PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of neurologyNeurologyAmerican Academy of Neurology
 
Neurology. Apr 17, 2012; 78(16): 1237–1244.
PMCID: PMC3324319
Network correlates of disease severity in multiple system atrophy
K.L. Poston, MD, MS, C.C. Tang, MD, PhD, T. Eckert, MD, V. Dhawan, PhD, S. Frucht, MD, J.-P. Vonsattel, MD, S. Fahn, MD, and D. Eidelberg, MDcorresponding author
From the Stanford University Medical Center (K.L.P.), Stanford, CA; Center for Neurosciences (C.C.T., T.E., V.D., D.E.), The Feinstein Institute for Medical Research, Manhasset; Movement Disorders Division (S.F.), Mount Sinai School of Medicine, New York; and the Department of Pathology (J.-P.V.) and Neurological Institute (S.F.), Columbia University, New York, NY. T.E. is currently affiliated with the Department of Neurology, University of Magdeburg, Germany.
corresponding authorCorresponding author.
Study funding: Supported by the NIH (NINDS R01 NS 35069 and P50 NS 071675) to D.E. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the NIH.
Correspondence & reprint requests to Dr. Eidelberg: david1/at/nshs.edu
Received July 15, 2011; Accepted December 8, 2011.
Objective:
Multiple system atrophy (MSA), the most common of the atypical parkinsonian disorders, is characterized by the presence of an abnormal spatial covariance pattern in resting state metabolic brain images from patients with this disease. Nonetheless, the potential utility of this pattern as a MSA biomarker is contingent upon its specificity for this disorder and its relationship to clinical disability in individual patients.
Methods:
We used [18F]fluorodeoxyglucose PET to study 33 patients with MSA, 20 age- and severity-matched patients with idiopathic Parkinson disease (PD), and 15 healthy volunteers. For each subject, we computed the expression of the previously characterized metabolic covariance patterns for MSA and PD (termed MSARP and PDRP, respectively) on a prospective single-case basis. The resulting network values for the individual patients were correlated with clinical motor ratings and disease duration.
Results:
In the MSA group, disease-related pattern (MSARP) values were elevated relative to the control and PD groups (p < 0.001 for both comparisons). In this group, MSARP values correlated with clinical ratings of motor disability (r = 0.57, p = 0.0008) and with disease duration (r = −0.376, p = 0.03). By contrast, MSARP expression in the PD group did not differ from control values (p = 1.0). In this group, motor ratings correlated with PDRP (r = 0.60, p = 0.006) but not with MSARP values (p = 0.88).
Conclusions:
MSA is associated with elevated expression of a specific disease-related metabolic pattern. Moreover, differences in the expression of this pattern in patients with MSA correlate with clinical disability. The findings suggest that the MSARP may be a useful biomarker in trials of new therapies for this disorder.
Multiple system atrophy (MSA) is a progressive, adult onset neurodegenerative disorder characterized by autonomic dysfunction with varying degrees of parkinsonism and cerebellar ataxia. Currently, there is no disease-modifying therapy available for MSA and, unlike Parkinson disease (PD), only limited symptomatic treatment. Clinical trials for potential therapies have been hampered by the lack of validated, disease-specific biomarkers for this illness.
To address this need, we examined the previously characterized MSA-related spatial covariance pattern (MSARP) as a potential biomarker of this disease. [18F]fluorodeoxyglucose (FDG) PET scans from a combined group of patients with MSA and healthy subjects were used in conjunction with spatial covariance analysis to identify a reproducible MSARP network.1,2 Indeed, prospectively computed measures of MSARP expression in individual patients were found to discriminate patients with MSA from clinically similar individuals with PD, as well as other parkinsonian syndromes.2,3 In the current study, we assessed the MSARP as a potential disease-specific imaging biomarker by quantifying its expression in FDG-PET scans from a large prospective patient cohort. The specificity of this metabolic network for MSA was evaluated by computing MSARP expression, as well as the expression of the previously validated PD-related spatial covariance pattern (PDRP) in each member of the MSA cohort and in a prospective group of patients with PD of similar age and clinical severity.4 Additionally, network values computed in both patient groups were correlated with independent motor disability ratings obtained concurrently in the same individuals at the time of imaging.
Subjects.
