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Coupled Cognitive Changes in Adulthood: A Meta-Analysis

Published on Mar 1, 2019in Psychological Bulletin16.41
· DOI :10.1037/bul0000179
Elliot M. Tucker-Drob27
Estimated H-index: 27
(University of Texas at Austin),
Andreas M. Brandmaier12
Estimated H-index: 12
(MPG: Max Planck Society),
Ulman Lindenberger77
Estimated H-index: 77
(MPG: Max Planck Society)
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  • Citations (3)
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Newest
Published on Oct 1, 2018in Neurobiology of Aging4.40
Karra Harrington18
Estimated H-index: 18
(University of Melbourne),
Adrian Schembri9
Estimated H-index: 9
+ 13 AuthorsChristopher C. Rowe72
Estimated H-index: 72
(University of Melbourne)
Abstract Cognitive decline is considered an inevitable consequence of aging; however, estimates of cognitive aging may be influenced negatively by undetected preclinical Alzheimer's disease (AD). This study aimed to determine the extent to which estimates of cognitive aging were biased by preclinical AD. Cognitively normal older adults (n = 494) with amyloid-β status determined from positron emission tomography neuroimaging underwent serial neuropsychological assessment at 18-month intervals ove...
Published on Jul 2, 2018in eLife7.55
Andreas M. Brandmaier12
Estimated H-index: 12
(MPG: Max Planck Society),
Elisabeth Wenger11
Estimated H-index: 11
(MPG: Max Planck Society)
+ 3 AuthorsUlman Lindenberger77
Estimated H-index: 77
(MPG: Max Planck Society)
Published on Jun 1, 2018in Psychology and Aging2.61
Ruth Sibbett4
Estimated H-index: 4
,
Tom C. Russ16
Estimated H-index: 16
(Edin.: University of Edinburgh)
+ 2 AuthorsIan J. Deary121
Estimated H-index: 121
Published on Apr 17, 2018in Frontiers in Psychology2.13
Andreas M. Brandmaier12
Estimated H-index: 12
(MPG: Max Planck Society),
Timo von Oertzen19
Estimated H-index: 19
(MPG: Max Planck Society)
+ 2 AuthorsChristopher Hertzog57
Estimated H-index: 57
(MPG: Max Planck Society)
Latent Growth Curve Models (LGCM) have become a standard technique to model change over time. Prediction and explanation of inter-individual differences in change are major goals in lifespan research. The major determinants of statistical power to detect individual differences in change are the magnitude of true inter-individual differences in linear change (LGCM slope variance), design precision, alpha level, and sample size. Here, we show that design precision can be expressed as the inverse o...
Published on 2017in Developmental Cognitive Neuroscience4.92
Rogier A. Kievit20
Estimated H-index: 20
(Cognition and Brain Sciences Unit),
Andreas M. Brandmaier12
Estimated H-index: 12
(MPG: Max Planck Society)
+ 7 AuthorsPeter Fonagy91
Estimated H-index: 91
(UCL: University College London)
Abstract Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitud...
Published on Oct 1, 2017in Psychological Science4.90
Rogier A. Kievit20
Estimated H-index: 20
(Cognition and Brain Sciences Unit),
Ulman Lindenberger77
Estimated H-index: 77
(MPG: Max Planck Society)
+ 4 AuthorsRaymond J. Dolan166
Estimated H-index: 166
(MPG: Max Planck Society)
One of the most replicable findings in psychology is the positive manifold: the observation that individual differences in cognitive abilities are universally positively correlated. Investigating the developmental origin of the positive manifold is crucial to understanding it. In a large longitudinal cohort of adolescents and young adults (N = 785; n = 566 across two waves, mean interval between waves = 1.48 years; age range = 14–25 years), we examined developmental changes in two core cognitive...
Published on Jan 1, 2017
Christine Sattler4
Estimated H-index: 4
,
Hans-Werner Wahl37
Estimated H-index: 37
+ 7 AuthorsAndreas Zenthöfer8
Estimated H-index: 8
Published on Jan 1, 2017in Spanish Journal of Psychology0.75
Stuart J. Ritchie23
Estimated H-index: 23
(Edin.: University of Edinburgh),
Elliot M. Tucker-Drob27
Estimated H-index: 27
+ 1 AuthorsIan J. Deary121
Estimated H-index: 121
(Edin.: University of Edinburgh)
The present study concerns the relation of mental and bodily characteristics to one another during ageing. The ‘common cause’ theory of ageing proposes that declines are shared across multiple, seemingly-disparate functions, including both physical and intellectual abilities. The concept of ‘reserve’ suggests that healthier cognitive (and perhaps bodily) functions from early in life are protective against the effects of senescence across multiple domains. In three waves of physical and cognitive...
