Phoebe Spetsieris

The metabolic topography of parkinsonism.

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 Parkinsons 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.

The Metabolic Topography of Normal Aging

Normal aging is associated with the degeneration of specific neural systems. We used [18F]fluorode-oxyglucose (FDG)/positron emission tomography (PET) and a statistical model of regional covariation to explore the metabolic topography of this process. We calculated global and regional metabolic rates for glucose (GMR and rCMRglc) in two groups of normal subjects studied independently on different tomographs: Group 1—130 normal subjects (62 men and 68 women; range 21–90 years); Group 2—20 normal subjects (10 men and 10 women; range 24–78 years). In each of the two groups, the Scaled Subprofile Model (SSM) was applied to rCMRglc data to identify specific age-related profiles. The validity of these profiles as aging markers was assessed by correlating the associated subject scores with chronological age in both normal populations. SSM analysis disclosed two significant topographic profiles associated with aging. The first topographic profile, extracted in an analysis of group 1 normals, was characterized by relative frontal hypometabolism associated with covariate metabolic increases in the parietooccipital association areas, basal ganglia, mid-brain, and cerebellum. Subject scores for this profile correlated significantly with age in both normal groups (R2 = 0.48 and 0.33, p < 0.0001 for groups 1 and 2, respectively). Because of clinical similarities between normal motoric aging and parkinsonism, we explored the possibility of shared elements in the metabolic topography of both processes. We performed a combined group SSM analysis of the 20 group 2 normals and 22 age-matched Parkinsons disease patients, and identified another aging-related topographic profile. This profile was characterized by relative basal ganglia hypermetabolism associated with covariate decreases in frontal premotor cortex. Subject scores for this profile also correlated significantly with age in both normal groups (group 1: R2 = 0.30, p < 0.00001; group 2: R2 = 0.59, p < 0.01). Healthy aging is associated with reproducible topographic covariation profiles associated with specific neural systems. FDG/PET may provide a useful metabolic marker of the normal agingprocess.

Abnormal Metabolic Network Activity in Parkinson'S Disease: Test—Retest Reproducibility

Parkinsons disease (PD) is associated with an abnormal pattern of regional brain function. The expression of this PD-related covariance pattern (PDRP) has been used to assess disease progression and the response to treatment. In this study, we validated the PDRP network as a measure of parkinsonism by prospectively computing its expression (PDRP scores) in 15O-water (H215O) and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans from PD patients and healthy volunteers. The reliability of this measure was also assessed within subjects using a test—retest design in mildly affected and advanced PD patients scanned at baseline and during treatment with levodopa or deep brain stimulation (DBS). We found that PDRP expression was significantly elevated in PD patients (P<0.001) relative to controls in a prospective analysis of brain scans obtained with either H215O or FDG PET. A significant correlation (R2=0.61; P<0.001) was evident between PDRP scores computed from H215O and FDG images in PD subjects scanned with both tracers. Test—retest reproducibility was very high (intraclass correlation coefficient (ICC)>0.92) for PDRP scores measured both within PET session and between sessions separated by up to 2 months. This high reproducibility was observed in both early stage and advanced PD patients scanned at baseline and during treatment. The within-subject variability of this measure was less than 10% for both unmedicated and treated conditions. These findings suggest that the PDRP network is a reproducible and stable descriptor of regional functional abnormalities in parkinsonism. The quantification of PDRP expression in PD patients can serve as a potential biomarker in PET intervention studies for this disorder.

Microstructural white matter changes in carriers of the DYT1 gene mutation

We tested the hypothesis that the DYT1 genotype is associated with a disorder of anatomical connectivity involving primarily the sensorimotor cortex. We used diffusion tensor magnetic resonance imaging (DTI) to assess the microstructure of white matter pathways in mutation carriers and control subjects. Fractional anisotropy (FA), a measure of axonal integrity and coherence, was reduced (p < 0.005) in the subgyral white matter of the sensorimotor cortex of DYT1 carriers. Abnormal anatomical connectivity of the supplementary motor area may contribute to the susceptibility of DYT1 carriers to develop clinical manifestations of dystonia. Ann Neurol 2004

Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features

In the current paper, we describe methodologies for single subject differential diagnosis of degenerative brain disorders using multivariate principal component analysis (PCA) of functional imaging scans. An automated routine utilizing these methods is applied to positron emission tomography (PET) brain data to distinguish several discrete parkinsonian movement disorders with similar clinical manifestations. Disease specific expressions of voxel-based spatial covariance patterns are predetermined using the Scaled Subprofile Model (SSM/PCA) and a scalar measure of the manifestation of each pattern in prospective subject images is subsequently derived. Scores are automatically compared to reference values generated for each pathological condition in a corresponding set of patient and control scans. Diagnostic outcome is optimized using strategies such as the derivation of patterns in a voxel subspace that reflects contrasting image characteristics between conditions, or by using an independent patient population as controls. The prediction models for two, three and four way classification problems using direct scalar comparison as well as classical discriminant analysis are assessed in a composite training population comprised of three different patient classes and normal controls, and validated in a similar independent test population. Results illustrate that highly accurate diagnosis can often be achieved by simple comparison of scores utilizing optimized patterns.

Network Structure and Function in Parkinson’s Disease

Little is known of the structural and functional properties of abnormal brain networks associated with neurological disorders. We used a social network approach to characterize the properties of the Parkinsons disease (PD) metabolic topography in 4 independent patient samples and in an experimental non-human primate model. The PD network exhibited distinct features. Dense, mutually facilitating functional connections linked the putamen, globus pallidus, and thalamus to form a metabolically active core. The periphery was formed by weaker connections linking less active cortical regions. Notably, the network contained a separate module defined by interconnected, metabolically active nodes in the cerebellum, pons, frontal cortex, and limbic regions. Exaggeration of the small-world property was a consistent feature of disease networks in parkinsonian humans and in the non-human primate model; this abnormality was only partly corrected by dopaminergic treatment. The findings point to disease-related alterations in network structure and function as the basis for faulty information processing in this disorder.

Comparative Analysis of Striatal FDOPA Uptake in Parkinson's Disease

UNLABELLED Striatal-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. METHODS Twenty-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. RESULTS Both 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. CONCLUSION These 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.

A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia

The heterogeneity of behavioral variant frontotemporal dementia (bvFTD) calls for multivariate imaging biomarkers.