Interventions

Type	Name	Description
OTHER	tDCS Active	This group will undergo the motor training and active tDCS. Will be performed 12 sessions in three sessions per week for 30 minutes. Participants will undergo an electroencephalogram before starting the clinical trial. The duration between this baseline EEG and entry into the clinical trial that will assess the effectiveness of tDCS will be two weeks. We will determine the clinical improvement labels used for machine learning classification based on data obtained during the clinical trial (baseline and post-treatment assessments), according to procedures conducted in similar studies.
OTHER	tDCS sham	This group will undergo the motor training and tDCS sham. Will be performed 12 sessions in three sessions per week for 30 minutes. Participants will undergo an electroencephalogram before starting the clinical trial. The duration between this baseline EEG and entry into the clinical trial that will assess the effectiveness of tDCS will be two weeks. We will determine the clinical improvement labels used for machine learning classification based on data obtained during the clinical trial (baseline and post-treatment assessments), according to procedures conducted in similar studies.

Timeline

• Start date: 2021-06-01
• Primary completion: 2021-06-01
• Completion: 2021-12-31
• First posted: 2021-03-26
• Last updated: 2021-03-26