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Related Experiment Videos

Structural Imaging in Parkinson's Disease: New Developments.

Stéphane Prange1,2, Elise Metereau1,2, Stéphane Thobois3,4,5

  • 1Institut des Sciences Cognitives Marc Jeannerod, Université de Lyon, CNRS, UMR 5229, F-69675, Bron, France.

Current Neurology and Neuroscience Reports
|June 20, 2019
PubMed
Summary

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This summary is machine-generated.

Advanced structural imaging techniques, including high-field MRI, aid in diagnosing Parkinson's disease (PD), predicting its progression, and assessing treatment outcomes by detecting in vivo pathological markers like iron and neuromelanin changes.

Area of Science:

  • Neuroimaging
  • Neurology
  • Medical Diagnostics

Background:

  • Parkinson's disease (PD) diagnosis relies on clinical symptoms, often appearing late.
  • Objective biomarkers are needed for early and accurate PD detection and monitoring.
  • Structural imaging offers potential for non-invasive assessment of PD-related neurodegeneration.

Purpose of the Study:

  • To review recent advancements in structural neuroimaging for Parkinson's disease.
  • To highlight imaging's role in PD diagnosis, prognosis, and treatment prediction.
  • To discuss novel MRI sequences and their application in PD research.

Main Methods:

  • Review of high-field MRI (≥3 Tesla) techniques.
  • Analysis of MR sequences sensitive to iron and nigral pigments.
Keywords:
MRIMachine learningNeuromelaninParkinson’s diseaseProdromal diagnosis

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  • Evaluation of imaging markers for diagnosis, prognosis, and treatment response.

    • Main Results:

    • High-field MRI detects in vivo pathological surrogates for PD diagnosis (e.g., altered nigral neuromelanin, iron, diffusivity, free-water).
    • Imaging can identify prodromal PD (e.g., locus coeruleus neuromelanin changes) and track progression (e.g., nigral iron, free-water).
    • Atrophy in specific nuclei (e.g., cholinergic basal forebrain) correlates with prognosis and cognitive impairment.

    Conclusions:

    • Multimodal structural imaging enhances PD diagnosis, prognosis, and prediction of treatment outcomes.
    • Future research may leverage machine learning and longitudinal studies for improved PD subtyping.
    • Advanced MRI techniques provide valuable insights into PD pathophysiology and clinical management.