Yilong Ma

Parkinson's disease tremor-related metabolic network: Characterization, progression, and treatment effects

The circuit changes that mediate parkinsonian tremor, while likely differing from those underlying akinesia and rigidity, are not precisely known. In this study, to identify a specific metabolic brain network associated with this disease manifestation, we used FDG PET to scan nine tremor dominant Parkinsons disease (PD) patients at baseline and during ventral intermediate (Vim) thalamic nucleus deep brain stimulation (DBS). Ordinal trends canonical variates analysis (OrT/CVA) was performed on the within-subject scan data to detect a significant spatial covariance pattern with consistent changes in subject expression during stimulation-mediated tremor suppression. The metabolic pattern was characterized by covarying increases in the activity of the cerebellum/dentate nucleus and primary motor cortex, and, to a less degree, the caudate/putamen. Vim stimulation resulted in consistent reductions in pattern expression (p<0.005, permutation test). In the absence of stimulation, pattern expression values (subject scores) correlated significantly (r=0.85, p<0.02) with concurrent accelerometric measurements of tremor amplitude. To validate this spatial covariance pattern as an objective network biomarker of PD tremor, we prospectively quantified its expression on an individual subject basis in independent PD populations. The resulting subject scores for this PD tremor-related pattern (PDTP) were found to exhibit: (1) excellent test-retest reproducibility (p<0.0001); (2) significant correlation with independent clinical ratings of tremor (r=0.54, p<0.001) but not akinesia-rigidity; and (3) significant elevations (p<0.02) in tremor dominant relative to atremulous PD patients. Following validation, we assessed the natural history of PDTP expression in early stage patients scanned longitudinally with FDG PET over a 4-year interval. Significant increases in PDTP expression (p<0.01) were evident in this cohort over time; rate of progression, however, was slower than for the PD-related akinesia/rigidity pattern (PDRP). We also determined whether PDTP expression is modulated by interventions specifically directed at parkinsonian tremor. While Vim DBS was associated with changes in PDTP (p<0.001) but not PDRP expression, subthalamic nucleus (STN) DBS reduced the activity of both networks (p<0.05). PDTP expression was suppressed more by Vim than by STN stimulation (p<0.05). These findings suggest that parkinsonian tremor is mediated by a distinct metabolic network involving primarily cerebello-thalamo-cortical pathways. Indeed, effective treatment of this symptom is associated with significant reduction in PDTP expression. Quantification of treatment-mediated changes in both PDTP and PDRP scores can provide an objective means of evaluating the differential effects of novel antiparkinsonian interventions on the different motor features of the disorder.

Parkinson's disease spatial covariance pattern: noninvasive quantification with perfusion MRI.

Parkinsons disease (PD) is associated with elevated expression of a specific disease-related spatial covariance pattern (PDRP) in radiotracer scans of cerebral blood flow and metabolism. In this study, we scanned nine early-stage patients with PD and nine healthy controls using continuous arterial spin labeling (CASL) perfusion magnetic resonance imaging (pMRI). Parkinsons disease-related metabolic pattern expression in CASL pMRI scans was compared with the corresponding 18F-fluorodeoxyglucose positron emission tomography values. The PDRP expression was abnormally elevated (P<0.01) in patients scanned with either modality, and the two values were highly intercorrelated (P<0.0001). Perfusion MRI methods can be used for accurate quantification of disease-related covariance patterns.

Comparative Analysis of Striatal FDOPA Uptake in Parkinson's Disease

UNLABELLEDnStriatal-to-occipital ratio (SOR) and influx constant K(i)(occ) are commonly used as analytic parameters in L-3,4-dihydroxy-6-(18)F-fluorophenylalanine (FDOPA) PET studies. Both have been shown to be useful in discriminating Parkinsons disease (PD) patients from healthy subjects. We evaluated the relative performance of SOR and influx constant (K(i)(occ)) in the clinical assessment of nigrostriatal dopaminergic function in PD.nnnMETHODSnTwenty-one parkinsonian patients (Hoehn and Yahr scale I-IV; mean age +/- SD, 56 +/- 9.2 y) and 11 healthy subjects (mean age, 60 +/- 16 y) underwent 3-dimensional dynamic FDOPA scanning from 0 to 100 min. After spatial realignment, PET images at each frame were integrated by summing 4 central striatal slices, and time-activity curves (TACs) were generated after placing a standard set of elliptic regions of interest over striatal and occipital structures. SOR and K(i)(occ) values for each subject were then computed from TACs at different times using an input function from the occipital cortex.nnnRESULTSnBoth SOR and K(i)(occ) showed significant bilateral decreases in striatal dopamine uptake in the PD group compared with the control group. SOR values estimated for 10-min frames between 65 and 95 min are statistically equivalent in group discrimination. In addition, SOR values in the caudate and putamen correlated strongly with K(i)(occ), especially toward the end of the scanning epoch. Both parameters correlated significantly and comparably with Unified Parkinsons Disease Rating Scale motor scores.nnnCONCLUSIONnThese results suggest that SOR determined from a single 10-min scan at 95 min is as accurate as K(i)(occ) in separating PD patients from healthy subjects and in predicting clinical measures of disease severity.

Validation of a Dynamic PET Simulator in Clinical Brain Studies

This work has been undertaken to evaluate the accuracy of 3-D dynamic simulations in neurological imaging protocols with positron emission tomography (PET). We used [18F] uorodopa PET images from a pair of normal brain and Parkinsonian brain. Spatially correlated MR images were segmented into several tissue types and anatomical structures. Voxels within every structure were assigned with the time activity curves (TACs) derived from clinical studies after correcting for partial volume e ects. Both noisefree and noisy projection data of this brain model were created and reconstructed as in the real scans. TACs were then generated from the dynamic images and compared with the measured data. The results show good agreement between the simulated and observed TACs in the normal brain. However the match is poorer in the Parkinsons brain particularly in striatal structures. This suggests a possible mismatch between the simulated true activity distribution and that in the diseased brain. Both normal and patient data have root-mean-square errors of 2% in cortical gray matter and <10% in striatum without and with noise. This tool can be used to optimize temporal sampling strategy and parameter estimation algorithms. 2