Structural and functional brain parameters related to cognitive performance across development: Replication and extension of the parieto-frontal integration theory in a single sample

The Parieto-Frontal Integration Theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional MRI measures of amplitude of low frequency fluctuations (ALFF) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic and cerebellar regions showed comparable effects, hence PFIT needs expansion into an Extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining low resting energy state.

[1]  Jami F. Young,et al.  Association of anxiety phenotypes with risk of depression and suicidal ideation in community youth , 2020, Depression and anxiety.

[2]  C. Beckmann,et al.  Principles of temporal association cortex organisation as revealed by connectivity gradients , 2020, Brain Structure and Function.

[3]  K. Amunts,et al.  Cytoarchitectonic Characterization and Functional Decoding of Four New Areas in the Human Lateral Orbitofrontal Cortex , 2020, Frontiers in Neuroanatomy.

[4]  Athanasia M. Mowinckel,et al.  Visualisation of Brain Statistics with R-packages ggseg and ggseg3d , 2019, 1912.08200.

[5]  H. Kraemer.  Is It Time to Ban the P Value? , 2019, JAMA psychiatry.

[6]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[7]  Dustin Scheinost,et al.  Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies , 2019, NeuroImage.

[8]  Andrei G. Vlassenko,et al.  Persistent metabolic youth in the aging female brain , 2019, Proceedings of the National Academy of Sciences.

[9]  E. Ferrer,et al.  Time-lagged associations between cognitive and cortical development from childhood to early adulthood. , 2019, Developmental psychology.

[10]  J. Kable,et al.  Are Bigger Brains Smarter? Evidence From a Large-Scale Preregistered Study , 2018, Psychological science.

[11]  Mathieu Wolff,et al.  The Cognitive Thalamus as a Gateway to Mental Representations , 2018, The Journal of Neuroscience.

[12]  M. Wintermark,et al.  Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know , 2018, American Journal of Neuroradiology.

[13]  Dustin Scheinost,et al.  Task-induced brain state manipulation improves prediction of individual traits , 2018, Nature Communications.

[14]  Rex E. Jung,et al.  Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence , 2018, Nature Communications.

[15]  Paola Galdi,et al.  A distributed brain network predicts general intelligence from resting-state human neuroimaging data , 2018, bioRxiv.

[16]  T. Grabowski,et al.  Quantitative cerebrovascular pathology in a community-based cohort of older adults , 2018, Neurobiology of Aging.

[17]  R. Gur,et al.  Association between traumatic stress load, psychopathology, and cognition in the Philadelphia Neurodevelopmental Cohort , 2018, Psychological Medicine.

[18]  M. Dylan Tisdall,et al.  Quantitative assessment of structural image quality , 2018, NeuroImage.

[19]  Dustin Scheinost,et al.  Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets , 2018, NeuroImage.

[20]  David C. Jangraw,et al.  A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task , 2018, NeuroImage.

[21]  M. Weissman,et al.  Test-retest reliability of cerebral blood flow in healthy individuals using arterial spin labeling: Findings from the EMBARC study. , 2018, Magnetic resonance imaging.

[22]  Ulrike Basten,et al.  Intelligence is associated with the modular structure of intrinsic brain networks , 2017, Scientific Reports.

[23]  Gilles E. Gignac,et al.  Brain volume and intelligence: The moderating role of intelligence measurement quality , 2017 .

[24]  Christos Davatzikos,et al.  Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.

[25]  Efstathios D. Gennatas,et al.  Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood , 2017, The Journal of Neuroscience.

[26]  T. Travison,et al.  Cerebral blood flow MRI in the nondemented elderly is not predictive of post-operative delirium but is correlated with cognitive performance , 2017, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[27]  A. Jacobs,et al.  The Temporal Pole Top‐Down Modulates the Ventral Visual Stream During Social Cognition , 2015, Cerebral cortex.

[28]  K. Witkiewitz,et al.  Fronto‐Parietal gray matter and white matter efficiency differentially predict intelligence in males and females , 2016, Human brain mapping.

[29]  Pascale Tremblay,et al.  Broca and Wernicke are dead, or moving past the classic model of language neurobiology , 2016, Brain and Language.

[30]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[31]  Ragini Verma,et al.  The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort , 2016, NeuroImage.

[32]  R. Gur,et al.  The Computerized Neurocognitive Battery: Validation, aging effects, and heritability across cognitive domains. , 2016, Neuropsychology.

[33]  Kosha Ruparel,et al.  The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. , 2015, Journal of child psychology and psychiatry, and allied disciplines.

[34]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[35]  Martin Voracek,et al.  Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? , 2015, Neuroscience & Biobehavioral Reviews.

[36]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.

