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      Cognitive reserve in the healthy elderly: cognitive and psychological factors

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          Cognitive reserve (CR) helps explain the mismatch between expected cognitive decline and observed maintenance of cognitive functioning in older age. Factors such as education, literacy, lifestyle, and social networking are usually considered to be proxies of CR and its variability between individuals. A more direct approach to examine CR is through the assessment of capacity to gain from practice in a standardized challenging cognitive task that demands activation of cognitive resources. In this study, we applied a testing-the-limits paradigm to a group of 136 healthy elderly subjects (60–75 years) and additionally examined the possible contribution of complex mental activities and quality of sleep to cognitive performance gain. We found a significant but variable gain and identified verbal memory, cognitive flexibility, and problem-solving as important factors. This outcome is in line with our earlier study on CR in healthy mental aging. Interestingly and contrary to expectations, our analysis revealed that complex mental activities and sleep quality do not significantly influence CR. Best subset regression showed that better verbal memory and higher cognitive flexibility were related to high CR, which could also be seen when contrasting “high” and “low” cognitive performers; again, complex mental activities and sleep quality did not contribute to this measure of CR. In conclusion, the results of this study support and extend previous findings on CR in older age; further, they underline the need for improvements in existing protocols for assessing CR in a dynamic manner.

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          How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence.

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            Use of the extreme groups approach: a critical reexamination and new recommendations.

            Analysis of continuous variables sometimes proceeds by selecting individuals on the basis of extreme scores of a sample distribution and submitting only those extreme scores to further analysis. This sampling method is known as the extreme groups approach (EGA). EGA is often used to achieve greater statistical power in subsequent hypothesis tests. However, there are several largely unrecognized costs associated with EGA that must be considered. The authors illustrate the effects EGA can have on power, standardized effect size, reliability, model specification, and the interpretability of results. Finally, the authors discuss alternative procedures, as well as possible legitimate uses of EGA. The authors urge researchers, editors, reviewers, and consumers to carefully assess the extent to which EGA is an appropriate tool in their own research and in that of others.
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              Linear Model Selection by Cross-validation

               Jun-jun Shao (1993)

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                ScienceOpen Research
                07 October 2015
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                [1 ]Department Psychology, Neuropsychology, Ludwig-Maximilians-Universität München, Munich, Germany
                [2 ]Max Planck Institute of Psychiatry, Munich, Germany
                [3 ]Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
                [4 ]ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
                Author notes
                [* ]Corresponding author's e-mail address: zihl@ 123456psy.lmu.de
                © 2015 Zihl et al.

                This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

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                Figures: 1, Tables: 3, References: 52, Pages: 8
                Original article


