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Using brain scans to distinguish movement disorders

This study combines assessments and scans to develop computer methods for identifying Parkinson's and related conditions.

Learning by observing or collecting information · Study reference: DRKS00034050

Plain-language introduction written with AI from the registry; not independently checked by a clinician. Read the original details below ↓

This registry record was last updated over six months ago, or its update date is missing. Recruitment may have changed. Confirm with the team before making plans.

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Looking for volunteers

Start (reported date)
2020-01-20

Planned dates can move. A study finishing does not tell us when a paper will be published.

No results summary has been confirmed in the registry records we imported. See connected papers below; we keep checking after recruitment ends.

Changes we have recorded
  • 2026-10-11 — recruiting

These are dates we observed a change, not necessarily the dates it happened.

Papers connected to this study

No connected paper has been found yet. The tracker checks the growing library for study identifiers and registry-linked publications.

Who can join?

Age 18 years and over · Ask the team who can join

These are starting points, not the full rules. The research team can tell you whether the study is right for your situation.

Read all the rules for taking part

Sex eligibility reported by registry: all

Inclusion criteria: Neurologically diagnosed idiopathic Parkinson's disease, an atypical parkinsonian syndrome, or diagnosed Wilson disease. Healthy subjects, matched for age and gender to atypical parkinsonian disease and Wilson disease patients. Exclusion criteria: MRI contraindication (e.g. metal implants, claustrophobia), other neuropsychiatric diseases (e.g. dementia etc.), pregnancy and breastfeeding.
Full study name & original research details

Official study title

Machine-learning based prediction models for Parkinson’s disease and atypical Parkinsonian disorders from advanced neuroimaging - HCPark

Short title used by the registry

Machine-learning based prediction models for Parkinson’s disease and atypical Parkinsonian disorders from advanced neuroimaging

Original description

Intervention: Group 1: Idiopathic Parkinson`s disease: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 2: Progressive supranuclear palsy: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 3: Multiple system atrophy: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 4: Corticobasal syndrome: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 5: Wilson disease: Neurological Examination Neuropsychological Examination 55 minutes MRI Primary outcome: Improving the diagnosis of patients with various movement disorders Secondary outcome: Are different neuroimaging methods (e.g. resting-state fMRI; DTI) capable of differentiating between examined diseases?

Conditions reported: Idiopathic Parkinson`s disease; Progressive supranuclear palsy; Multiple system atrophy; Corticobasal syndrome; Wilson disease

Registry records for this study

Records are joined using registration identifiers. Titles alone do not establish that two studies are the same.

Study type
Observational
Interventions
Group 1: Idiopathic Parkinson`s disease: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 2: Progressive supranuclear palsy: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 3: Multiple system atrophy: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 4: Corticobasal syndrome: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 5: Wilson disease: Neurological Examination Neuropsychological Examination 55 minutes MRI
Phases
Not reported
Sponsor
Heinrich-Heine-Universität und Universitätsklinikum Düsseldorf
Start date reported by registry
2020-01-20 (reported)

Registry updated: Not reported · Status last verified by the registry submitter: Not reported

Registry records retrieved 2026-10-14 (UTC). Individual records may have older updates. Recruitment and eligibility must be confirmed with the study team.

Contact the research team

Public study contacts supplied to the registry. Ask whether recruitment is still open and what participation involves.

Julian Caspers · +4902118100 · julian.caspers@med.uni-duesseldorf.de

Study locations

Site status can differ from overall study status. “Status not reported” means local availability needs confirmation. Remote participation and travel arrangements must be checked with the team.

Facility not reported

Germany

Status not reported

The original descriptions and participation rules come from the registry. Participation is voluntary and does not guarantee benefit.