Time-lagged associations between cognitive and cortical development from childhood to early adulthood.

Throughout childhood and adolescence, humans experience marked changes in cortical structure and cognitive ability. Cortical thickness and surface area, in particular, have been associated with cognitive ability. Here we ask the question: What are the time-related associations between cognitive changes and cortical structure maturation. Identifying a developmental sequence requires multiple measurements of these variables from the same individuals across time. This allows capturing relations among the variables and, thus, finding whether (a) developmental cognitive changes follow cortical structure maturation, (b) cortical structure maturation follows cognitive changes, or (c) both processes influence each other over time. Four hundred and thiry children and adolescents (age range = 6.01-22.28 years) completed the Wechsler Abbreviated Scale of Intelligence battery and were MRI scanned at 3 time points separated by ≈2 years (Mage T1 = 10.60, SD = 3.58; Mage T2 = 12.63, SD = 3.62; Mage T3 = 14.49, SD = 3.55). Latent change score models were applied to quantify age-related relationships among the variables of interest. Our results indicate that cortical and cognitive changes related to each other reciprocally. Specifically, the magnitude or rate of the change in each variable at any occasion-and not the previous level-was predictive of later changes. These results were replicated for brain regions selected according to the coordinates identified in the Basten et al.'s (2015) meta-analysis, to the parieto-frontal integration theory (Jung & Haier, 2007) and to the whole cortex. Potential implications regarding brain plasticity and cognitive enhancement are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

[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.