                The authors want to replicate the findings from a previous study (Zihl et al., PLoS One. 2014;9: e84590) and want to demonstrate that the Digit Symbol Substitution Test (DSST) is a valid and reliable measure of cognitive reserve (CR) when used in a testing-the-limits paradigm. Their main hypotheses were that (1) participants with higher processing resources and higher executive capacities will show higher CR, and (2) CR is positively correlated with cognitive lifestyles, mood, well-being, and sleep quality. To test the hypotheses, 136 healthy elderly participants (age range: 60–75 years) with high level of education (≥13 years of schooling) had to perform 10 consecutive DSST trials. In addition, the authors assessed several cognitive and non-cognitive variables that might contribute to, or interact with CR. In my view this is an interesting study. However, there are several methodological problems that limit its scientific merit. A major problem of the present study is that the performance of the elderly participants was not compared to the performance of a young group. Therefore, it is not possible to replicate previous findings and to clarify whether age is an important moderating factor. It is also not possible to clarify whether the performance gain in the DSST is special or extraordinary. It is well known that repeated practice leads to an improvement in performance, regardless whether elderly participants or brain-damaged patients were investigated. Another problem in the present manuscript is the fact that the statistical analyses were unambitious. As Satz et al. (2010) indicated more complex statistical methods (e.g. Structural Equation Modeling, SEM) are needed to achieve “greater clarity to the conceptualization and study of the reserve construct”. By using SEM or regression analyses the moderating effects of the different variables (e.g. age, cognitive lifestyles, mood, well-being, and sleep quality) could be analyzed in a more sophisticated way. A further problem is that there is no a priori model of the moderating or mediating variables presented and tested. For example, sleep quality or reading performance are not included in the two models postulated by Satz et al. (2010). Due to these reasons the scientific merit of the present study is limited and it is difficult for me to see how the findings “... support and extend the model proposed by Satz et al. [3] ...”. Here are further points that need some clarification: • The authors should indicate that participants in this study did not participate in the first study (Zihl et al., PLoS One. 2014;9: e84590). • Performance gain was calculated as the difference between correctly assigned symbols in the first and in the best trial of the 10 consecutive trials. I wonder whether this calculation is really indicating the pure performance gain. What if the best trials is just an outlier? Couldn´t a mean score of the last two or three trials not be a more stable and reliable indicator of pure performance gain? • Please indicate when the more complex CR index is used and which score is presented in Table 1? • In addition to the effect sizes, the respective confidence intervals should be reported. • The Bonferroni correction is not clear to me. How many contrasts were calculated and what was the real selected alpha level? Please keep in mind that the Bonferroni correction is very conservative and leads to an increase in beta error. • Please indicate the number of participants in the extreme group comparisons. • Table 1: Scoring of the Stroop Test is not clear to me. If I understand correctly participants needed 100.45 seconds to read the word plate and 41.22 seconds the interference plate. Shouldn´t this be the other way round? The response time is fairly accurate. How was it measured? • Obvious limitations (e.g. missing control group) of the study were not mentioned in the Discussion section of the manuscript.
                2015-01-20 10:00 UTC
                3 people recommend this
                In this interesting study, the authors replicated the use of the paradigm testing-the-limits paradigm (Digit Symbol Substitution Test- DSST) in a group of 136 healthy elderly subjects (age range: 60–75 years) with high level of education (≥13 years of schooling). This mentally challenging task is considered to assess Cognitive Reserve (CR) in a dynamic way. Additionally, the authors assessed several cognitive and non cognitive variables that contribute to, or interact with CR, as cognitive multitasking, verbal learning and long-term memory, verbal short-term and working memory, information processing speed/attention and cognitive flexibility, interference, verbal fluency, visual problem-solving, and reading performance and psychological variables as mood, social and physical activities, sleep quality, and well-being. The authors in their introduction consider that it is important to demonstrate that the DSST is a valid and reliable measure of the CR, and should be reproduced. While I strongly agree with this assertion, the authors had already applied this instrument in a similar population (N=140, age 57–74 yrs, M = 67.27, SD = 4.16, >13 years education, see Zihl J et al. PLoS One. 2014;9: e84590). The use of a high selected healthy elderly group (with high educational level) was possibly related with the observed lack of association between CR and the non-cognitive variables assessed with this study. While the authors reflect this in the discussion, these should also be justified in the introduction section. Regarding the methods and results sections, some minor comments/suggestions: 1) The authors acknowledge that “All corresponding p-values reported are Bonferroni-corrected due to multiple comparisons, with significance level set at α = .05”. These Bonferroni corrections are not clear in the data presented (eg., report significant data with p=.05 – see table 2). 2) Considering the relations between psychological variables and cognitive variables, between cognitive variables and CR, as well as between CR and both cognitive and psychological variables, a moderation/mediation analysis should be also investigated. 3) As the authors report that there was no evidence that psychological measures were influenced by aging, the same should be reported for cognitive measures. 4) The authors could provide the data showing that “comparison of high and low performers in this study revealed higher performance in measures of cognitive architecture in subjects with higher mood scores and higher engagement in leisure activities, supporting the view that mood and cognitive lifestyle are key determinants of cognitive performance in healthy older ” page 6, if we consider that no significant differences were found in psychological measures obtained from low and high performers (results section) – page 5.
                2014-12-27 14:23 UTC
                5 people recommend this

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