Reproducibility of brain‐cognition relationships using three cortical surface‐based protocols: An exhaustive analysis based on cortical thickness

People differ in their cognitive functioning. This variability has been exhaustively examined at the behavioral, neural and genetic level to uncover the mechanisms by which some individuals are more cognitively efficient than others. Studies investigating the neural underpinnings of interindividual differences in cognition aim to establish a reliable nexus between functional/structural properties of a given brain network and higher order cognitive performance. However, these studies have produced inconsistent results, which might be partly attributed to methodological variations. In the current study, 82 healthy young participants underwent MRI scanning and completed a comprehensive cognitive battery including measurements of fluid, crystallized, and spatial intelligence, along with working memory capacity/executive updating, controlled attention, and processing speed. The cognitive scores were obtained by confirmatory factor analyses. T1‐weighted images were processed using three different surface‐based morphometry (SBM) pipelines, varying in their degree of user intervention, for obtaining measures of cortical thickness (CT) across the brain surface. Distribution and variability of CT and CT‐cognition relationships were systematically compared across pipelines and between two cognitively/demographically matched samples to overcome potential sources of variability affecting the reproducibility of findings. We demonstrated that estimation of CT was not consistent across methods. In addition, among SBM methods, there was considerable variation in the spatial pattern of CT‐cognition relationships. Finally, within each SBM method, results did not replicate in matched subsamples. Hum Brain Mapp 36:3227–3245, 2015. © 2015 Wiley Periodicals, Inc.

[1]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[2]  Alan C Evans,et al.  Impact of scale space search on age‐ and gender‐related changes in MRI‐based cortical morphometry , 2013, Human brain mapping.

[3]  Kenia Martínez,et al.  Improvement in working memory is not related to increased intelligence scores , 2010 .

[4]  Alan C. Evans,et al.  Enhancement of MR Images Using Registration for Signal Averaging , 1998, Journal of Computer Assisted Tomography.

[5]  Arthur W. Toga,et al.  Diffeomorphic Sulcal Shape Analysis on the Cortex , 2012, IEEE Transactions on Medical Imaging.

[6]  K. Worsley,et al.  Unified univariate and multivariate random field theory , 2004, NeuroImage.

[7]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[8]  Alan C. Evans,et al.  Multiple surface identification and matching in magnetic resonance images , 1994, Other Conferences.

[9]  V. Gil,et al.  Medición en ciencias sociales y de la salud , 2011 .

[10]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[11]  Roberto Colom,et al.  General intelligence and memory span: Evidence for a common neuroanatomic framework , 2007, Cognitive neuropsychology.

[12]  B. Thompson,et al.  EFFECTS OF SAMPLE SIZE, ESTIMATION METHODS, AND MODEL SPECIFICATION ON STRUCTURAL EQUATION MODELING FIT INDEXES , 1999 .

[13]  G. Sapiro,et al.  Geometric partial differential equations and image analysis [Book Reviews] , 2001, IEEE Transactions on Medical Imaging.

[14]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[15]  Cheuk Y. Tang,et al.  Gray Matter and Intelligence Factors: Is There a Neuro-g?. , 2009 .

[16]  B. Byrne Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming , 1998 .

[17]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[18]  P. Bentler,et al.  Comparative fit indexes in structural models. , 1990, Psychological bulletin.

[19]  Thomas J. Bouchard,et al.  The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized , 2005 .

[20]  Alan C. Evans,et al.  Fast and robust parameter estimation for statistical partial volume models in brain MRI , 2004, NeuroImage.

[21]  Lorena R. R. Gianotti,et al.  Functional brain network efficiency predicts intelligence , 2012, Human brain mapping.

[22]  R. Haier,et al.  The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence , 2007, Behavioral and Brain Sciences.

[23]  Richard L. Lewis,et al.  The mind and brain of short-term memory. , 2008, Annual review of psychology.

[24]  M. A. Anusuya,et al.  Human Intelligence , 1965, Nature.

[25]  A. Dale,et al.  Distinct genetic influences on cortical surface area and cortical thickness. , 2009, Cerebral cortex.

[26]  Alan C. Evans,et al.  Measurement of Cortical Thickness Using an Automated 3-D Algorithm: A Validation Study , 2001, NeuroImage.

[27]  R. Haier,et al.  Reversed hierarchy in the brain for general and specific cognitive abilities: A morphometric analysis , 2014, Human brain mapping.