We identified 41 patients with a clinical diagnosis of MSA who were referred for FDG-PET as part of a larger study of metabolic pattern analysis for the differential diagnosis of parkinsonian movement disorders.3 Of these, 33 patients (15 men, 18 women; age 60.6 ± 8.4 years; off-state Unified Parkinson's Disease Rating Scale [UPDRS] motor scores 34.2 ± 13.8) were selected based upon the following inclusion criteria: 1) the treating movement disorders specialist established a diagnosis of MSA based on best clinical diagnosis after at least 6 months of follow-up; 2) the diagnosis of MSA was confirmed by a second movement disorders specialist (K.L.P.) on chart review using published criteria5; 3) UPDRS motor ratings were obtained in a practically defined “off-state” within 1 month of imaging; and (4) there was no evidence of structural brain abnormalities (i.e., mass lesions, white matter changes, ischemia) on routine MRI to explain the clinical findings.
The patients were classified as having either the parkinsonian (MSA-P) or cerebellar (MSA-C) disease subtype based upon the predominant clinical feature that was evident at the time of initial movement disorders evaluation.6 Of the patients enrolled in this study, 26 were classified as MSA-P and 7 as MSA-C. Two of the participants underwent repeat metabolic imaging: one patient (patient 1) was scanned at baseline and again 4 and 7 years later; the other (patient 2) was scanned at baseline and 7 years. In 3 patients, the clinical diagnosis of MSA was confirmed on postmortem assessment by an experienced neuropathologist using established histologic criteria.7
Scan data from the MSA group were compared with those from 20 age- and severity-matched patients with PD (10 men, 10 women; age 60.1 ± 8.7 years; off-state UPDRS motor scores 33.1 ± 13.1). In these subjects, the diagnosis of idiopathic PD was made according to published criteria.8 Scans from 15 healthy volunteer subjects (8 men, 7 women; age 58.8 ± 6.4 years) were used as controls in comparisons of the imaging data from the MSA and PD groups.
PET.
PET imaging with FDG was performed in 3-dimensional mode using a GE Advance tomograph (General Electric; Milwaukee, WI) at North Shore University Hospital, as described in detail elsewhere.9,10 The subjects fasted overnight and antiparkinsonian medication was withheld for at least 12 hours before the start of the imaging session. Images of relative cerebral glucose utilization were acquired in a single 10-minute frame, beginning 35 minutes after the IV injection of a 5 mCi dose of radiotracer. An isotropic spatial smoothing kernel (10 mm full width at half maximum) was applied to the images, which were standardized according to the Montreal Neurological Institute (MNI) template. Image processing was performed using Statistical Parametric Mapping (SPM) software (Wellcome Department of Cognitive Neurology, London, UK) running in MATLAB (Mathworks, Natick, MA).
Data analysis.
We quantified the expression of the MSARP and PDRP metabolic networks (figure 1, A and B) in each of the MSA, PD, and control scans using an automated voxel-based algorithm (software available at http://www.fil.ion.ucl.ac.uk/spm/ext/#SSM) as described elsewhere.3,4,11 These spatial covariance patterns represent disease-related biological signals that can be detected and quantified in metabolic brain images. The expression of a given disease pattern (i.e., the “subject score” for the pattern) can be computed prospectively from an individual's brain scan. The resulting value represents the magnitude of network activity expressed in the metabolic image of that particular subject. All network computations were performed blind to subject, disease group, and clinical disability ratings. MSARP and PDRP subject scores were z transformed with respect to the corresponding healthy control values such that network activity in the normal group had a mean value of zero and a SD of 1.
Figure 1
Figure 1
Multiple system atrophy (MSA)– and Parkinson disease (PD)–related metabolic patterns
In addition to comparing PDRP and MSARP expression across the 3 groups, we measured regional metabolic activity in the lentiform nucleus and cerebellum of each subject using an automated region of interest (ROI) template as described previously.12 These particular regions were selected because of prior data demonstrating consistent reductions in local glucose utilization in these brain regions in metabolic scans from patients with MSA.1315 For each region, glucose metabolism was averaged across hemispheres. To reduce intersubject variability, both regional measures were normalized by the global metabolic rate.
