Clinical trial NCT04819061: Predicting Outcomes From tDCS Intervention in Parkinson' Disease Using Electroencephalographic Biomarkers and Machine Learning Approach: the PREDICT Study Protocol
Official title: Predicting Outcomes From tDCS Intervention in Parkinson' Disease Using Electroencephalographic Biomarkers and Machine Learning Approach: the PREDICT Study Protocol
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Parkinson Disease
Electroencephalogram
Transcranial Direct Current Stimulation
Lead sponsor: Federal University of Paraíba
Sponsor class: OTHER
Collaborators: name: Universidade Federal do Rio Grande do Norte
First posted: 2021-03-26
Enrollment: 56
Sex: ALL
Minimum age (years): 40
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: This is a sham-controlled, double-blind randomized multicentric clinical trial that will analyze patients with a confirmed diagnosis of Parkinson disease who were subjected to tDCS associated with dual-task motor training. Whe aim to predict response to tDCS treatment using electroencephalographic biomarkers and machine learning approach.
Primary purpose: OTHER

Brief 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.


Eligibility criteria: Inclusion Criteria:

* Diagnosis of idiopathic Parkinson's disease by a neurologist based on Parkinson's Disease Society Brain Bank (PDSBB) criteria (Hughes et al.,1992)
* Disease staging between 1.5 and 3, according to the modified Hoehn and Yahr scale (Hoehn and Yahr, 1967)
* Regular pharmacological treatment with levodopa (equivalent dose \> 300mg) or taking antiparkinsonian medication such as anticholinergics, selegiline, dopamine agonists (amantadine) and COMT (catechol-O-methyl transferase) inhibitors
* Score of more than 24 points on the Mini-Mental State Examination (Folstein et al., 1975)

Exclusion Criteria:

* Associated neurological, musculoskeletal and/or cardiorespiratory diseases that could compromise gait;
* alcohol or substance abuse disorders;
* Deep brain stimulation implant;
* History of brain trauma or neurological disease that would interfere with study procedures.

Primary outcomes:
measure: Functional Mobility measured using the Timed Up and Go test (Podsiadlo D, Richardson S, 1991)
timeFrame: 4 weeks
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
References: