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      A novel computer adaptive word list memory test optimized for remote assessment: Psychometric properties and associations with neurodegenerative biomarkers in older women without dementia

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          Abstract

          Introduction

          This study established the psychometric properties and preliminary validity of the Stricker Learning Span (SLS), a novel computer adaptive word list memory test designed for remote assessment and optimized for smartphone use.

          Methods

          Women enrolled in the Mayo Clinic Specialized Center of Research Excellence (SCORE) were recruited via e‐mail or phone to complete two remote cognitive testing sessions. Convergent validity was assessed through correlation with previously administered in‐person neuropsychological tests (n = 96, ages 55–79) and criterion validity through associations with magnetic resonance imaging measures of neurodegeneration sensitive to Alzheimer's disease (n = 47).

          Results

          SLS performance significantly correlated with the Auditory Verbal Learning Test and measures of neurodegeneration (temporal meta‐regions of interest and entorhinal cortical thickness, adjusting for age and education). Test–retest reliabilities across two sessions were 0.71–0.76 (two‐way mixed intraclass correlation coefficients).

          Discussion

          The SLS is a valid and reliable self‐administered memory test that shows promise for remote assessment of aging and neurodegenerative disorders.

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          Most cited references48

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          A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.

          Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.
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            Unified segmentation.

            A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
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              A reproducible evaluation of ANTs similarity metric performance in brain image registration.

              The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling. Copyright © 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Stricker.Nikki@mayo.edu
                Journal
                Alzheimers Dement (Amst)
                Alzheimers Dement (Amst)
                10.1002/(ISSN)2352-8729
                DAD2
                Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring
                John Wiley and Sons Inc. (Hoboken )
                2352-8729
                09 March 2022
                2022
                : 14
                : 1 ( doiID: 10.1002/dad2.v14.1 )
                : e12299
                Affiliations
                [ 1 ] Department of Psychiatry and Psychology Mayo Clinic Rochester Minnesota USA
                [ 2 ] Department of Information Technology Mayo Clinic Rochester Minnesota USA
                [ 3 ] Department of Quantitative Health Sciences Mayo Clinic Rochester Minnesota USA
                [ 4 ] Department of Neurology and Psychological & Brain Sciences Washington University in St. Louis St. Louis Missouri USA
                [ 5 ] Department of Neurology Mayo Clinic Rochester Minnesota USA
                [ 6 ] Department of Radiology Mayo Clinic Rochester Minnesota USA
                Author notes
                [*] [* ] Correspondence

                Nikki H. Stricker, Department of Psychiatry and Psychology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

                E‐mail: Stricker.Nikki@ 123456mayo.edu

                Article
                DAD212299
                10.1002/dad2.12299
                8905660
                35280963
                98c88229-77f6-4a92-9def-b230c3c1c8f9
                © 2022 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 04 February 2022
                : 13 September 2021
                : 08 February 2022
                Page count
                Figures: 2, Tables: 4, Pages: 10, Words: 6906
                Categories
                Research Article
                Cognitive & Behavioral Assessment
                Custom metadata
                2.0
                2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.2 mode:remove_FC converted:09.03.2022

                aging,alzheimer's disease,cortical thickness,entorhinal cortex,hippocampus,learning,mayo test drive,mobile health,reliability,smartphone,stricker learning span,symbols test,validity,web

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