Time-lagged associations between cognitive and cortical development from childhood to early adulthood.
暂无分享,去创建一个
[1] Lu Ou,et al. Whats for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R , 2019, R J..
[2] E. Ferrer,et al. Studying developmental processes in accelerated cohort-sequential designs with discrete- and continuous-time latent change score models. , 2019, Psychological methods.
[3] Sy-Miin Chow,et al. Methodological Issues and Extensions to the Latent Difference Score Framework 1 , 2018, Longitudinal Multivariate Psychology.
[4] Paola Galdi,et al. A distributed brain network predicts general intelligence from resting-state human neuroimaging data , 2018, bioRxiv.
[5] S. Karama,et al. Brain-intelligence relationships across childhood and adolescence: A latent-variable approach , 2018 .
[6] R. Colom,et al. Enhancing Intelligence: From the Group to the Individual , 2018, Journal of Intelligence.
[7] B. J. Casey,et al. Prediction complements explanation in understanding the developing brain , 2018, Nature Communications.
[8] Joshua F. Wiley,et al. MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus , 2018, Structural equation modeling : a multidisciplinary journal.
[9] Benjamin S. Aribisala,et al. Coupled changes in hippocampal structure and cognitive ability in later life , 2018, Brain and behavior.
[10] F. Gobet,et al. Video Game Training Does Not Enhance Cognitive Ability: A Comprehensive Meta-Analytic Investigation , 2017, Psychological bulletin.
[11] Michael Moutoussis,et al. Developmental cognitive neuroscience using latent change score models: A tutorial and applications , 2017, Developmental Cognitive Neuroscience.
[12] E. Ferrer,et al. Frontoparietal Structural Connectivity in Childhood Predicts Development of Functional Connectivity and Reasoning Ability: A Large-Scale Longitudinal Investigation , 2017, The Journal of Neuroscience.
[13] Charles C. Driver,et al. Continuous time structural equation modeling with R package ctsem , 2017 .
[14] E. Sowell,et al. Puberty and structural brain development in humans , 2017, Frontiers in Neuroendocrinology.
[15] J. Willing,et al. Pubertal onset as a critical transition for neural development and cognition , 2017, Brain Research.
[16] R. Haier. The Neuroscience of Intelligence , 2016 .
[17] Elizabeth A. L. Stine-Morrow,et al. Do “Brain-Training” Programs Work? , 2016, Psychological science in the public interest : a journal of the American Psychological Society.
[18] J. Flynn. Does your Family Make You Smarter?: Nature, Nurture, and Human Autonomy , 2016 .
[19] Andrew R. Bender,et al. White matter and memory in healthy adults: Coupled changes over two years , 2016, NeuroImage.
[20] Monica Melby-Lervåg,et al. There is no convincing evidence that working memory training is effective: A reply to Au et al. (2014) and Karbach and Verhaeghen (2014) , 2015, Psychonomic Bulletin & Review.
[21] Susanne M. Jaeggi,et al. There is no convincing evidence that working memory training is NOT effective: A reply to Melby-Lervåg and Hulme (2015) , 2015, Psychonomic Bulletin & Review.
[22] N. Allen,et al. Observed Measures of Negative Parenting Predict Brain Development during Adolescence , 2016, PloS one.
[23] Alan C. Evans,et al. Trajectories of cortical thickness maturation in normal brain development — The importance of quality control procedures , 2016, NeuroImage.
[24] Kristopher J Preacher,et al. No Need to be Discrete: A Method for Continuous Time Mediation Analysis , 2016 .
[25] R. Colom,et al. Structural efficiency within a parieto-frontal network and cognitive differences , 2016 .
[26] Martijn P van den Heuvel,et al. Development of the brain's structural network efficiency in early adolescence: A longitudinal DTI twin study , 2015, Human brain mapping.
[27] John Protzko. The environment in raising early intelligence: A meta-analysis of the fadeout effect , 2015 .
[28] Johan H. L. Oud,et al. Relating Latent Change Score and Continuous Time Models , 2015 .
[29] Ulrike Basten,et al. Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence , 2015 .
[30] Susana Muñoz Maniega,et al. Coupled Changes in Brain White Matter Microstructure and Fluid Intelligence in Later Life , 2015, The Journal of Neuroscience.
[31] Alan C. Evans,et al. Changes in thickness and surface area of the human cortex and their relationship with intelligence. , 2015, Cerebral cortex.
[32] Jeffrey M. Spielberg,et al. A Longitudinal Study: Changes in Cortical Thickness and Surface Area during Pubertal Maturation , 2015, PloS one.
[33] Alan C. Evans,et al. Accelerated longitudinal cortical thinning in adolescence , 2015, NeuroImage.
[34] Susanne M. Jaeggi,et al. Improving fluid intelligence with training on working memory: a meta-analysis , 2015, Psychonomic bulletin & review.
