Network correlates of disease severity in multiple system atrophy

Poston, Kathleen L.; Tang, Chris C.; Eckert, Thomas; Dhawan, Vijay; Frucht, Steven J.; Vonsattel, Jean Paul; Fahn, Stanley; Eidelberg, David

doi:10.1212/wnl.0b013e318250d7fd

Cited by 52 publications

(42 citation statements)

References 28 publications

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“…[34][35][36] Indeed, we found that reductions in putamen DAT binding were similar for MSA-P and LBD patients; correlations of this measure with disease duration and UPDRS motor ratings were of borderline significance in the two patient groups. That said, in keeping with findings from an earlier North American MSA cohort, 7 MSARP expression in the current MSA-P sample correlated significantly with concurrent UPDRS motor ratings. While a significant correlation (p < 0.001) was observed between putamen DAT binding and PDRP expression in LBD, an analogous dopaminergic relationship with MSARP values was not present in the MSA-P cohort.…”

Section: Discussionsupporting

confidence: 88%

“…Thus, as previously observed, patients with LBD (PDND, PDD, and DLB) exhibited increased PDRP expression, 5,8,25,26 whereas those with MSA and PSP had characteristic elevations in the corresponding disease-related patterns. 7,10,11 In contrast to the relatively symmetrical network topographies associated with PD, MSA, and PSP, CBD is characterized by highly specific hemispheric asymmetries. 6 As previously noted, 6 hemispheric asymmetries in CBDRP expression discriminated patients with CBS from those with PSP and other forms of parkinsonism.…”

Section: Discussionmentioning

confidence: 99%

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Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations

Lee

Eidelberg

2016

J Cereb Blood Flow Metab

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Little is known of the precise relationship between the expression of disease-related metabolic patterns and nigrostriatal dopaminergic dysfunction in parkinsonism. We studied 51 subjects with Parkinson's disease (PD) (18 non-demented, 24 demented, and 9 dementia with Lewy bodies) and 127 with atypical parkinsonian syndromes (47 multiple system atrophy (MSA), 38 progressive supranuclear palsy (PSP), and 42 corticobasal syndrome (CBS)) with 18 F-fluorodeoxyglucose PET to quantify the expression of previously validated disease-related patterns for PD, MSA, PSP, and CBS and 18 F-fluoropropyl-b-CIT PET to quantify caudate and putamen dopamine transporter (DAT) binding. The patients in each group exhibited significant elevations in the expression of the corresponding disease-related pattern (p < 0.001), relative to 16 healthy subjects. With the exception of cerebellar MSA (MSA-C), all groups displayed significant reductions in putamen DAT binding relative to healthy subjects (p < 0.05). Correlations between the dopaminergic and metabolic measures were significant in PD and CBS but not in MSA and PSP. In all patient groups with the exception of MSA-C and CBS, pattern expression values and DAT binding correlated with disease duration and severity measures. The findings suggest that in these parkinsonian disorders, metabolic network expression and DAT binding provide complementary information regarding the underlying disease process.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations

Lee

Eidelberg

2016

J Cereb Blood Flow Metab

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“…This accords with the low PDRP expression values seen in patients with MSA. 32 Indeed, the 3 atypical phenoconverters exhibited negative subject scores, denoting subnormal network activity, which discriminated them from the other subjects with RBD. Moreover, in agreement with the earlier onset of MSA relative to PD/DLB, 33 these individuals proved to be younger than phenoconverters to PD or DLB (baseline age 5 58.8 6 8.3 years vs 71.4 6 4.3 years).…”

