The Factor Structure of Cognitive Functioning in Cognitively Healthy Participants: a Meta-Analysis and Meta-Analysis of Individual Participant Data.

Many neuropsychologists are of the opinion that the multitude of cognitive tests may be grouped into a much smaller number of cognitive domains. However, there is little consensus on how many domains exist, what these domains are, nor on which cognitive tests belong to which domain. This incertitude can be solved by factor analysis, provided that the analysis includes a broad range of cognitive tests that have been administered to a very large number of people. In this article, two such factor analyses were performed, each combining multiple studies. However, because it was not possible to obtain complete multivariate data on more than the most common test variables in the field, not all possible domains were examined here. The first analysis was a factor meta-analysis of correlation matrices combining data of 60,398 healthy participants from 52 studies. Several models from the literature were fitted, of which a version based on the Cattell-Horn-Carroll (CHC) model was found to describe the correlations better than the others. The second analysis was a factor analysis of the Advanced Neuropsychological Diagnostics Infrastructure (ANDI) database, combining scores of 11,881 participants from 54 Dutch and Belgian studies not included in the first meta-analysis. Again, the model fit was better for the CHC model than for other models. Therefore, we conclude that the CHC model best characterizes both cognitive domains and which test belongs to each domain. Therefore, although originally developed in the intelligence literature, the CHC model deserves more attention in neuropsychology.

Investigating the effects of musical training on functional brain development with a novel Melodic MMN paradigm.

Sensitivity to changes in various musical features was investigated by recording the mismatch negativity (MMN) auditory event-related potential (ERP) in musically trained and nontrained children semi-longitudinally at the ages of 9, 11, and 13 years. The responses were recorded using a novel Melodic multi-feature paradigm which allows fast (<15 min) recording of an MMN profile for changes in melody, rhythm, musical key, timbre, tuning and timing. When compared to the nontrained children, the musically trained children displayed enlarged MMNs for the melody modulations by the age 13 and for the rhythm modulations, timbre deviants and slightly mistuned tones already at the age of 11. Also, a positive mismatch response elicited by delayed tones was larger in amplitude in the musically trained than in the nontrained children at age 13. No group differences were found at the age 9 suggesting that the later enhancement of the MMN in the musically trained children resulted from training and not pre-existing difference between the groups. The current study demonstrates the applicability of the Melodic multi-feature paradigm in school-aged children and indicates that musical training enhances auditory discrimination for musically central sound dimensions in pre-adolescence.

Regression-Based Normative Data for the Montreal Cognitive Assessment (MoCA) and Its Memory Index Score (MoCA-MIS) for Individuals Aged 18-91.

(1) Background: There is a need for a brief assessment of cognitive function, both in patient care and scientific research, for which the Montreal Cognitive Assessment (MoCA) is a psychometrically reliable and valid tool. However, fine-grained normative data allowing for adjustment for age, education, and/or sex are lacking, especially for its Memory Index Score (MIS). (2) Methods: A total of 820 healthy individuals aged 18-91 (366 men) completed the Dutch MoCA (version 7.1), of whom 182 also completed the cued recall and recognition memory subtests enabling calculation of the MIS. Regression-based normative data were computed for the MoCA Total Score and MIS, following the data-handling procedure of the Advanced Neuropsychological Diagnostics Infrastructure (ANDI). (3) Results: Age, education level, and sex were significant predictors of the MoCA Total Score (Conditional R2 = 0.4, Marginal R2 = 0.12, restricted maximum likelihood (REML) criterion at convergence: 3470.1) and MIS (Marginal R2 = 0.14, REML criterion at convergence: 682.8). Percentile distributions are presented that allow for age, education and sex adjustment for the MoCA Total Score and the MIS. (4) Conclusions: We present normative data covering the full adult life span that can be used for the screening for overall cognitive deficits and memory impairment, not only in older people with or people at risk of neurodegenerative disease, but also in younger individuals with acquired brain injury, neurological disease, or non-neurological medical conditions.

An Operational Definition of 'Abnormal Cognition' to Optimize the Prediction of Progression to Dementia: What Are Optimal Cut-Off Points for Univariate and Multivariate Normative Comparisons?

BACKGROUND: In neuropsychology and neurology, there is no consensus on the definition of abnormal cognition. OBJECTIVE: To operationally define 'abnormal cognition' for optimally predicting progression to dementia in a memory clinic sample, and to test whether multivariate profile analysis of cognitive test results improves this prediction compared to standard clinical evaluation. METHODS: We used longitudinal data from 835 non-demented patients of the Amsterdam Dementia Cohort. For 10 cognitive measures at baseline, we determined which number of abnormal tests and which magnitude of score deviations best predicted progression. RESULTS: Predictive ability for progression to dementia of one, two, and three abnormal test scores out of 10 is highly similar (Cox hazard ratios: 3.7-4.1) provided cut-off values are adapted appropriately. Cut-offs have to be less stringent if the number of abnormal tests required increases: the optimal cut-off is z < -1.45 when one deviating score is required, z < -1.15 when two abnormal tests are required, and z < -0.70 when three abnormal tests are required. The profile analysis has similar predictive ability at the cut-off of p < 0.22 (hazard ratio 3.8). A likelihood ratio test showed that this analysis improves prediction of progression to dementia when added to standard clinical evaluation (p < 0.001). CONCLUSION: Abnormal cognition may be defined as one, two, or three abnormal test scores out of 10 if the magnitude of score deviations is adapted accordingly. An abnormal score profile predicts decline to dementia equally well, and improves the prediction when used complimentary to standard clinical evaluation.

Multivariate normative comparisons for neuropsychological assessment by a multilevel factor structure or multiple imputation approach.

Neuropsychologists administer neuropsychological tests to decide whether a patient is cognitively impaired. This clinical decision is made by comparing a patient's scores to those of healthy participants in a normative sample. In a multivariate normative comparison, a patient's entire profile of scores is compared to scores in a normative sample. Such a multivariate comparison has been shown to improve clinical decision making. However, it requires a multivariate normative data set, which often is unavailable. To obtain such a multivariate normative data set, the authors propose to aggregate healthy control group data from existing neuropsychological studies. As not all studies administered the same tests, this aggregated database will contain substantial amounts of missing data. The authors therefore propose two solutions: multiple imputation and factor modeling. Simulation studies show that factor modeling is preferred over multiple imputation, provided that the factor model is adequately specified. This factor modeling approach will therefore allow routine use of multivariate normative comparisons, enabling more accurate clinical decision making. (PsycINFO Database Record

Advanced Neuropsychological Diagnostics Infrastructure (ANDI): A Normative Database Created from Control Datasets.

In the Advanced Neuropsychological Diagnostics Infrastructure (ANDI), datasets of several research groups are combined into a single database, containing scores on neuropsychological tests from healthy participants. For most popular neuropsychological tests the quantity, and range of these data surpasses that of traditional normative data, thereby enabling more accurate neuropsychological assessment. Because of the unique structure of the database, it facilitates normative comparison methods that were not feasible before, in particular those in which entire profiles of scores are evaluated. In this article, we describe the steps that were necessary to combine the separate datasets into a single database. These steps involve matching variables from multiple datasets, removing outlying values, determining the influence of demographic variables, and finding appropriate transformations to normality. Also, a brief description of the current contents of the ANDI database is given.