Abnormal metabolic networks in atypical parkinsonism

Eckert, Thomas; Tang, Chengke; Ma, Yilong; Brown, Nathaniel; Lin, Tanya; Frucht, Steven J.; Feigin, Andrew; Eidelberg, David

doi:10.1002/mds.21933

Cited by 172 publications

(156 citation statements)

References 34 publications

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“…4 That said, metabolic imaging data suggest that PD can be distinguished from APS, and the different forms of APS can be distinguished from one another by distinct disease-related metabolic network topographies. [5][6][7] Disease-related metabolic covariance patterns ( Figure 1) have been identified and validated for PD (PDRP), 8,9 MSA (MSARP), 7,10 PSP (PSPRP), 10 and corticobasal ganglionic degeneration (CBDRP). 6 Indeed, these topographies have been used in concert as the basis for an automated computational algorithm to classify clinically indeterminate cases.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations

Lee

Eidelberg

2016

J Cereb Blood Flow Metab

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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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“…Disease-related metabolic patterns were also present in MSA consisting of hypometabolism in putamen and cerebellum in MSA (Eckert, et al 2008). Poston et al found that differences in expression of the MSA-related pattern correlated with clinical disability (Poston, et al 2012).…”

Section: Multiple System Atrophymentioning

confidence: 96%

“…The covariance pattern of PSP consists of decreased metabolism in the prefrontal cortex, frontal eye fields, caudate nuclei, medial thalamus and upper brainstem (Eckert, et al 2008). Brain stem atrophy and atrophy of the medial frontal cortical regions have also been reported in histopathological studies (Hauw, et al 1994).…”

Section: Progressive Supranuclear Palsymentioning

confidence: 99%