The default mode network integrity in patients with Parkinson’s disease is levodopa equivalent dose-dependent

Krajcovicova, Lenka; Mikl, Michal; Mareček, Radek; Rektorová, Irena

doi:10.1007/s00702-011-0723-5

Cited by 96 publications

(77 citation statements)

References 53 publications

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“…3A and 4B, black bar). Overall, differences in disease stage and treatment status may explain the inconsistency of prior studies of DMN connectivity and task-related deactivation in PD subjects (22)(23)(24). Partial restoration of these responses occurred following levodopa administration (Fig.…”

Section: Discussionmentioning

confidence: 94%

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: Discussionmentioning

confidence: 94%

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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“…Independent component analysis (ICA) enables isolation of resting-state brain networks, where individual areas in a given network show tight functional connectivity. 3 Recent studies demonstrated effectiveness of this technique in identifying changes in the default mode network 4,5 and sensorimotor network 6 in PD. Although ICA-derived BG network (BGN) has been reliably identified in other disease states, [7][8][9] to our knowledge, no study so far has investigated it in PD.…”

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confidence: 99%

Functional connectivity in the basal ganglia network differentiates PD patients from controls

et al. 2014

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Objective: To examine functional connectivity within the basal ganglia network (BGN) in a group of cognitively normal patients with early Parkinson disease (PD) on and off medication compared to age-and sex-matched healthy controls (HC), and to validate the findings in a separate cohort of participants with PD.Methods: Participants were scanned with resting-state fMRI (RS-fMRI) at 3T field strength.Resting-state networks were isolated using independent component analysis. A BGN template was derived from 80 elderly HC participants. BGN maps were compared between 19 patients with PD on and off medication in the discovery group and 19 age-and sex-matched controls to identify a threshold for optimal group separation. The threshold was applied to 13 patients with PD (including 5 drug-naive) in the validation group to establish reproducibility of findings.Results: Participants with PD showed reduced functional connectivity with the BGN in a wide range of areas. Administration of medication significantly improved connectivity. Average BGN connectivity differentiated participants with PD from controls with 100% sensitivity and 89.5% specificity. The connectivity threshold was tested on the validation cohort and achieved 85% accuracy. Conclusions:We demonstrate that resting functional connectivity, measured with MRI using an observer-independent method, is reproducibly reduced in the BGN in cognitively intact patients with PD, and increases upon administration of dopaminergic medication. Our results hold promise for RS-fMRI connectivity as a biomarker in early PD. Classification of evidence:This study provides Class III evidence that average connectivity in the BGN as measured by RS-fMRI distinguishes patients with PD from age-and sex-matched controls. Functional changes in the basal ganglia (BG) lie at the heart of Parkinson disease (PD). fMRI employing motor tasks has identified abnormalities in BG and supplementary motor area. 1,2However, interpretation of those changes may be confounded by the fact that patients with PD have difficulty with performing motor tasks. Resting-state fMRI can overcome this problem by providing an index of activity across the whole brain while the participant is at rest. Independent component analysis (ICA) enables isolation of resting-state brain networks, where individual areas in a given network show tight functional connectivity.3 Recent studies demonstrated effectiveness of this technique in identifying changes in the default mode network 4,5 and sensorimotor network 6 in PD. Although ICA-derived BG network (BGN) has been reliably identified in other disease states, 7-9 to our knowledge, no study so far has investigated it in PD.

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“…They focused on the DMN as it is supposed to be involved in cognitive processing, and found a decrease in FC in rigidity-predominant PD patients compared with healthy controls and tremor-predominant PD patients. This result led them to hypothesize that the discrepancies in the literature (Krajcovicova et al, 2012;Tessitore et al, 2012) might be related, in part, to the fact that other studies did not take PD subtypes into account. In order to evaluate possible alterations of the cognitive domain, together with the DMN, other RSNs have recently been investigated both with ICA and using graph theory analysis (Berman et al, 2016;Putcha et al, 2015).…”

Section: Parkinson's Diseasementioning

confidence: 93%

Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions

Dipasquale

Cercignani

2016

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SummaryNon-invasive mapping of brain functional connectivity (FC) has played a fundamental role in neuroscience, and numerous scientists have been fascinated by its ability to reveal the brain's intricate morphology and functional properties. In recent years, two different techniques have been developed that are able to explore FC in pathophysiological conditions and to provide simple and non-invasive biomarkers for the detection of disease onset, severity and progression. These techniques are independent component analysis, which allows a network-based functional exploration of the brain, and graph theory, which provides a quantitative characterization of the whole-brain FC. In this paper we provide an overview of these two techniques and some examples of their clinical applications in the most common neurodegenerative disorders associated with cognitive decline, including mild cognitive impairment, Alzheimer's disease, Parkinson's disease, dementia with Lewy Bodies and behavioral variant frontotemporal dementia.

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The default mode network integrity in patients with Parkinson’s disease is levodopa equivalent dose-dependent

Cited by 96 publications

References 53 publications

Metabolic resting-state brain networks in health and disease

Metabolic resting-state brain networks in health and disease

Functional connectivity in the basal ganglia network differentiates PD patients from controls

Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions

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