Ignite Creation Date:
2025-12-24 @ 12:09 PM
Ignite Modification Date:
2026-01-01 @ 8:33 AM
Study NCT ID:
NCT04819061
Status:
UNKNOWN
Last Update Posted:
2021-03-26
First Post:
2021-03-06
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predicting Outcomes From tDCS Intervention in Parkinson' Disease Using Electroencephalographic Biomarkers and Machine Learning Approach: the PREDICT Study Protocol
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010300', 'term': 'Parkinson Disease'}], 'ancestors': [{'id': 'D020734', 'term': 'Parkinsonian Disorders'}, {'id': 'D001480', 'term': 'Basal Ganglia Diseases'}, {'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D009069', 'term': 'Movement Disorders'}, {'id': 'D000080874', 'term': 'Synucleinopathies'}, {'id': 'D019636', 'term': 'Neurodegenerative Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'TRIPLE', 'whoMasked': ['PARTICIPANT', 'CARE_PROVIDER', 'INVESTIGATOR']}, 'primaryPurpose': 'OTHER', 'interventionModel': 'PARALLEL', 'interventionModelDescription': '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.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 56}}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2021-06-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-03', 'completionDateStruct': {'date': '2021-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2021-03-25', 'studyFirstSubmitDate': '2021-03-06', 'studyFirstSubmitQcDate': '2021-03-25', 'lastUpdatePostDateStruct': {'date': '2021-03-26', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-03-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2021-06-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Functional Mobility measured using the Timed Up and Go test (Podsiadlo D, Richardson S, 1991)', 'timeFrame': '4 weeks', 'description': 'The functional mobility will be measured using the Timed Up and Go test to stand up from a chair at the command: "Walk 3 meters, walk along a demarcated course, turn around and walk back to the chair, then sit down".'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Parkinson Disease', 'Electroencephalogram', 'Transcranial Direct Current Stimulation']}, 'descriptionModule': {'briefSummary': '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.\n\nThe 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '70 Years', 'minimumAge': '40 Years', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Diagnosis of idiopathic Parkinson's disease by a neurologist based on Parkinson's Disease Society Brain Bank (PDSBB) criteria (Hughes et al.,1992)\n* Disease staging between 1.5 and 3, according to the modified Hoehn and Yahr scale (Hoehn and Yahr, 1967)\n* 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\n* Score of more than 24 points on the Mini-Mental State Examination (Folstein et al., 1975)\n\nExclusion Criteria:\n\n* Associated neurological, musculoskeletal and/or cardiorespiratory diseases that could compromise gait;\n* alcohol or substance abuse disorders;\n* Deep brain stimulation implant;\n* History of brain trauma or neurological disease that would interfere with study procedures."}, 'identificationModule': {'nctId': 'NCT04819061', 'acronym': 'PREDICT', 'briefTitle': "Predicting Outcomes From tDCS Intervention in Parkinson' Disease Using Electroencephalographic Biomarkers and Machine Learning Approach: the PREDICT Study Protocol", 'organization': {'class': 'OTHER', 'fullName': 'Federal University of Paraíba'}, 'officialTitle': "Predicting Outcomes From tDCS Intervention in Parkinson' Disease Using Electroencephalographic Biomarkers and Machine Learning Approach: the PREDICT Study Protocol", 'orgStudyIdInfo': {'id': 'EEGtDCS'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'Active group', 'description': 'In the group G1 will be administered: tDCS active + dual-task motor training', 'interventionNames': ['Other: tDCS Active']}, {'type': 'SHAM_COMPARATOR', 'label': 'Sham group', 'description': 'In the group G2 will be administered: tDCS sham + dual-task motor training', 'interventionNames': ['Other: tDCS sham']}], 'interventions': [{'name': 'tDCS Active', 'type': 'OTHER', 'description': '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.', 'armGroupLabels': ['Active group']}, {'name': 'tDCS sham', 'type': 'OTHER', 'description': '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.\n\nWe 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.', 'armGroupLabels': ['Sham group']}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Suellen Andrade', 'role': 'CONTACT', 'email': 'suellenandrade@gmail.com', 'phone': '986046032', 'phoneExt': '5583'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Federal University of Paraíba', 'class': 'OTHER'}, 'collaborators': [{'name': 'Universidade Federal do Rio Grande do Norte', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator and Professor', 'investigatorFullName': 'Suellen Marinho Andrade', 'investigatorAffiliation': 'Federal University of Paraíba'}}}}