Section: Logistical Regression Analysis Logistical Regression Analysismentioning

confidence: 96%

Abnormal metabolic network activity in REM sleep behavior disorder

et al. 2014

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Objective: To determine whether the Parkinson disease-related covariance pattern (PDRP) expression is abnormally increased in idiopathic REM sleep behavior disorder (RBD) and whether increased baseline activity is associated with greater individual risk of subsequent phenoconversion.Methods: For this cohort study, we recruited 2 groups of RBD and control subjects. Cohort 1 comprised 10 subjects with RBD (63.5 6 9.4 years old) and 10 healthy volunteers (62.7 6 8.6 years old) who underwent resting-state metabolic brain imaging with 18 F-fluorodeoxyglucose PET. Cohort 2 comprised 17 subjects with RBD (68.9 6 4.8 years old) and 17 healthy volunteers (66.6 6 6.0 years old) who underwent resting brain perfusion imaging with ethylcysteinate dimer SPECT. The latter group was followed clinically for 4.6 6 2.5 years by investigators blinded to the imaging results. PDRP expression was measured in both RBD groups and compared with corresponding control values.Results: PDRP expression was elevated in both groups of subjects with RBD (cohort 1: p , 0.04; cohort 2: p , 0.005). Of the 17 subjects with long-term follow-up, 8 were diagnosed with Parkinson disease or dementia with Lewy bodies; the others did not phenoconvert. For individual subjects with RBD, final phenoconversion status was predicted using a logistical regression model based on PDRP expression and subject age at the time of imaging (r 2 5 0.64, p , 0.0001).Conclusions: Latent network abnormalities in subjects with idiopathic RBD are associated with a greater likelihood of subsequent phenoconversion to a progressive neurodegenerative syndrome.

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“…[MSARP] (20,21)), and PSP (progressive supranuclear palsy-related pattern [PSPRP] (20,22)) to classify a North American training cohort of 167 individuals with parkinsonism with an inconclusive clinical diagnosis. These subjects underwent metabolic brain imaging an average of 2.6 y before a final clinical diagnosis was made.…”

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confidence: 99%

Automated Differential Diagnosis of Early Parkinsonism Using Metabolic Brain Networks: A Validation Study

et al. 2015

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The differentiation of idiopathic Parkinson disease (IPD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the most common atypical parkinsonian look-alike syndromes (APS), can be clinically challenging. In these disorders, diagnostic inaccuracy is more frequent early in the clinical course when signs and symptoms are mild. Diagnostic inaccuracy may be particularly relevant in trials of potential disease-modifying agents, which typically involve participants with early clinical manifestations.In an initial study, we developed a probabilistic algorithm to classify subjects with clinical parkinsonism but uncertain diagnosis based on the expression of metabolic covariance patterns for IPD, MSA, and PSP. Classifications based on this algorithm agreed closely with final clinical diagnosis. Nonetheless, blinded prospective validation is required before routine use of the algorithm can be considered. Methods: We used metabolic imaging to study an independent cohort of 129 parkinsonian subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 y or less at the time of imaging. After imaging, subjects were followed by blinded movement disorders specialists for an average of 2.2 y before final diagnosis was made. When the algorithm was applied to the individual scan data, the probabilities of IPD, MSA, and PSP were computed and used to classify each of the subjects. The resulting image-based classifications were then compared with the final clinical diagnosis. Results: IPD subjects were distinguished from APS with 94% specificity and 96% positive predictive value (PPV) using the original 2-level logistic classification algorithm. The algorithm achieved 90% specificity and 85% PPV for MSA and 94% specificity and 94% PPV for PSP. The diagnostic accuracy was similarly high (specificity and PPV . 90%) for parkinsonian subjects with short symptom duration. In addition, 25 subjects were classified as level I indeterminate parkinsonism and 4 more subjects as level II indeterminate APS. Conclusion: Automated pattern-based image classification can improve the diagnostic accuracy in patients with parkinsonism, even at early disease stages.

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Network correlates of disease severity in multiple system atrophy

Cited by 52 publications

References 28 publications

Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations

Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations

Abnormal metabolic network activity in REM sleep behavior disorder

Automated Differential Diagnosis of Early Parkinsonism Using Metabolic Brain Networks: A Validation Study

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