Published on Dec 1, 2016in Nature Communications11.88
Simon R. Cox17
Estimated H-index: 17
,
Stuart J. Ritchie23
Estimated H-index: 23
+ 7 AuthorsIan J. Deary121
Estimated H-index: 121
Part of understanding ageing involves knowing how the brain’s connecting pathways change in healthy aging. Here, authors provide a detailed characterisation of data from 3513 UK Biobank participants, and show that the microstructure of these pathways becomes more similar with age.
Published on Nov 1, 2016in Intelligence2.61
Stuart J. Ritchie23
Estimated H-index: 23
,
Elliot M. Tucker-Drob27
Estimated H-index: 27
(University of Texas at Austin)
+ 8 AuthorsJohn M. Starr83
Estimated H-index: 83
Abstract It is critical to discover why some people's cognitive abilities age better than others'. We applied multivariate growth curve models to data from a narrow-age cohort measured on a multi-domain IQ measure at age 11 years and a comprehensive battery of thirteen measures of visuospatial, memory, crystallized, and processing speed abilities at ages 70, 73, and 76 years ( n = 1091 at age 70). We found that 48% of the variance in change in performance on the thirteen cognitive measures was s...
Cited By3
Newest
Published on Oct 1, 2019in Cognitive Development2.06
Smaragda Kazi9
Estimated H-index: 9
(Panteion University),
Elena Kazali2
Estimated H-index: 2
(Panteion University)
+ 2 AuthorsAndreas Demetriou1
Estimated H-index: 1
(University of Nicosia)
Abstract This study explored longitudinally how cognizance mediates between executive and reasoning process from 4 to 10 years of age. Four-, 6-, and 8-years old children were tested twice by executive (inhibition, flexibility in shifting, and working memory), cognizance (awareness of perceptual and inferential origins of knowledge, first- and second-order ToM, and awareness of similarities and differences between cognitive processes), and reasoning tasks (deductive and Raven-like fluid reasonin...
Published on Jul 26, 2019in bioRxiv
Maxwell L. Elliott2
Estimated H-index: 2
(Duke University),
Daniel W. Belsky29
Estimated H-index: 29
+ 7 AuthorsAhmad R. Hariri71
Estimated H-index: 71
An individual's brain-age is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. Brain-age has been proposed as a biomarker of age-related deterioration of the brain. Having an older brain-age has been linked to Alzheimer's, dementia and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brain-age a biomarker of acce...
Published on Jun 1, 2019in Trends in Cognitive Sciences16.17
Joshua D. Koen10
Estimated H-index: 10
(ND: University of Notre Dame),
Michael D. Rugg92
Estimated H-index: 92
(UTD: University of Texas at Dallas)
Many cognitive abilities decline with age even in the absence of detectable pathology. Recent evidence indicates that age-related neural dedifferentiation, operationalized in terms of neural selectivity, may contribute to this decline. We review here work exploring the relationship between neural dedifferentiation, cognition, and age. Compelling evidence for age effects on neural selectivity comes from both non-human animal and human research. However, current data suggest that age does not mode...
Published on Oct 24, 2018in bioRxiv
Anders M. Fjell55
Estimated H-index: 55
(University of Oslo),
Chi-Hua Chen24
Estimated H-index: 24
(UCSD: University of California, San Diego)
+ 14 AuthorsLia Ferschmann3
Estimated H-index: 3
(University of Oslo)
The human cerebral cortex is highly regionalized. We aimed to test whether principles of regionalization could be traced from embryonic development throughout the human lifespan. A data-driven fuzzy-clustering approach was used to identify regions of coordinated longitudinal development of cortical surface area (SA) and thickness (CT) over 1.5 years (n = 301, 4-12 years). First, the SA clusters were compared to patterns from embryonic cortical development. The earliest sign of cortical regionali...
View next paperAge-related difference in relationships between cognitive processing speed and general cognitive status.