Group differences in whole-brain pattern expression and in lentiform and cerebellar regional metabolic activity were assessed using analysis of variance with post hoc Bonferroni tests. Dichotomous variables (e.g., gender) were evaluated using the χ2 statistic. Multiple regression analysis was used to examine relationships between the network- and region-level metabolic measures (MSARP/PDRP scores; lentiform/cerebellar glucose metabolism) and clinical severity (UPDRS motor ratings). Statistical analyses were implemented in SPSS 17.0 for Windows (SPSS, Chicago, IL). Group differences were considered significant for p < 0.05.
Standard protocol approvals, registrations, and patient consents.
Ethical permission to conduct this retrospective analysis was obtained from the Institutional Review Board of the North Shore–Long Island Jewish Health System.
Group differences.
Clinical measures.
Clinical data from all patients are summarized in table 1. The MSA and PD groups did not differ with respect to age (p = 0.78) or to off-state UPDRS motor ratings (p = 0.97). The patients with MSA were found to have shorter symptom duration than their PD counterparts, with a similar level of motor disability (p = 0.001).16,17 The proportion of male to female participants did not differ across groups (p = 0.87, χ2 test). When MSA-P, MSA-C, and PD were considered as discrete clinical categories, no group differences were observed in subject age at the time of imaging (MSA-P: 61.5 ± 8.9 years, mean ± SD; MSA-C: 57.5 ± 5.7; PD: 60.1 ± 8.7, p = 0.15). Nonetheless, disease duration was shorter in the MSA-C group (MSA-P: 4.2 ± 2.6 years, mean ± SD; MSA-C: 2.9 ± 1.5, p = 0.04), and a trend was noted toward relatively lower (less severe) motor ratings in the MSA-C group (MSA-P: 36.8 ± 14.1, mean ± SD; MSA-C: 23.5 ± 7.3, p = 0.07).
Table 1
Table 1
Demographic features
Network activity.
MSARP subject scores (figure 2A) differed for the 3 groups (F2,65 = 68.48, p < 0.001), with higher pattern expression in the MSA group compared to the PD and normal control groups (p < 0.001, post hoc Bonferroni tests). MSARP values computed in the patients with PD did not differ from the corresponding measures determined in the healthy control subjects (p = 1.0). When the MSA cohort was separated into discrete MSA-P and MSA-C subgroups, MSARP scores were likewise found to differ from PD and normal controls (F2,50 = 53.19, p < 0.001). Pattern expression was elevated in both MSA-P and MSA-C when each subgroup was separately compared to PD and healthy control subjects (p < 0.001, post hoc Bonferroni tests). MSARP expression was marginally elevated in MSA-P relative to MSA-C (p = 0.08).
Figure 2
Figure 2
Pattern expression in multiple system atrophy (MSA) and Parkinson disease (PD)
PDRP scores (figure 2B) also differed across the 3 groups (F2,65 = 57.81, p < 0.001), with higher expression in patients with PD compared to patients with MSA (p < 0.001) and healthy control subjects (p < 0.001). PDRP scores did not differ for the MSA and healthy control groups (p = 0.34). Moreover, PDRP values did not differ across the MSA subgroups (p = 1.0).
Regional metabolism.
At a regional level, metabolic activity in the lentiform nucleus and cerebellum were also found to differ across groups (lentiform: F2,65 = 28.5, p < 0.001; cerebellum: F2,65 = 5.44, p = 0.007, see table e-1 on the Neurology® Web site at www.neurology.org). Regional metabolic activity in the lentiform nucleus was lower in MSA relative to PD and healthy control subjects (p < 0.001, post hoc Bonferroni tests). In the cerebellum, metabolic activity was lower in patients with MSA relative to patients with PD (p = 0.01), but was only marginally reduced with respect to healthy controls (p = 0.09). In both regions, metabolic activity did not differ between patients with PD and healthy control subjects (p = 1.0). A marginal reduction in cerebellar metabolic activity was observed in MSA-C relative to PD (p = 0.06), but no group difference was observed with respect to MSA-P (p = 1.0) and healthy control subjects (p = 0.19).
Clinical–metabolic relationships.
Network-level correlations.
In the MSA group, increased MSARP expression correlated with greater clinical disability (r = 0.57, p = 0.001) as determined by higher UPDRS motor ratings (figure 3A). By contrast, in these patients, more severe clinical disability (i.e., higher UPDRS motor ratings) correlated with lower PDRP expression values (r = −0.53, p = 0.002). An inverse relationship was noted between MSARP and PDRP subject scores (r = −0.44, p = 0.01). Further analysis disclosed that MSARP and PDRP subject scores together accounted for 42% of the variability in motor disability ratings across the subjects with MSA (p = 0.0017, multiple linear regression). Each of the 2 disease patterns contributed to the predictive model (MSARP: p < 0.02; PDRP: p < 0.05) without interaction effect (p = 0.85).
