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      Loss of Parietal Memory Network Integrity in Alzheimer’s Disease

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          Abstract

          A functional brain network, termed the parietal memory network (PMN), has been shown to reflect the familiarity of stimuli in both memory encoding and retrieval. The function of this network has been separated from the commonly investigated default mode network (DMN) in both resting-state fMRI and task-activations. This study examined the deficit of the PMN in Alzheimer’s disease (AD) patients using resting-state fMRI and independent component analysis (ICA) and investigated its diagnostic value in identifying AD patients. The DMN was also examined as a reference network. In addition, the robustness of the findings was examined using different types of analysis methods and parameters. Our results showed that the integrity as an intrinsic connectivity network for the PMN was significantly decreased in AD and this feature showed at least equivalent predictive ability to that for the DMN. These findings were robust to varied methods and parameters. Our findings suggest that the intrinsic connectivity of the PMN is disrupted in AD and further call for considering the PMN and the DMN separately in clinical neuroimaging studies.

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          Most cited references29

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          Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

          The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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            Selective changes of resting-state networks in individuals at risk for Alzheimer's disease.

            Alzheimer's disease (AD) is a neurodegenerative disorder that prominently affects cerebral connectivity. Assessing the functional connectivity at rest, recent functional MRI (fMRI) studies reported on the existence of resting-state networks (RSNs). RSNs are characterized by spatially coherent, spontaneous fluctuations in the blood oxygen level-dependent signal and are made up of regional patterns commonly involved in functions such as sensory, attention, or default mode processing. In AD, the default mode network (DMN) is affected by reduced functional connectivity and atrophy. In this work, we analyzed functional and structural MRI data from healthy elderly (n = 16) and patients with amnestic mild cognitive impairment (aMCI) (n = 24), a syndrome of high risk for developing AD. Two questions were addressed: (i) Are any RSNs altered in aMCI? (ii) Do changes in functional connectivity relate to possible structural changes? Independent component analysis of resting-state fMRI data identified eight spatially consistent RSNs. Only selected areas of the DMN and the executive attention network demonstrated reduced network-related activity in the patient group. Voxel-based morphometry revealed atrophy in both medial temporal lobes (MTL) of the patients. The functional connectivity between both hippocampi in the MTLs and the posterior cingulate of the DMN was present in healthy controls but absent in patients. We conclude that in individuals at risk for AD, a specific subset of RSNs is altered, likely representing effects of ongoing early neurodegeneration. We interpret our finding as a proof of principle, demonstrating that functional brain disorders can be characterized by functional-disconnectivity profiles of RSNs.
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              A study of cross-validation and bootstrap for accuracy estimation and model selection in

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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
                27 March 2019
                2019
                : 11
                : 67
                Affiliations
                [1] 1Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , Shanghai, China
                [2] 2Brain Science and Technology Research Center, Shanghai Jiao Tong University , Shanghai, China
                [3] 3Department of Neurology, XuanWu Hospital of Capital Medical University , Beijing, China
                [4] 4Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders , Beijing, China
                [5] 5Beijing Institute of Geriatrics , Beijing, China
                [6] 6National Clinical Research Center for Geriatric Disorders , Beijing, China
                [7] 7Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University , Shanghai, China
                Author notes

                Edited by: Lutz Jäncke, University of Zurich, Switzerland

                Reviewed by: Adrian Gilmore, National Institute of Mental Health (NIMH), United States; Marina Weiler, National Institutes of Health (NIH), United States; Rui Li, Institute of Psychology (CAS), China

                These authors have contributed equally to this work

                Article
                10.3389/fnagi.2019.00067
                6446948
                11cab0f8-d3c8-44a3-9a44-b83b86ed8717
                Copyright © 2019 Hu, Du, Zhang, Li, Han 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
                : 01 August 2018
                : 08 March 2019
                Page count
                Figures: 9, Tables: 5, Equations: 0, References: 67, Pages: 15, Words: 9355
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 81270023, 81571756, 61633018
                Categories
                Neuroscience
                Original Research

                Neurosciences
                alzheimer’s disease,parietal memory network,default mode network,network integrity,independent component analysis

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