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      Regional locus coeruleus degeneration is uncoupled from noradrenergic terminal loss in Parkinson’s disease

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          Abstract

          Previous studies have reported substantial involvement of the noradrenergic system in Parkinson’s disease. Neuromelanin-sensitive MRI sequences and PET tracers have become available to visualize the cell bodies in the locus coeruleus and the density of noradrenergic terminal transporters.

          Combining these methods, we investigated the relationship of neurodegeneration in these distinct compartments in Parkinson’s disease. We examined 93 subjects (40 healthy controls and 53 Parkinson’s disease patients) with neuromelanin-sensitive turbo spin-echo MRI and calculated locus coeruleus-to-pons signal contrasts. Voxels with the highest intensities were extracted from published locus coeruleus coordinates transformed to individual MRI. To also investigate a potential spatial pattern of locus coeruleus degeneration, we extracted the highest signal intensities from the rostral, middle, and caudal third of the locus coeruleus. Additionally, a study-specific probabilistic map of the locus coeruleus was created and used to extract mean MRI contrast from the entire locus coeruleus and each rostro-caudal subdivision. Locus coeruleus volumes were measured using manual segmentations.

          A subset of 73 subjects had 11C-MeNER PET to determine noradrenaline transporter density, and distribution volume ratios of noradrenaline transporter-rich regions were computed. Patients with Parkinson’s disease showed reduced locus coeruleus MRI contrast independently of the selected method (voxel approaches: P < 0.0001, P < 0.001; probabilistic map: P < 0.05), specifically on the clinically-defined most affected side (P < 0.05), and reduced locus coeruleus volume (P < 0.0001). Reduced MRI contrast was confined to the middle and caudal locus coeruleus (voxel approach, rostral: P = 0.48, middle: P < 0.0001, and caudal: P < 0.05; probabilistic map, rostral: P = 0.90, middle: P < 0.01, and caudal: P < 0.05). The noradrenaline transporter density was lower in patients with Parkinson’s diseasein all examined regions (group effect P < 0.0001). No significant correlation was observed between locus coeruleus MRI contrast and noradrenaline transporter density. In contrast, the individual ratios of noradrenaline transporter density and locus coeruleus MRI contrast were lower in Parkinson’s disease patients in all examined regions (group effect P < 0.001). Our multimodal imaging approach revealed pronounced noradrenergic terminal loss relative to cellular locus coeruleus degeneration in Parkinson’s disease; the latter followed a distinct spatial pattern with the middle-caudal portion being more affected than the rostral part.

          The data shed first light on the interaction between the axonal and cell body compartments and their differential susceptibility to neurodegeneration in Parkinson’s disease, which may eventually direct research towards potential novel treatment approaches.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            FSL.

            FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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              MDS clinical diagnostic criteria for Parkinson's disease.

              This document presents the Movement Disorder Society Clinical Diagnostic Criteria for Parkinson's disease (PD). The Movement Disorder Society PD Criteria are intended for use in clinical research but also may be used to guide clinical diagnosis. The benchmark for these criteria is expert clinical diagnosis; the criteria aim to systematize the diagnostic process, to make it reproducible across centers and applicable by clinicians with less expertise in PD diagnosis. Although motor abnormalities remain central, increasing recognition has been given to nonmotor manifestations; these are incorporated into both the current criteria and particularly into separate criteria for prodromal PD. Similar to previous criteria, the Movement Disorder Society PD Criteria retain motor parkinsonism as the core feature of the disease, defined as bradykinesia plus rest tremor or rigidity. Explicit instructions for defining these cardinal features are included. After documentation of parkinsonism, determination of PD as the cause of parkinsonism relies on three categories of diagnostic features: absolute exclusion criteria (which rule out PD), red flags (which must be counterbalanced by additional supportive criteria to allow diagnosis of PD), and supportive criteria (positive features that increase confidence of the PD diagnosis). Two levels of certainty are delineated: clinically established PD (maximizing specificity at the expense of reduced sensitivity) and probable PD (which balances sensitivity and specificity). The Movement Disorder Society criteria retain elements proven valuable in previous criteria and omit aspects that are no longer justified, thereby encapsulating diagnosis according to current knowledge. As understanding of PD expands, the Movement Disorder Society criteria will need continuous revision to accommodate these advances.
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                Author and article information

                Contributors
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                Journal
                Brain
                Oxford University Press (OUP)
                0006-8950
                1460-2156
                September 01 2021
                October 22 2021
                July 01 2021
                September 01 2021
                October 22 2021
                July 01 2021
                : 144
                : 9
                : 2732-2744
                Affiliations
                [1 ]Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
                [2 ]Faculty of Medicine and University Hospital Cologne, Department of Neurology, University of Cologne, D-50937 Köln, Germany
                [3 ]Department of Nuclear Medicine and PET, Aarhus University Hospital, DK-8200 Aarhus N, Denmark
                [4 ]Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich, D-52425 Jülich, Germany
                [5 ]German Center for Neurodegenerative Diseases (DZNE), D-39120 Magdeburg, Germany
                [6 ]Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, D-39120 Magdeburg, Germany
                [7 ]Center for Behavioral Brain Sciences, University of Magdeburg, D-39120 Magdeburg, Germany
                [8 ]Division of Brain Sciences, Imperial College London, London SW7 2AZ, UK
                [9 ]Institute of Translational and Clinical Research, University of Newcastle upon Tyne, Newcastle upon Tyne NE1 7RU, UK
                Article
                10.1093/brain/awab236
                34196700
                356b75b4-565a-4491-843e-92e287c3c381
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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