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      Abnormal cortical atrophy and functional connectivity are associated with depression in Parkinson’s disease

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          Abstract

          Objective

          This study aimed to investigate the association of altered cortical thickness and functional connectivity (FC) with depression in Parkinson’s disease (PD).

          Materials and methods

          A total of 26 non-depressed PD patients (PD-ND), 30 PD patients with minor depression (PD-MnD), 32 PD patients with major depression (PD-MDD), and 30 healthy controls (HC) were enrolled. Differences in cortical thickness among the four groups were assessed, and the results were used to analyze FC differences in regions of cortical atrophy. Binary logistic regression and receiver operating characteristic (ROC) curve analyses were also performed to identify clinical features and neuroimaging biomarkers that might help in the prediction of PD-MDD.

          Results

          Patients with PD-MDD showed decreased cortical thickness compared to patients with PD-ND in the left superior temporal and right rostral middle frontal gyri (RMFG), as well as weak FC between the left superior temporal gyrus and right cerebellum posterior lobe and between right RMFG and right inferior frontal gyrus and insula. The combination of cortical thickness, FC, and basic clinical features showed strong potential for predicting PD-MDD based on the area under the ROC curve (0.927, 95% CI 0.854–0.999, p < 0.001).

          Conclusion

          Patients with PD-MDD show extensive cortical atrophy and FC alterations, suggesting that cortical thickness and FC may be neuroimaging-based diagnostic biomarkers for PD-MDD.

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          Most cited references51

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          FreeSurfer.

          FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 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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              Cortical surface-based analysis. I. Segmentation and surface reconstruction.

              Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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                Author and article information

                Contributors
                Journal
                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                1663-4365
                31 August 2022
                2022
                : 14
                : 957997
                Affiliations
                [1] 1Department of Geriatric Neurology, First Affiliated Hospital, Kunming Medical University , Kunming, China
                [2] 2Department of Neurology, Chengdu Seventh People’s Hospital , Chengdu, China
                [3] 3Department of Radiology, First Affiliated Hospital, Kunming Medical University , Kunming, China
                [4] 4Department of Anesthesia, Kunming Xishan District People’s Hospital , Kunming, China
                [5] 5Yunnan Provincial Clinical Research Center for Neurological Diseases , Kunming, China
                [6] 6Yunnan Province Clinical Research Center for Geriatric Disease , Kunming, China
                Author notes

                Edited by: Sasanka Chakrabarti, Maharishi Markandeshwar University, Mullana, India

                Reviewed by: Wooyoung Jang, Gangneung Asan Hospital, South Korea; Atanu Biswas, Institute of Post Graduate Medical Education and Research (IPGMER), India

                *Correspondence: Shimei Li, 51024930@ 123456qq.com

                These authors have contributed equally to this work and share first authorship

                This article was submitted to Parkinson’s Disease and Aging-related Movement Disorders, a section of the journal Frontiers in Aging Neuroscience

                Article
                10.3389/fnagi.2022.957997
                9471004
                36118705
                b8864cee-e39c-4230-804e-e8c56bcf663f
                Copyright © 2022 Yin, Li, Yang, Gao, Hu, Luo, Li, Zhu, Zhou, Ren, Li and Yang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 May 2022
                : 03 August 2022
                Page count
                Figures: 4, Tables: 5, Equations: 0, References: 51, Pages: 13, Words: 8187
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Funded by: Applied Basic Research Foundation of Yunnan Province, doi 10.13039/100007471;
                Categories
                Neuroscience
                Original Research

                Neurosciences
                parkinson’s disease,depression,cortical atrophy,functional connectivity,neuroimaging biomarkers

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