[28]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[29]  Suzanne E. Welcome,et al.  Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children , 2022 .

[30]  Michael C. Pyryt Human cognitive abilities: A survey of factor analytic studies , 1998 .

[31]  K. McGrew CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research , 2009 .

[32]  D. V. Essen,et al.  Cognitive neuroscience 2.0: building a cumulative science of human brain function , 2010, Trends in Cognitive Sciences.

[33]  Alan C. Evans,et al.  BigBrain: An Ultrahigh-Resolution 3D Human Brain Model , 2013, Science.

[34]  John B. Carroll,et al.  The Higher-stratum Structure of Cognitive Abilities: Current Evidence Supports g and About Ten Broad Factors , 2003 .

[35]  A. Toga,et al.  Mapping sulcal pattern asymmetry and local cortical surface gray matter distribution in vivo: maturation in perisylvian cortices. , 2002, Cerebral cortex.

[36]  Steven Robbins,et al.  An unbiased iterative group registration template for cortical surface analysis , 2007, NeuroImage.

[37]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[38]  J. S. Long,et al.  Testing Structural Equation Models , 1993 .

[39]  R. Haier,et al.  Human intelligence and brain networks , 2010, Dialogues in clinical neuroscience.

[40]  Alan C. Evans,et al.  Cortical thickness analysis examined through power analysis and a population simulation , 2005, NeuroImage.

[41]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[42]  Rex E. Jung,et al.  Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model , 2009 .

[43]  Michael W. Cole,et al.  Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence , 2012, The Journal of Neuroscience.

[44]  Rainer Goebel,et al.  Measuring structural–functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment , 2012, NeuroImage.

[45]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[46]  Richard J. Haier,et al.  Neuroanatomic overlap between intelligence and cognitive factors: Morphometry methods provide support for the key role of the frontal lobes , 2013, NeuroImage.

[47]  M. Fox,et al.  Individual Variability in Functional Connectivity Architecture of the Human Brain , 2013, Neuron.

[48]  J. Raven,et al.  Manual for Raven's progressive matrices and vocabulary scales , 1962 .

[49]  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.

[50]  Gil Gaudia Intelligence about Intelligence , 1973, The Elementary School Journal.

[51]  Rex E. Jung,et al.  Cortical thickness correlates of specific cognitive performance accounted for by the general factor of intelligence in healthy children aged 6 to 18 , 2011, NeuroImage.

[52]  Richard M. Leahy,et al.  A Method for Automated Cortical Surface Registration and Labeling , 2012, WBIR.

[53]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[54]  C. Adcock,et al.  Primary Mental Abilities. , 1971, The Journal of general psychology.

[55]  L. Nyberg,et al.  Common fronto-parietal activity in attention, memory, and consciousness: Shared demands on integration? , 2005, Consciousness and Cognition.

[56]  I. Deary 125 years of intelligence in the American Journal of Psychology. , 2012, The American journal of psychology.

[57]  Kiralee M. Hayashi,et al.  Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia , 2004, NeuroImage.

[58]  Psychometric monographs , 1952 .

[59]  Richard M. Leahy,et al.  Geodesic curvature flow on surfaces for automatic sulcal delineation , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[60]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[61]  Anderson M. Winkler,et al.  Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies , 2010, NeuroImage.

[62]  P. Ackerman,et al.  Individual differences in working memory within a nomological network of cognitive and perceptual speed abilities. , 2002, Journal of experimental psychology. General.

[63]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[64]  J Mazziotta,et al.  A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[65]  R. Cabeza,et al.  Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies , 2000, Journal of Cognitive Neuroscience.

[66]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[67]  Jason Lerch,et al.  Measuring Cortical Thickness , 2001 .

[68]  J. Flynn,et al.  Intelligence: new findings and theoretical developments. , 2012, The American psychologist.

[69]  T. Yarkoni Big Correlations in Little Studies: Inflated fMRI Correlations Reflect Low Statistical Power—Commentary on Vul et al. (2009) , 2009, Perspectives on psychological science : a journal of the Association for Psychological Science.

[70]  R. Colom,et al.  Working memory and intelligence are highly related constructs, but why? , 2008 .

[71]  Alan C. Evans,et al.  Depth potential function for folding pattern representation, registration and analysis , 2009, Medical Image Anal..

[72]  Alan C. Evans,et al.  A method for identifying geometrically simple surfaces from three-dimensional images , 1998 .