Ontology highlight
ABSTRACT: Background
The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects.Objectives
We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD.Methods
Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms.Results
Both classifiers discriminated HC from PD (p?2?=?0.24, p?2?=?0.23, p?2?=?0.25, p?2?=?0.16, p?ConclusionsTwo independent analyses performed in a cohort of mild PD patients replicated key features of the PDRP, confirming that FDG-PET and multivariate classification can provide an objective, sensitive biomarker of disease stage with the potential to detect treatment effects on PD progression.
SUBMITTER: Matthews DC
PROVIDER: S-EPMC6120603 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
NeuroImage. Clinical 20180810
<h4>Background</h4>The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects.<h4>Objectives</h4>We sought to independently evaluate the potential utility of ...[more]