[37]  Efstathios D. Gennatas,et al.  Linked Sex Differences in Cognition and Functional Connectivity in Youth. , 2015, Cerebral cortex.

[38]  Xi-Nian Zuo,et al.  Short-term test–retest reliability of resting state fMRI metrics in children with and without attention-deficit/hyperactivity disorder , 2015, Developmental Cognitive Neuroscience.

[39]  Ulrike Basten,et al.  Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence , 2015 .

[40]  Stuart J. Ritchie,et al.  Beyond a bigger brain: Multivariable structural brain imaging and intelligence , 2015, Intelligence.

[41]  Christos Davatzikos,et al.  Imaging patterns of brain development and their relationship to cognition. , 2015, Cerebral cortex.

[42]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[43]  Baxter P. Rogers,et al.  Analyzing the association between functional connectivity of the brain and intellectual performance , 2015, Front. Hum. Neurosci..

[44]  R. Gur,et al.  Topologically Dissociable Patterns of Development of the Human Cerebral Cortex , 2015, The Journal of Neuroscience.

[45]  Steven P Reise,et al.  Psychometric properties of the Penn Computerized Neurocognitive Battery. , 2015, Neuropsychology.

[46]  Dinggang Shen,et al.  Large deformation diffeomorphic registration of diffusion-weighted imaging data , 2014, Medical Image Anal..

[47]  Rex E. Jung,et al.  Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence , 2014, NeuroImage.

[48]  Arno Klein,et al.  Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements , 2014, NeuroImage.

[49]  Klaus H. Maier-Hein,et al.  Methodological considerations on tract-based spatial statistics (TBSS) , 2014, NeuroImage.

[50]  Efstathios D. Gennatas,et al.  Impact of puberty on the evolution of cerebral perfusion during adolescence , 2014, Proceedings of the National Academy of Sciences.

[51]  Kosha Ruparel,et al.  Within-individual variability in neurocognitive performance: age- and sex-related differences in children and youths from ages 8 to 21. , 2014, Neuropsychology.

[52]  Kosha Ruparel,et al.  Neurocognitive growth charting in psychosis spectrum youths. , 2014, JAMA psychiatry.

[53]  Raphael T. Gerraty,et al.  Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery. , 2014, Neuropsychology.

[54]  Christos Davatzikos,et al.  Neuroimaging of the Philadelphia Neurodevelopmental Cohort , 2014, NeuroImage.

[55]  I. Koerte,et al.  Diffusion Tensor Imaging , 2014 .

[56]  Masao Ito,et al.  Consensus Paper: The Cerebellum's Role in Movement and Cognition , 2014, The Cerebellum.

[57]  Alex R. Smith,et al.  Sex differences in the structural connectome of the human brain , 2013, Proceedings of the National Academy of Sciences.

[58]  Christos Davatzikos,et al.  Functional Maturation of the Executive System during Adolescence , 2013, The Journal of Neuroscience.

[59]  M. Bar,et al.  The role of the parahippocampal cortex in cognition , 2013, Trends in Cognitive Sciences.

[60]  Bharat B. Biswal,et al.  The Influence of the Amplitude of Low-Frequency Fluctuations on Resting-State Functional Connectivity , 2013, Front. Hum. Neurosci..

[61]  Paul A. Yushkevich,et al.  Multi-Atlas Segmentation with Joint Label Fusion , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[62]  Mark A. Elliott,et al.  Being right is its own reward: Load and performance related ventral striatum activation to correct responses during a working memory task in youth , 2012, NeuroImage.

[63]  B. Avants,et al.  Longitudinal reproducibility and accuracy of pseudo-continuous arterial spin-labeled perfusion MR imaging in typically developing children. , 2012, Radiology.

[64]  Peter Kirsch,et al.  Test–retest reliability of evoked BOLD signals from a cognitive–emotive fMRI test battery , 2012, NeuroImage.

[65]  Hervé Abdi,et al.  A comprehensive reliability assessment of quantitative diffusion tensor tractography , 2012, NeuroImage.

[66]  L. Barsalou,et al.  Effects of Meditation Experience on Functional Connectivity of Distributed Brain Networks , 2012, Front. Hum. Neurosci..

[67]  D. Puigdemont,et al.  Deep brain stimulation of the subcallosal cingulate gyrus: further evidence in treatment-resistant major depression. , 2012, The international journal of neuropsychopharmacology.

[68]  Raquel E Gur,et al.  Age group and sex differences in performance on a computerized neurocognitive battery in children age 8-21. , 2012, Neuropsychology.

[69]  Marco Molinari,et al.  The cerebellar cognitive profile. , 2011, Brain : a journal of neurology.

[70]  J. Gray,et al.  Meditation experience is associated with differences in default mode network activity and connectivity , 2011, Proceedings of the National Academy of Sciences.

