The metabolic topography of parkinsonism.
Academic Article
Overview
abstract
We used [18F]fluorodeoxyglucose/positron emission tomography (18F-FDG/PET) and a statistical model of regional covariation to study brain topographic organization in parkinsonism. We studied 22 patients with Parkinson's disease (PD), 20 age-matched normal volunteers, and 10 age- and severity-matched patients with presumed striatonigral degeneration (SND). We used FDG/PET to calculate global, regional, and normalized metabolic rates for glucose (GMR, rCMRglc, rCMRglc/GMR). Metabolic parameters in the three groups were compared using an analysis of variance, with a correction for multiple comparisons, and discriminant analysis. The scaled subprofile model (SSM) was applied to the combined rCMRglc dataset to identify topographic covariance profiles that distinguish PD patients from SND patients and normals. GMR, rCMRglc, and rCMRglc/GMR were normal in PD; caudate and lentiform rCMRglc/GMR was reduced in the SND group (p < 0.01). SSM analysis of the combined group of patients and normals revealed a significant topographic profile characterized by increased metabolic activity in the lentiform nucleus and thalamus associated with decreased activity in the lateral frontal, paracentral, inferior parietal, and parietooccipital areas. Individual subject scores for this profile were significantly elevated in PD patients compared with normals and SND patients (p < 0.001) and discriminated the three groups. In the PD group, subject scores for this factor correlated with individual subject Hoehn and Yahr (H & Y) scores (p < 0.02), and with quantitative rigidity (p < 0.01) and bradykinesia (p < 0.03) ratings, but not with tremor ratings. SSM analysis of right-left metabolic asymmetries yielded a topographic contrast profile that accurately discriminated mildly affected PD patients (H & Y Stage I) from normals. Our findings demonstrate that abnormal topographic covariance profiles exist in parkinsonism. These profiles have potential clinical application as neuroimaging markers in parkinsonism.