Figure 3
Figure 3
Relationship between pattern expression and motor ratings
In the MSA group, increased MSARP expression correlated (r = 0.38, p = 0.03) with longer symptom duration (figure 4). Longitudinal metabolic data were available from 2 patients with MSA. In patient 1, MSARP scores increased over time (year 2 = 2.91, year 9 = 7.27). PDRP expression values for this subject remained in the normal range, declining minimally (−0.63 units) over time. In patient 2, MSARP expression increased continuously over the 3 time points (year 2 = 0.43, year 6 = 0.91, year 9 = 1.82), while PDRP scores fluctuated within the normal range over the same time period (year 2 = 0.17, year 6 = −0.43, year 9 = 0.76).
Figure 4
Figure 4
Relationship between multiple system atrophy–related spatial covariance pattern (MSARP) expression and symptom duration
In the PD group, increased PDRP expression correlated (r = 0.60, p = 0.006) with greater clinical disability, i.e., higher UPDRS motor ratings (figure 3B). MSARP expression did not correlate with clinical disability in these patients (r = 0.038, p = 0.88). In this group, the correlation between UPDRS motor ratings and PDRP scores was not improved by including MSARP expression values in the regression model (p = 0.99, interaction effect).
Region-level correlations.
In the MSA group, increased UPDRS motor ratings correlated with reduced local metabolic activity in the lentiform nucleus (r = −0.37, p = 0.04) but not in the cerebellum (r = −0.10, p = 0.58). There was no correlation between regional measures and individual differences in symptom duration (lentiform: p = 0.065; cerebellum: p = 0.64).
In this study, we quantified the expression of 2 previously characterized disease-related metabolic patterns, the MSARP and PDRP, in a sizeable cohort of patients with MSA, and compared the resulting network values to those from a group of patients with PD with a similar degree of motor disability. As expected for an MSA biomarker, MSARP expression was elevated in patients diagnosed clinically with this disorder, while PDRP expression was normal in the same subjects. Analogously, the patients with PD were characterized by elevated PDRP expression and normal MSARP scores. The specificity of the group differences seen for the 2 networks, and their distinctive relationships with clinical disability ratings, underscores the “disease-relatedness” of the MSARP and PDRP as functional imaging biomarkers.
Natural history studies in MSA have found that the time from symptom onset to death varies widely between patients with MSA with survival ranging from 3 to 15 years.18 By contrast, functional disability is closely associated with the severity of motor symptoms,16 and standardized rating scales are currently being used to measure disease progression in MSA experimental trials.1921 Given the variability inherent in such clinical descriptors, alternative quantifiers of disease severity/progression may enhance the accuracy and speed of experimental trials of new MSA therapies. In this study, cross-sectional analysis of the MSA cohort indicated that the expression of the corresponding disease-related pattern increased in patients with longer symptom duration and worse motor disability. Similarly, the 2 patients with MSA who underwent longitudinal imaging studies exhibited increasing MSARP expression over time, while PDRP expression fluctuated within the normal range. These data suggest the MSARP is likely to be a disease-specific and clinically sensitive biomarker of MSA progression. Nonetheless, a systematic longitudinal study of early-stage MSA is required prior to adopting this network as an outcome measure in clinical trials.
The subjects with MSA included in this study represent patients who initially presented to a movement disorders specialist with an uncertain parkinsonian diagnosis, including patients with early symptoms. Indeed, 30% of the patients with MSA had symptoms for 2 years or less at the time of imaging. Certainly, objective disease biomarkers are most valuable in patients who are early in the disease course, as this is the target population for inclusion into clinical trials. However, diagnostic accuracy can be challenging in such subjects. Therefore, we took several measures to verify the diagnosis of MSA in this cohort. Clinicopathologic studies suggest the sensitivity for initial clinical diagnosis in MSA is comparatively low (22%–56%).22,23 Nonetheless, after follow-up by a movement disorders specialist, the sensitivity for an MSA diagnosis is as high as 88%–100%.23,24 For this reason, we included only patients who both met diagnostic criteria for MSA23 on the last evaluation after PET and in whom the clinical diagnosis was confirmed by a movement disorders specialist after at least 6 months, with an average of 2.1 ± 0.5 years of follow-up after PET. In addition, in 9% of the patients with MSA included in our cohort, the diagnosis was confirmed by postmortem examination, providing further diagnostic verification.
