Abnormal metabolic network activity in REM sleep behavior disorder

Holtbernd, Florian; Gagnon, Jean‐François; Postuma, Ron; Ma, Yilong; Tang, Chris C.; Feigin, Andrew; Dhawan, Vijay; Vendette, Mélanie; Soucy, Jean‐Paul; Eidelberg, David; Montplaisir, Jacques

doi:10.1212/wnl.0000000000000130

Cited by 155 publications

(140 citation statements)

References 34 publications

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“…In line with 2 previous studies, RBD subjects significantly expressed the PDRP 12, 13. Although on average, PDRP z ‐scores were lower in RBD subjects compared with PD/DLB, more than half of the RBD subjects already had a PDRP z ‐score in the range of PD patients.…”

Section: Discussionsupporting

confidence: 90%

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FDG PET, dopamine transporter SPECT, and olfaction: Combining biomarkers in REM sleep behavior disorder

et al. 2017

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BackgroundIdiopathic REM sleep behavior disorder is a prodromal stage of Parkinson's disease and dementia with Lewy bodies. Hyposmia, reduced dopamine transporter binding, and expression of the brain metabolic PD‐related pattern were each associated with increased risk of conversion to PD. The objective of this study was to study the relationship between the PD‐related pattern, dopamine transporter binding, and olfaction in idiopathic REM sleep behavior disorder.MethodsIn this cross‐sectional study, 21 idiopathic REM sleep behavior disorder subjects underwent 18F‐fluorodeoxyglucose PET, dopamine transporter imaging, and olfactory testing. For reference, we included 18F‐fluorodeoxyglucose PET data of 19 controls, 20 PD patients, and 22 patients with dementia with Lewy bodies. PD‐related pattern expression z‐scores were computed from all PET scans.ResultsPD‐related pattern expression was higher in idiopathic REM sleep behavior disorder subjects compared with controls (P = 0.048), but lower compared with PD (P = 0.001) and dementia with Lewy bodies (P < 0.0001). PD‐related pattern expression was higher in idiopathic REM sleep behavior disorder subjects with hyposmia and in subjects with an abnormal dopamine transporter scan (P < 0.05, uncorrected).ConclusionPD‐related pattern expression, dopamine transporter binding, and olfaction may provide complementary information for predicting phenoconversion. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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Section: Discussionsupporting

confidence: 90%

“…To date, 2 groups have reported that RBD subjects have higher PDRP subject scores compared with controls 12, 13. In a longitudinal study of 17 RBD subjects, baseline PDRP expression was associated with a high risk of developing PD or DLB within 5 years 12…”

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confidence: 99%

FDG PET, dopamine transporter SPECT, and olfaction: Combining biomarkers in REM sleep behavior disorder

et al. 2017

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“…Two studies demonstrated that idiopathic RBD patients significantly express the PDRP (44,45). This finding was recently confirmed by our group (Meles et al, unpublished data, 2013(Meles et al, unpublished data, -2016.…”

Section: Predicting Pd Onset In Idiopathic Rbdsupporting

confidence: 74%

“…In contrast, 3 idiopathic RBD patients who developed MSA 2-4 y later did not express the PDRP at baseline. A logistic regression model based on PDRP expression and age at baseline could accurately predict phenoconversion to PD/DLB (44). These findings indicate that network expression has potential as a marker for phenoconversion and may have additional value in predicting which phenotype a patient with idiopathic RBD will develop.…”

Section: Predicting Pd Onset In Idiopathic Rbdmentioning

confidence: 77%

Metabolic Imaging in Parkinson Disease

et al. 2016

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This review focuses on recent human 18 F-FDG PET studies in Parkinson disease. First, an overview is given of the current analytic approaches to metabolic brain imaging data. Next, we discuss how 18 F-FDG PET studies have advanced understanding of the relation between distinct brain regions and associated symptoms in Parkinson disease, including cognitive decline. In addition, the value of 18 F-FDG PET studies in differential diagnosis, identifying prodromal patients, and the evaluation of treatment effects are reviewed. Finally, anticipated developments in the field are addressed.

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“…In addition to loss of normal DMN activity, the earliest stages of neurodegenerative illnesses are often associated with increasing expression of independent, abnormal disease-related metabolic networks (15,18,20,21). Using our approach, we directly compared the time course of the changes in normal and pathological network activity that occur in patients with these disorders.…”

Section: Discussionmentioning

confidence: 99%

Metabolic resting-state brain networks in health and disease

Spetsieris

Tang

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.default mode network | resting state networks | PET | principal component analysis | neurodegeneration T he persistence of local brain function in the absence of focused cognitive activity has attracted much interest over the past decade (1-3). Functional MRI (fMRI) is the most commonly used method to identify resting-state functional brain networks (RSNs), particularly the default mode network (DMN). Because of the spatiotemporal complexity of resting-state fMRI recordings, the extraction of stable RSN topographies using this technique has had to rely on processing algorithms, such as independent component analysis (ICA), to isolate discrete sources of signal in the data. Although this approach has delineated consistent patterns of resting activity in healthy populations (4-6), few validated methods exist to quantify and compare the expression of specific RSNs in individual subjects. Such measurements are particularly relevant in the study of progressive neurodegenerative disorders, in which stereotyped abnormalities develop selectively over time in one or another neural system (7). Indeed, associations between new network topographies and previously reported RSNs, particularly the DMN, have often been descriptive (8-10). In this vein, seed-based functional connectivity measurements have been used to delineate areas correlating with activity profiles in a specific nodal region. Regions identified by this method, however, may not exhibit t...

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Abnormal metabolic network activity in REM sleep behavior disorder

Cited by 155 publications

References 34 publications

FDG PET, dopamine transporter SPECT, and olfaction: Combining biomarkers in REM sleep behavior disorder

FDG PET, dopamine transporter SPECT, and olfaction: Combining biomarkers in REM sleep behavior disorder

Metabolic Imaging in Parkinson Disease

Metabolic resting-state brain networks in health and disease

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