Cognitive Dimensions in Alzheimer`s Disease, Mild Cognitive Impairment, and Normal Elderly: Developing a Common Metric~!2009-11-20~!2010-02-01~!2010-03-12~!

Chapman, Robert M.; Mapstone, Mark; McCrary, John W.; Gardner, Margaret N.; Bachus, Laura E.; DeGrush, Elizabeth; Reilly, Lindsey A.; Sandoval, Tiffany C.; Guillily, Maria D.

doi:10.2174/1874827901003010001

Cited by 7 publications

(11 citation statements)

References 42 publications

(42 reference statements)

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“…We developed a common metric for all the clinical groups of interest by using all of their data in the PCA, believing it would be a stronger measurement tool because it reflected both individual and, more importantly for discrimination, group differences. Also, including impaired and normal individuals in the PCA ensures that components best able to differentiate between the groups will appear in the component structure (Chapman et al, 2009). Methodologically, using a variety of groups in the development of the underlying structure would tend to avoid the one-group risk of restricting the range in the test measures and thereby attenuating correlations among variables that can result in falsely low estimates of component loadings (Fabrigar, MacCullum, Wegener, & Stahan, 1999).…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Alzheimer's disease using neuropsychological testing improved by multivariate analyses

Chapman

Mapstone

Porsteinsson

et al. 2010

Journal of Clinical and Experimental Neuropsychology

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Neuropsychological assessment aids in the diagnosis of Alzheimer’s disease (AD) by objectively establishing cognitive impairment from standardized tests. We present new criteria for diagnosis that use weighted combined scores from multiple tests. Our method employs two multivariate analyses: Principal Components Analysis (PCA) and discriminant analysis. PCA (N = 216 subjects) created more interpretable cognitive dimensions by resolving 49 test measures in our neuropsychological battery to 13 component scores for each subject. The component scores were used to build discriminant functions that classified each participant as either an early-stage AD (N = 55) or normal elderly (N = 78). Our discriminant function performed with high accuracy, sensitivity, and specificity (nearly all >90%) in the development, a cross-validation, and a new subjects validation. When contrasted to two different traditional empirical methods for diagnosis (using cutscores and defining AD as falling below 5% on two or more test domains), our results suggested that the multivariate method was superior in classification (approximately 20% more accurate).

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Section: Discussionmentioning

confidence: 99%

Diagnosis of Alzheimer's disease using neuropsychological testing improved by multivariate analyses

Chapman

Mapstone

Porsteinsson

et al. 2010

Journal of Clinical and Experimental Neuropsychology

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“…First, increasing the number of observations added stability to the resultant structure. Second, including a variety of subject groups in the creation of the component structure allowed for greater generalizability to the population (Chapman, Mapstone, McCrary et al, 2010; John, Easton, Prichep, & Friedman, 1993). Using data from only one group also would risk restricting the range in the test measures and attenuating correlations among variables that could result in falsely low estimates of component loadings (Fabrigar, MacCullum, Wegener, & Stahan, 1999).…”

Section: Methodsmentioning

confidence: 99%

“…Demographic information for the additional subjects also appears in Table 1 (for more detailed demographic and neuropsychological information concerning the AD and Control subjects, see Chapman, Mapstone, Porsteinsson, et al, 2010). …”

Section: Methodsmentioning

confidence: 99%

“…First, it revealed underlying cognitive dimensions implicit in neuropsychological test performance through the component loadings. The loadings related the test measures to the components through each measure’s weighted contribution to the components’ structure (Albert et al, 2007; Carroll, 1993; Chapman, Mapstone, McCrary et al, 2010; Chapman, Mapstone, Porsteinsson et al, 2010; Harman, 1976). Secondly, PCA achieved data reduction by remapping the 49 test measures to a smaller number of component scores via the component structure without being influenced by the group to which the subjects belonged.…”

