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      Association Between White Matter Connectivity and Early Dementia in Patients With Parkinson Disease

      , , , , , , ,
      Neurology
      Ovid Technologies (Wolters Kluwer Health)

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          Abstract

          Background and Objectives

          Several clinical and neuroimaging biomarkers have been proposed to identify individuals with Parkinson disease (PD) who are at risk for ongoing cognitive decline. This study aimed to explore whether white matter (WM) connectivity disruption is associated with dementia conversion in patients with newly diagnosed PD with mild cognitive impairment (PD-MCI).

          Methods

          Seventy-five patients with drug-naive PD-MCI who underwent serial cognitive assessments during the follow-up period (>5 years) were enrolled for the neuroimaging analyses. The patients were classified into either the PD with dementia (PDD) high-risk group (PDD-H, n = 38) or low-risk group (PDD-L, n = 37), depending on whether they converted to dementia within 5 years of PD diagnosis. We conducted degree-based statistic analyses based on a graph-theoretical concept to identify the subnetworks whose WM connectivity was disrupted in the PDD-H group compared with the PDD-L group.

          Results

          The PDD-H group showed poorer cognitive performance on frontal/executive, visual memory/visuospatial, and attention/working memory/language function than the PDD-L group at baseline assessment. The PDD-H group exhibited more severely disrupted WM connectivity in both frontal and posterior cortical regions with 8 hub nodes in the degree-based statistic analysis. The strength of structural connectivity within the identified subnetworks was correlated with the composite scores of frontal/executive function domain (γ = 0.393) and the risk score of PDD conversion within 5 years (γ = −0.480).

          Discussion

          This study demonstrated that disrupted WM connectivity in frontal and posterior cortical regions, which correlated with frontal/executive dysfunction, is associated with early dementia conversion in PD-MCI.

          Classification of Evidence

          This study provides Class II evidence that disrupted WM connectivity in frontal and posterior cortical regions is associated with early dementia conversion in PD-MCI.

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          Most cited references49

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          An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

          The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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            Fast robust automated brain extraction.

            An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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              An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging

              In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal–Tanner gradients on a 3 T Siemens Verio, a 3 T Siemens Connectome Skyra or a 7 T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Neurology
                Neurology
                Ovid Technologies (Wolters Kluwer Health)
                0028-3878
                1526-632X
                May 02 2022
                May 03 2022
                May 03 2022
                February 21 2022
                : 98
                : 18
                : e1846-e1856
                Article
                10.1212/WNL.0000000000200152
                35190467
                16519ea6-d73c-4037-a212-331eeb8bcec4
                © 2022
                History

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