FDG PET in the Evaluation of Parkinson's Disease

Abstract

Network analysis of (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is an innovative approach for the study of in movement disorders, such as Parkinson's disease (PD). Spatial covariance analysis of imaging data acquired from PD patients has revealed characteristic regional patterns associated with the motor and cognitive features of disease. Quantification of pattern expression in individual patients can be used for diagnosis, assessment of disease severity, and evaluation of novel medical and surgical therapies. Identification of disease-specific patterns in other parkinsonian syndromes, such as multiple system atrophy and progressive supranuclear palsy, has improved diagnostic accuracy in patients with difficult to diagnose parkinsonism. Further developments of these techniques are likely to enhance the role of functional imaging in investigating underlying abnormalities and potential new therapies in these neurodegenerative diseases.

Figure 1. Parkinson’s Disease-Related Spatial Covariance Patterns

Left: Parkinson’s disease motor-related spatial covariance pattern (PDRP; [20]) characterized by pallidothalamic, pontine, and motor cortical hypermetabolism, associated with relative metabolic reductions in the lateral premotor and posterior parietal areas. Right: Parkinson’s disease cognition-related spatial covariance pattern (PDCP; [28]) characterized by hypometabolism of prefrontal cortex, rostral supplementary motor area, and superior parietal regions. [Relative metabolic increases are displayed in red; relative metabolic decreases are displayed in blue. Both patterns were overlaid on a standard MRI brain template. The left hemisphere was cut in the transverse plane at z=−5 mm. The right hemisphere was displayed as a surface projection on the same brain template.] [Hirano S, Asanuma K, Ma Y, Tang C, Feigin A, Dhawan V, Carbon M, Eidelberg D. Dissociation of metabolic and neurovascular responses to levodopa in the treatment of Parkinson’s disease. J Neurosci 2008;28:4203, Reprinted with permission from The Society for Neuroscience, Copyright © 2008]

Figure 2. Spatial Covariance Patterns Associated with Atypical Parkinsonism

A. Metabolic pattern associated with multiple system atrophy (MSARP) characterized by covarying metabolic decreases in the putamen and the cerebellum. B. Metabolic pattern associated with progressive supranuclear palsy (PSPRP) characterized by covarying metabolic decreases in the medial prefrontal cortex (PFC), the frontal eye fields, the ventrolateral prefrontal cortex (VLPFC), the caudate nuclei, the medial thalamus, and the upper brainstem. [The covariance patterns were overlaid on T1-weighted MR-template images. The displays represent regions that contributed significantly to the network and that were demonstrated to be reliable by bootstrap resampling. Voxels with negative region weights (metabolic decreases) are color-coded blue.] [Eckert T, Tang C, Ma Y, Brown N, Lin T, Frucht S, Feigin A, Eidelberg D. Abnormal metabolic networks in atypical parkinsonism. Movement Disorders, 2008; 23(5):730,731, Reprinted with permission from John Wiley & Sons, Inc., Copyright © 2008 Movement Disorders Society]

Figure 3. Longitudinal Progression of PD-Related Pattern Expression

Mean network activity at baseline, 24, and 48 months. Values for the PD-motor and cognitive spatial covariance patterns (PDRP and PDCP; see Fig. 1) were computed at each time point and displayed relative to the mean for 15 age-matched healthy subjects. Network activity increased significantly over time for both patterns (p<0.001; RMANOVA), with the PDRP progressing faster than the PDCP (p<0.04). Relative to controls, PDRP activity in the patient group was elevated at all three time points, while PDCP activity reached abnormal levels only at the final time point. [Huang C, Tang C, Feigin A, Lesser M, Ma Y, Pourfar M, Dhawan V, Eidelberg D. Changes in network activity with the progression of Parkinson’s disease. Brain, 2007; 130(Pt 7):1842, Reprinted with permission from Oxford University Press]

Figure 4. Changes in Regional Metabolism Following Gene Therapy

A. Voxel based analysis of changes in regional metabolic activity following unilateral STN AAV-GAD gene therapy for advanced PD. Following unilateral gene therapy, a significant reduction in metabolism (top) was found in the operated thalamus, involving the ventrolateral and mediodorsal nuclei. The analysis also revealed a significant metabolic increase (bottom) following surgery in the ipsilateral primary motor region (BA 4), which extended into the adjacent lateral premotor cortex (PMC; BA 6). [Representative axial T1-weighted MRI with merged FDG PET slices; the operated (OP) side is signified on the left. Metabolic increases following surgery are displayed using a red-yellow scale. Metabolic declines are displayed using a blue-purple scale. The displays were thresholded at p<0.05, corrected for multiple comparisons]. B. Postoperative changes in PDRP activity controlling for the effect of disease progression. These progression-corrected values (PDRP_c scores) reflect the net effect of STN AAV-GAD on network expression for each subject and timepoint (see text). Relative to baseline, PDRP_c scores declined following gene therapy (p<0.001, RMANOVA), with significant reductions relative to baseline at both 6 (gray) and 12 (black) months. These changes correlated (p<0.03) with clinical outcome over the course of the study. [**p<0.005, Bonferroni tests; Bars represent standard error]. C. Changes in mean PDCP network activity over time for the operated (filled circles) and the unoperated (open circles) hemispheres. Following gene therapy, there was no change in PDCP activity over time in either of the two hemispheres (p=0.72). [The dashed line represents one standard error above the normal mean value of zero]. [Feigin A, Kaplitt MG, Tang C, Lin T, Mattis P, Dhawan V, During MJ, Eidelberg D. Modulation of metabolic brain networks following subthalamic gene therapy for Parkinson’s disease. Proceedings of the National Academy of Sciences USA, 2007; 104(49):19560, 19561, Reprinted with permission © 2007 by The National Academy of Sciences of the USA]