As previously reported, we found that MSA was associated with significant localized reductions in basal ganglia and cerebellar metabolic activity, which were not present in patients with PD.13,25 However, in contrast to the network-based MSARP measurements, these regional changes exhibited only a weak correlation with clinical motor ratings. While patients with MSA can have clinical parkinsonism without ataxia, and vice versa, data from a recent clinicopathologic study from 100 patients with MSA suggest that basal ganglia and cerebellar regional pathology do not exist in isolation.26 Furthermore, although the putamen and cerebellum are key elements of the MSARP topography, this metabolic network includes significant contributions from other brain areas.1 Thus, this spatial covariance pattern is likely to be more representative of the widespread neurodegenerative changes that underlie this disorder. We note that for our primary analysis, patients with MSA-P and MSA-C were analyzed together as one pathologic disease group. However, when these clinically defined subgroups were considered separately, only the patients with MSA-P exhibited significant reductions in putamen metabolism—and neither subgroup displayed significant metabolic reductions in the cerebellum. Indeed, despite the modest degree of regional change seen in these patients, both MSA subgroups exhibited substantial MSARP elevations compared to the PD and normal control groups. In aggregate, these observations are consistent with the notion that network biomarkers based on whole brain imaging data provide more robust descriptors of neurodegenerative processes than measurements derived from one or more isolated brain regions.4
To evaluate the specificity of the relationships between these metabolic networks and disease severity, we correlated MSARP and PDRP subject scores with corresponding motor disability ratings obtained at the time of imaging. Indeed, the correlations between network expression and motor ratings were observed to be fundamentally different in the MSA and PD patient groups. In the MSA group, we noted a correlation with higher MSARP expression in patients with more severe motor dysfunction. An analogous correlation was evident in the PD group, with greater expression of the relevant disease-related pattern (i.e., PDRP) in the more advanced patients. In keeping with the disease specificity of the MSARP, the expression of this pattern in patients with PD failed to correlate with individual motor ratings.
Interestingly, a significant clinical–network correlation was also observed in the MSA group, in which higher (more severe) motor ratings were associated with lower PDRP values. Of note, PDRP expression was normal in the MSA group and individual differences in the corresponding network values were relatively small. This is consistent with our observations in the 2 MSA patients with longitudinal imaging, in whom PDRP scores fluctuated within the normal range over the 4–7 years of follow-up. By contrast, in the MSA group, concurrently measured MSARP scores were abnormally elevated and increased with worsening motor symptoms. In this vein, the magnitude of the slope of the line correlating clinical ratings with MSARP scores was twice that for the corresponding correlation with declining PDRP values.
Finally, we note that there was no interaction effect between the 2 sets of network values in their prediction of the clinical severity ratings. Thus, the distinct clinical–network correlations observed for the 2 patterns in the MSA cohort are unlikely to represent the divergent effects of disease on basal ganglia glucose metabolism.13,15 This point is supported by an exploratory analysis in which we prospectively measured MSARP and PDRP expression in the individual scan data following the removal of the putamen using an anatomically standardized volume of interest mask.27 We found that the resulting pattern scores in the MSA group continued to correlate with the motor ratings (MSARP: r = 0.42, p = 0.02; PDRP: r = −0.51, p = 0.004). Similarly, a priori removal of the putamen from the PD scans did not substantially alter the clinical–network correlations observed in this group (PDRP: r = 0.60, p = 0.005; MSARP: r = 0.10, p = 0.69). An interesting possibility is that the correlation observed between decreased PDRP expression and increased motor ratings in patients with MSA is attributable to a predominance of nigral over striatal cell loss early in the disease process. While speculative, such an effect is consistent with the transient presence of a dopaminergic treatment response in some patients with early-stage MSA.18
The findings we describe support the proposition that network analysis of rest-state metabolic imaging data can provide robust, useful biomarkers for the diagnosis and assessment of patients with neurodegenerative disorders.3,28 Nonetheless, longitudinal imaging studies in patients with early MSA will be necessary to confirm the relationship between pattern expression and disease progression, and to quantify the annual rate of network change in these subjects.