Section: Methodsmentioning

confidence: 99%

“…The primary procedures were the MULTTEST, FACTOR, STEPDISC, and DISCRIM procedures. These have also been applied to neuropsychological tests used to classify AD from normal elderly (Chapman, Mapstone, Porsteinsson et al, 2010), as well as applied to brain Event-Related Potentials used to study AD (Chapman et al, 2007) and MCI conversion to AD (Chapman et al, 2009). To evaluate the statistical significance of the classification results, we applied Fisher’s Exact Test with an alpha level of 0.05.…”

Section: Methodsmentioning

confidence: 99%

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Predicting conversion from mild cognitive impairment to Alzheimer's disease using neuropsychological tests and multivariate methods

Chapman

Mapstone

McCrary

et al. 2010

Journal of Clinical and Experimental Neuropsychology

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Behavioral markers measured through neuropsychological testing in Mild Cognitive Impairment (MCI) were analyzed and combined in multivariate ways to predict conversion to Alzheimer’s disease (AD) in a longitudinal study of 43 MCI patients. The test measures taken at a baseline evaluation were first reduced to underlying components (Principal Components Analysis, PCA) and then the component scores were used in discriminant analysis to classify MCI individuals as likely to convert or not. When empirically weighted and combined, episodic memory, speeded executive functioning, recognition memory (false and true positives), visuospatial memory processing speed, and visuospatial episodic memory were together strong predictors of conversion to AD. These multivariate combinations of the test measures achieved through the PCA were good, statistically significant predictors of MCI conversion to AD (84% accuracy, 86% sensitivity, and 83% specificity). Importantly, the posterior probabilities of group membership that accompanied the binary prediction for each participant indicated the confidence of the prediction. Most of the subjects (81%) were in the highly confident probability bins (0.70 – 1.00), where the obtained prediction accuracy was more than 90%. The strength and reliability of this multivariate prediction method were tested by cross-validation and randomized resampling.

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Establishing robust cognitive dimensions for characterization and differentiation of patients with Alzheimer's disease, mild cognitive impairment, frontotemporal dementia and depression

Beck

Schmid

Berres

et al. 2013

Int J Geriat Psychiatry

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ObjectiveThe diagnosis of mild cognitive impairment (MCI) and dementia requires detailed neuropsychological examinations. These examinations typically yield a large number of outcome variables, which may complicate the interpretation and communication of results. The purposes of this study were the following: (i) to reduce a large data set of interrelated neuropsychological variables to a smaller number of cognitive dimensions; (ii) to create a common metric for these dimensions (z-scores); and (iii) to study the ability of the cognitive dimensions to distinguish between groups of patients with different types of cognitive impairment.MethodsWe tested 1646 patients with different forms of dementia or with a major depression with a standard (n = 632) or, if cognitively less affected, a challenging neuropsychological battery (n = 1014). To identify the underlying cognitive dimensions of the two test batteries, maximum likelihood factor analyses with a promax rotation were conducted. To interpret the sum scores of the factors as standard scores, we divided them by the standard deviation of a cognitively healthy sample (n = 1145).ResultsThe factor analyses yielded seven factors for each test battery. The cognitive dimensions in both test batteries distinguished patients with different forms of dementia (MCI, Alzheimer's dementia or frontotemporal dementia) and patients with major depression. Furthermore, patients with stable MCI could be separated from patients with progressing MCI. Discriminant analyses with an independent new sample of patients (n = 306) revealed that the new dimension scores distinguished new samples of patients with MCI from patients with Alzheimer's dementia with high accuracy.ConclusionThese findings suggest that these cognitive dimensions may benefit neuropsychological diagnostics.

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Cognitive Dimensions in Alzheimer`s Disease, Mild Cognitive Impairment, and Normal Elderly: Developing a Common Metric~!2009-11-20~!2010-02-01~!2010-03-12~!

Cited by 7 publications

References 42 publications

Diagnosis of Alzheimer's disease using neuropsychological testing improved by multivariate analyses

Diagnosis of Alzheimer's disease using neuropsychological testing improved by multivariate analyses

Predicting conversion from mild cognitive impairment to Alzheimer's disease using neuropsychological tests and multivariate methods

Establishing robust cognitive dimensions for characterization and differentiation of patients with Alzheimer's disease, mild cognitive impairment, frontotemporal dementia and depression

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