Metabolic brain networks in neurodegenerative disorders: a functional imaging approach

Eidelberg, David

doi:10.1016/j.tins.2009.06.003

Cited by 348 publications

(443 citation statements)

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“…This RSN (Fig. 1C, Left), which accounted for 23.1% of the subject × voxel variance, topographically resembled (r 2 > 0.8, P < 0.001) the PD-related metabolic covariance pattern (PDRP), a metabolic network that is consistently expressed in patients with this disorder (15,18,19). Similarly, PD1-PC1 expression was elevated in PD patients and low in healthy subjects, making it an accurate discriminator of PD subjects (e.g., P = 1.2e −7 for PD1 vs. NL2 subject scores, Student's t test).…”

Section: Resultsmentioning

confidence: 78%

“…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%

“…Thus, FDG PET may be used to map resting brain function in a complementary manner to fMRI. Spatial covariance analysis (15)(16)(17) was applied to resting-state metabolic scans from healthy subjects to identify reliable normal RSN topographies, which were then used to quantify pattern expression in individual subjects on a prospective single scan basis. These computations were used to validate the newly identified RSNs in independent testing samples scanned at rest or during task performance.…”

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confidence: 99%

“…Expression values for the three major normal metabolic RSNs (NL1-PC1, -PC2, and -PC3) were computed in H 2 15 O PET scans from a group of 14 later-stage PD patients (PD3: M/F 11/3; age 57.6 ± 7.6 y; disease duration 11.7 ± 4.9 y). These subjects (Table S1, Activation studies) were scanned at rest and during task performance in the baseline unmedicated (off) state and in the treated (on) state during intravenous levodopa administration.…”

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confidence: 99%

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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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Section: Resultsmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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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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“…Such patterns can be extracted from 18 F‐FDG‐PET data with scaled subprofile model and principal component analysis (SSM PCA6). With SSM PCA, a PD‐related pattern (PDRP) has been identified in multiple cohorts 7, 8, 9, 10.…”

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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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Metabolic brain networks in translational neurology: Concepts and applications

Niethammer

Eidelberg

2012

Annals of Neurology

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Over the past 2 decades, functional imaging techniques have become commonplace in the study of brain disease. Nevertheless, very few validated analytical methods have been developed specifically to identify and measure systems-level abnormalities in living patients. Network approaches are particularly relevant for translational research in the neurodegenerative disorders, which often involve stereotyped abnormalities in brain organization. In recent years, spatial covariance mapping, a multivariate analytical tool applied mainly to metabolic images acquired in the resting state, has provided a useful means of objectively assessing brain disorders at the network level. By quantifying network activity in individual subjects on a scan-by-scan basis, this technique makes it possible to objectively assess disease progression and the response to treatment on a system-wide basis. To illustrate the utility of network imaging in neurological research, we review recent applications of this approach in the study of Parkinson disease and related movement disorders. Novel uses of the technique are discussed, including the prediction of cognitive responses to dopaminergic therapy, evaluation of the effects of placebo treatment on network activity, assessment of preclinical disease progression, and the use of automated pattern-based algorithms to enhance diagnostic accuracy.

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Metabolic brain networks in neurodegenerative disorders: a functional imaging approach

Cited by 348 publications

References 59 publications

Metabolic resting-state brain networks in health and disease

Metabolic resting-state brain networks in health and disease

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

Metabolic brain networks in translational neurology: Concepts and applications

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