Supplementary Material
Data Supplement
ACKNOWLEDGMENT
The authors thank Toni Fitzpatrick for copyediting assistance and Noam Gerber for data management.
GLOSSARY
FDG[18F]fluorodeoxyglucose
MNIMontreal Neurological Institute
MSAmultiple system atrophy
MSA-Cmultiple system atrophy cerebellar subtype
MSA-Pmultiple system atrophy parkinsonian subtype
MSARPmultiple system atrophy–related spatial covariance pattern
PDRPParkinson disease–related spatial covariance pattern
PDParkinson disease
ROIregion of interest
SPMStatistical Parametric Mapping
UPDRSUnified Parkinson's Disease Rating Scale

Footnotes
Supplemental data at www.neurology.org
AUTHOR CONTRIBUTIONS
D.E. and C.C.T. designed the research; K.L.P., C.C.T., T.E., and V.D. performed the research; K.L.P. and C.C.T. analyzed the data; K.L.P., C.C.T., and D.E. wrote the manuscript; J.-P.V., S.F., T.E., and V.D. provided critical revision of the manuscript; and V.D., J.-P.V. and S.F. provided administrative, technical, or material support.
DISCLOSURE
Dr. Poston has received research support from Neurologix, Inc., Ceregene, Inc., and the NIH/NINDS. Dr. Tang has received research support from the NIH/NINDS and Neurologix, Inc. Dr. Eckert reports no disclosures. Dr. Dhawan has received research support from the NIH (NIDCD, NIAID, NINDS), Dana Foundation, and Neurologix, Inc. Dr. Frucht has received funding for travel or speaker honoraria from Jazz Pharmaceuticals, Lundbeck, Inc., and Merz Pharmaceuticals, LLC; receives publishing royalties for Movement Disorders Emergencies: Diagnosis and Treatment (Humana Press, 2005); and serves/has served as a consultant for UCB, Jazz Pharmaceuticals, Merz Pharmaceuticals, LLC, GE Healthcare, and Allergan, Inc. Dr. Vonsattel receives research support from the NIH/NIA, The Hereditary Disease Foundation, and The Louis and Rachel Rudin Foundation. Dr. Fahn serves on scientific advisory boards for Intech Pharma Pvt. Ltd., IMPAX Laboratories, Inc., Civitas, RJG Foundation, and Lundbeck, Inc.; serves as Co-editor of Current Neurology and Neurosurgery Report; receives publishing royalties for Principles and Practice of Movement Disorders (Elsevier, 2007); serves as a consultant for Green Cross Corporation of Korea and Genervon Laboratories; and receives research support from the US Department of Defense's Telemedicine and Advanced Technology Research Center (TATRC), the NIH, the Parkinson's Disease Foundation, and the Smart Family Foundation. Dr. Eidelberg serves on scientific advisory boards for and has received honoraria from the Thomas Hartman Foundation for Parkinson's Research, Inc., the Michael J. Fox Foundation, and the Bachmann-Strauss Dystonia and Parkinson Foundation; has served as a consultant for Neurologix, Inc. and Merck & Co., Inc.; serves on the editorial boards of Annals of Neurology and NeuroImage and as an Associate Editor of the Journal of Neuroscience and Molecular Imaging and Biology; is listed as coinventor of patents on the use of imaging markers to screen patients for nervous system dysfunction; and has received research support from the NIH (NINDS, NCRR, NIDCD, NIAID), the Dana Foundation, the Bachmann-Strauss Dystonia and Parkinson Foundation, and the CHDI Foundation.