[35] Martin Lövdén,et al. Changes in perceptual speed and white matter microstructure in the corticospinal tract are associated in very old age , 2014, NeuroImage.
[36] Lara M. Wierenga,et al. Unique developmental trajectories of cortical thickness and surface area , 2014, NeuroImage.
[37] John O. Willis,et al. Wechsler Abbreviated Scale of Intelligence , 2014 .
[38] Wendy Johnson,et al. Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents , 2014, NeuroImage.
[39] N. Allen,et al. Positive parenting predicts the development of adolescent brain structure: A longitudinal study , 2013, Developmental Cognitive Neuroscience.
[40] L Penke,et al. Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age , 2013, Molecular Psychiatry.
[41] J. Castro-Fornieles,et al. The Human Cerebral Cortex Flattens during Adolescence , 2013, The Journal of Neuroscience.
[42] P. Thompson,et al. Understanding human intelligence by imaging the brain. , 2013 .
[43] Susan M Resnick,et al. Recent Changes Leading to Subsequent Changes: Extensions of Multivariate Latent Difference Score Models , 2012, Structural equation modeling : a multidisciplinary journal.
[44] J. Oud,et al. An SEM approach to continuous time modeling of panel data: relating authoritarianism and anomia. , 2012, Psychological methods.
[45] Adrian Furnham,et al. The Wiley-Blackwell handbook of individual differences , 2013 .
[46] Emilio Ferrer,et al. Longitudinal Modeling of Developmental Changes in Psychological Research , 2010 .
[47] E. Ferrer,et al. Factorial Invariance within Longitudinal Structural Equation Models: Measuring the Same Construct across Time. , 2010, Child development perspectives.
[48] I. Deary,et al. The neuroscience of human intelligence differences , 2010, Nature Reviews Neuroscience.
[49] T. Salthouse. When does age-related cognitive decline begin? , 2009, Neurobiology of Aging.
[50] Karama S,et al. Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds , 2009, NeuroImage.
[51] J. Mcardle. Latent variable modeling of differences and changes with longitudinal data. , 2009, Annual review of psychology.
[52] U. Lindenberger,et al. Neuroanatomical correlates of fluid intelligence in healthy adults and persons with vascular risk factors. , 2008, Cerebral cortex.
[53] B. Shaywitz,et al. Longitudinal models of developmental dynamics between reading and cognition from childhood to adolescence. , 2007, Developmental psychology.
[54] Emilio Ferrer,et al. Processing speed in childhood and adolescence: longitudinal models for examining developmental change. , 2007, Child development.
[55] R. Haier,et al. The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence , 2007, Behavioral and Brain Sciences.
[56] Alan C. Evans,et al. The NIH MRI study of normal brain development , 2006, NeuroImage.
[57] C. Jack,et al. Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI , 2005, Neurology.
[58] Alan C. Evans,et al. Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification , 2005, NeuroImage.
[59] Ron Kikinis,et al. Structural modeling of dynamic changes in memory and brain structure using longitudinal data from the normative aging study. , 2004, The journals of gerontology. Series B, Psychological sciences and social sciences.
[60] Emilio Ferrer,et al. Alternative Structural Models for Multivariate Longitudinal Data Analysis , 2003 .
[61] J. Mcardle,et al. Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. , 2002, Developmental psychology.
[62] William Meredith,et al. The role of factorial invariance in modeling growth and change. , 2001 .
[63] H. Toyoda,et al. Structural Equation Modeling : Present and Future. Festschrift in honor of Karl Joreskog , 2001 .
[64] J. Mcardle,et al. Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. , 2001 .
[65] Alan C. Evans,et al. Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.
[66] T. Salthouse,et al. Processing speed as a mental capacity. , 1994, Acta psychologica.
[67] W. Meredith. Measurement invariance, factor analysis and factorial invariance , 1993 .
[68] R. Kail,et al. Global developmental change in processing time. , 1992 .
[69] R. Kail. Developmental change in speed of processing during childhood and adolescence. , 1991, Psychological bulletin.
[70] P. Bentler,et al. Comparative fit indexes in structural models. , 1990, Psychological bulletin.
[71] John B. Willett,et al. Some Results on Reliability for the Longitudinal Measurement of Change: Implications for the Design of Studies of Individual Growth , 1989 .
[72] S. Sclove. Application of model-selection criteria to some problems in multivariate analysis , 1987 .
[73] R. Cattell. Intelligence : its structure, growth and action , 1987 .
[74] J. H. Steiger. Statistically based tests for the number of common factors , 1980 .
[75] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[76] R. Cattell,et al. Age differences in fluid and crystallized intelligence. , 1967, Acta psychologica.
[77] R. Cattell,et al. Refinement and test of the theory of fluid and crystallized general intelligences. , 1966, Journal of educational psychology.
[78] M. A. Anusuya,et al. Human Intelligence , 1965, Nature.