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      Call for Papers: Sex and Gender in Neurodegenerative Diseases

      Submit here before September 30, 2024

      About Neurodegenerative Diseases: 3.0 Impact Factor I 4.3 CiteScore I 0.695 Scimago Journal & Country Rank (SJR)

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      The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction: The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). Methods: From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined. Results: Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer’s disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70–0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve. Conclusion: We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.

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          Most cited references36

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          Clock-drawing: is it the ideal cognitive screening test?

          The clock-drawing test has achieved widespread clinical use in recent years as a cognitive screening instrument and a significant amount of literature relates to its psychometric properties and clinical utility. This review aims to synthesize the available evidence and assess the value of this screening test according to well-defined criteria. A Medline and Psycho-info literature search of all languages was done from 1983 to 1998 including manual cross-referencing of bibliographies. A brief summary of all original scoring systems is provided as well as a review of replication studies. Psychometric data including correlations with other cognitive tests were recorded. Qualitative aspects of the test are also described. Among published studies, the mean sensitivity (85%) and specificity (85%) of the clock-drawing test are impressive. Correlations with the Mini-Mental State Examination and other cognitive tests was high, generally greater than r = 0.5. High levels of inter-rater and test-re-test reliability and positive predictive value are recorded and despite significant variability in the scoring systems, all report similar psychometric properties. The clock test also shows a sensitivity to cognitive change with good predictive validity. The clock-drawing test meets defined criteria for a cognitive screening instrument. It taps into a wide range of cognitive abilities including executive functions, is quick and easy to administer and score with excellent acceptability by subjects. Together with informant reports, the clock-drawing test is complementary to the widely used and validated Mini-Mental State Examination and should provide a significant advance in the early detection of dementia and in monitoring cognitive change. A simple scoring system with emphasis on the qualitative aspects of clock-drawing should maximize its utility. Copyright 2000 John Wiley & Sons, Ltd.
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            Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.

            Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.
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              Ten Percent Electrode System for Topographic Studies of Spontaneous and Evoked EEG Activities

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                Author and article information

                Journal
                DEM
                Dement Geriatr Cogn Disord
                10.1159/issn.1420-8008
                Dementia and Geriatric Cognitive Disorders
                S. Karger AG
                1420-8008
                1421-9824
                2020
                September 2020
                01 July 2020
                : 49
                : 1
                : 38-47
                Affiliations
                [_a] aNorwegian Advisory Unit for Aging and Health, Vestfold Health Trust, Tønsberg, Norway
                [_b] bDepartment of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
                [_c] cDepartment of Neurology, Regional Dementia Research Center, Zealand University Hospital, Roskilde, Denmark
                [_d] dDepartment of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
                [_e] eDepartment of Neurology, Danish Dementia Research Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
                [_f] fDepartment of Geriatric Medicine, Haraldsplass Deaconess Hospital, Bergen, Norway
                [_g] gDepartment of Clinical Science, University of Bergen, Bergen, Norway
                [_h] hDepartment of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
                [_i] iNorwegian National Health Association, Oslo, Norway
                [_j] jSection for Clinical Geriatrics, NVS Department, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
                Author notes
                *Knut Engedal, Norwegian Advisory Unit for Aging and Health, Vestfold Health Trust, PO 2136, NO–3103 Tønsberg (Norway), knut.engedal@aldringoghelse.no
                Article
                508392 Dement Geriatr Cogn Disord 2020;49:38–47
                10.1159/000508392
                32610316
                56e77278-a6bc-4006-ac9e-71d85e459628
                © 2020 S. Karger AG, Basel

                Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher. Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

                History
                : 07 April 2020
                : 01 May 2020
                Page count
                Figures: 2, Tables: 6, Pages: 10
                Categories
                30th Anniversary: Research Article

                Geriatric medicine,Neurology,Cardiovascular Medicine,Neurosciences,Clinical Psychology & Psychiatry,Public health
                Subjective cognitive decline,Dementia,EEG,Mild cognitive impairment

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