1. Eckert T, Tang C, Ma Y, et al. Abnormal metabolic networks in atypical parkinsonism. Mov Disord 2008;23:727–733. [PubMed]
2. Spetsieris PG, Ma Y, Dhawan V, Eidelberg D. Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features. Neuroimage 2009;45:1241–1252. [PubMed]
3. Tang CC, Poston KL, Eckert T, et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol 2010;9:149–158. [PubMed]
4. Eidelberg D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci 2009;32:548–557. [PMC free article] [PubMed]
5. Litvan I, Bhatia KP, Burn DJ, et al. SIC Task Force appraisal of clinical diagnostic criteria for parkinsonian disorders. Mov Disord 2003;18:467–486. [PubMed]
6. Geser F, Wenning GK, Seppi K, et al. Progression of multiple system atrophy (MSA): a prospective natural history study by the European MSA Study Group (EMSA SG). Mov Disord 2006;21:179–186. [PubMed]
7. Papp MI, Lantos PL. The distribution of oligodendroglial inclusions in multiple system atrophy and its relevance to clinical symptomatology. Brain 1994;117:235–243. [PubMed]
8. Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 1992;55:181–184. [PMC free article] [PubMed]
9. Asanuma K, Tang C, Ma Y, et al. Network modulation in the treatment of Parkinson's disease. Brain 2006;129:2667–2678. [PubMed]
10. Ma Y, Tang C, Spetsieris PG, et al. Abnormal metabolic network activity in Parkinson's disease: test-retest reproducibility. J Cereb Blood Flow Metab 2007;27:597–605. [PubMed]
11. Spetsieris PG, Eidelberg D. Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues. Neuroimage 2011;54:2899–2914. [PMC free article] [PubMed]
12. Moeller JR, Nakamura T, Mentis MJ, et al. Reproducibility of regional metabolic covariance patterns: comparison of four populations. J Nucl Med 1999;40:1264–1269. [PubMed]
13. Eidelberg D, Takikawa S, Moeller JR, et al. Striatal hypometabolism distinguishes striatonigral degeneration from Parkinson's disease. Ann Neurol 1993;33:518–527. [PubMed]
14. Antonini A, Kazumata K, Feigin A, et al. Differential diagnosis of parkinsonism with [18F]fluorodeoxyglucose and PET. Mov Disord 1998;13:268–274. [PubMed]
15. Eckert T, Barnes A, Dhawan V, et al. FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage 2005;26:912–921. [PubMed]
16. Tison F, Yekhlef F, Chrysostome V, et al. Parkinsonism in multiple system atrophy: natural history, severity (UPDRS-III), and disability assessment compared with Parkinson's disease. Mov Disord 2002;17:701–709. [PubMed]
17. Seppi K, Yekhlef F, Diem A, et al. Progression of parkinsonism in multiple system atrophy. J Neurol 2005;252:91–96. [PubMed]
18. O'Sullivan SS, Massey LA, Williams DR, et al. Clinical outcomes of progressive supranuclear palsy and multiple system atrophy. Brain 2008;131:1362–1372. [PubMed]
19. Bensimon G, Ludolph A, Agid Y, et al. Riluzole treatment, survival and diagnostic criteria in Parkinson plus disorders: the NNIPPS Study. Brain 2009;132:156–171. [PMC free article] [PubMed]
20. Dodel R, Spottke A, Gerhard A, et al. Minocycline 1-year therapy in multiple-system-atrophy: effect on clinical symptoms and [11C] (R)-PK11195 PET (MEMSA-trial). Mov Disord 2010;25:97–107. [PubMed]
21. May S, Gilman S, Sowell BB, et al. Potential outcome measures and trial design issues for multiple system atrophy. Mov Disord 2007;22:2371–2377. [PubMed]
22. Litvan I, Goetz CG, Jankovic J, et al. What is the accuracy of the clinical diagnosis of multiple system atrophy? A clinicopathologic study. Arch Neurol 1997;54:937–944. [PubMed]
23. Osaki Y, Wenning GK, Daniel SE, et al. Do published criteria improve clinical diagnostic accuracy in multiple system atrophy? Neurology 2002;59:1486–1491. [PubMed]
24. Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 2002;125:861–870. [PubMed]
25. Lyoo CH, Jeong Y, Ryu YH, et al. Effects of disease duration on the clinical features and brain glucose metabolism in patients with mixed type multiple system atrophy. Brain 2008;131:438–446. [PubMed]
26. Ozawa T, Paviour D, Quinn NP, et al. The spectrum of pathological involvement of the striatonigral and olivopontocerebellar systems in multiple system atrophy: clinicopathological correlations. Brain 2004;127:2657–2671. [PubMed]
27. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 2003;19:1233–1239. [PubMed]
28. Huang C, Tang C, Feigin A, et al. Changes in network activity with the progression of Parkinson's disease. Brain 2007;130:1834–1846. [PubMed]
Articles from Neurology are provided here courtesy of
American Academy of Neurology