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      Demographically adjusted CERAD wordlist test norms in a Norwegian sample from 40 to 80 years

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          2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.

          The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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            The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS).

            The Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) is a consensus neuropsychological battery with established reliability and validity. One of the difficulties in implementing the MACFIMS in clinical settings is the reliance on manualized norms from disparate sources. In this study, we derived regression-based norms for the MACFIMS, using a unique data set to control for standard demographic variables (i.e., age, age2, sex, education). Multiple sclerosis (MS) patients (n = 395) and healthy volunteers (n = 100) did not differ in age, level of education, sex, or race. Multiple regression analyses were conducted on the performance of the healthy adults, and the resulting models were used to predict MS performance on the MACFIMS battery. This regression-based approach identified higher rates of impairment than manualized norms for many of the MACFIMS measures. These findings suggest that there are advantages to developing new norms from a single sample using the regression-based approach. We conclude that the regression-based norms presented here provide a valid alternative to identifying cognitive impairment as measured by the MACFIMS.
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              CERAD-neuropsychological battery in screening mild Alzheimer's disease.

              The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery (nb) is used as an evaluation tool for dementia. In Finland, CERAD-nb was introduced in 1999 and has been proposed to be used in primary health care. However, some of its parts need reassessment and focusing. The goal of this study was to examine the sensitivity and specificity of the subtests and their cut-off points most appropriate for identifying mild Alzheimer's disease (AD). The study population consisted of 171 patients with mild AD and 315 cognitively normal elderly. Both groups underwent CERAD-nb investigation as a part of a wider examination procedure. The most efficient subtests to discriminate patients with mild AD from the normal elderly were Wordlist delayed recall and savings, Wordlist learning and Wordlist recognition and a new variable of Total recall. Optimal cut-off points for each subtest are suggested. The sensitivities of the verbal memory subtests varied between 0.75 and 0.94, the specificities between 0.80 and 0.93 and the areas under the receiver operating characteristics curve between 0.89 and 0.96. The CERAD-nb is capable of differentiating cases with mild AD from normal elderly individuals particularly with its verbal memory subtests. New cut-off scores for CERAD's subtests validated in the study further enhance the differentiating power, and with these clarifications, CERAD-nb is considered appropriate to be used as a screening tool for AD even in primary health care. © 2011 John Wiley & Sons A/S.
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                Author and article information

                Journal
                The Clinical Neuropsychologist
                The Clinical Neuropsychologist
                Informa UK Limited
                1385-4046
                1744-4144
                March 08 2019
                March 08 2019
                : 1-13
                Affiliations
                [1 ] Department of Neurology, University Hospital of North Norway, Tromsø, Norway;
                [2 ] Department of Psychology Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway;
                [3 ] Department of Neurology, Akershus University Hospital, Lørenskog, Norway;
                [4 ] Department of Psychology, University of Oslo, Oslo, Norway;
                [5 ] Department of Clinical Medicine, Brain and Circulation Research Group, UiT The Arctic University of Norway, Tromsø, Norway;
                [6 ] Department of Neuromedicine and Movement Science Faculty of Medicine and Health, Sciences Norwegian University of Science and Technology, Trondheim, Norway;
                [7 ] Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway;
                [8 ] Departamento de Inteligencia Artificial Universidad Nacional de Educación a Distancia, Madrid, Spain;
                [9 ] Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
                Article
                10.1080/13854046.2019.1574902
                30849268
                e0099e76-a6d6-4080-a41b-68e02b5002ee
                © 2019

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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