[71]  Armin Raznahan,et al.  How Does Your Cortex Grow? , 2011, The Journal of Neuroscience.

[72]  Brian B. Avants,et al.  An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data , 2011, Neuroinformatics.

[73]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[74]  Yong He,et al.  Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study. , 2011, Cerebral cortex.

[75]  H. Wickham.  ggplot2 , 2011 .

[76]  John G. Csernansky,et al.  Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults , 2010, Journal of Cognitive Neuroscience.

[77]  Wen-Chau Wu,et al.  In vivo venous blood T1 measurement using inversion recovery true‐FISP in children and adults , 2010, Magnetic resonance in medicine.

[78]  Satrajit S. Ghosh,et al.  Evaluation of volume-based and surface-based brain image registration methods , 2010, NeuroImage.

[79]  John Duncan,et al.  The role of the right inferior frontal gyrus: inhibition and attentional control , 2010, NeuroImage.

[80]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[81]  R. Gur,et al.  A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: Standardization and initial construct validation , 2010, Journal of Neuroscience Methods.

[82]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[83]  L. Cahill,et al.  Sex differences in molecular neuroscience: from fruit flies to humans , 2010, Nature Reviews Neuroscience.

[84]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[85]  E. Rolls,et al.  The orbitofrontal cortex and beyond: From affect to decision-making , 2008, Progress in Neurobiology.

[86]  A. Turken,et al.  Left inferior frontal gyrus is critical for response inhibition , 2008, BMC Neuroscience.

[87]  Ke Zhou,et al.  Diffusion tensor imaging of normal white matter maturation from late childhood to young adulthood: Voxel-wise evaluation of mean diffusivity, fractional anisotropy, radial and axial diffusivities, and correlation with reading development , 2008, NeuroImage.

[88]  Hans-Jochen Heinze,et al.  Contribution of Subcortical Structures to Cognition Assessed with Invasive Electrophysiology in Humans , 2008, Front. Neurosci..

[89]  Ze Wang,et al.  Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx. , 2008, Magnetic resonance imaging.

[90]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.

[91]  I. Olson,et al.  The Enigmatic temporal pole: a review of findings on social and emotional processing. , 2007, Brain : a journal of neurology.

[92]  Chaozhe Zhu,et al.  Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI , 2007, NeuroImage.

[93]  Wen-Chau Wu,et al.  Feasibility of Velocity Selective Arterial Spin Labeling in Functional MRI , 2007, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

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

[95]  S. F. Witelson,et al.  Intelligence and brain size in 100 postmortem brains: sex, lateralization and age factors. , 2006, Brain : a journal of neurology.

[96]  David H. Laidlaw,et al.  Sampling DTI fibers in the human brain based on DWI forward modeling , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[97]  V. Schmithorst,et al.  Cognitive functions correlate with white matter architecture in a normal pediatric population: A diffusion tensor MRI study , 2005, Human brain mapping.

[98]  Yingli Lu,et al.  Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.

[99]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[100]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

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

[102]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[103]  N. Makris,et al.  Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging. , 2001, Cerebral cortex.

[104]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[105]  G. Humphreys,et al.  Differential effects of word length and visual contrast in the fusiform and lingual gyri during , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[106]  B. Turetsky,et al.  An fMRI Study of Sex Differences in Regional Activation to a Verbal and a Spatial Task , 2000, Brain and Language.

[107]  Raquel E Gur,et al.  Sex differences in brain-behavior relationships between verbal episodic memory and resting regional cerebral blood flow , 2000, Neuropsychologia.

[108]  M. Forster,et al.  Key Concepts in Model Selection: Performance and Generalizability. , 2000, Journal of mathematical psychology.

[109]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[110]  B. Turetsky,et al.  Sex Differences in Brain Gray and White Matter in Healthy Young Adults: Correlations with Cognitive Performance , 1999, The Journal of Neuroscience.

[111]  E. Spelke,et al.  Sources of mathematical thinking: behavioral and brain-imaging evidence. , 1999, Science.

[112]  P. Royston,et al.  Generalized additive models , 1998 .

[113]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[114]  L. Cosmides,et al.  The Adapted mind : evolutionary psychology and the generation of culture , 1992 .

[115]  Ranjan Duara,et al.  Frontal hypermetabolism and thalamic hypometabolism in a patient with abnormal orienting and retrosplenial amnesia , 1990, Neuropsychologia.

[116]  W D Obrist,et al.  Sex and handedness differences in cerebral blood flow during rest and cognitive activity. , 1982, Science.

[117]  C. Blyth.  On Simpson's Paradox and the Sure-Thing Principle , 1972 .

[118]  N. Geschwind.  The Organization of Language and the Brain: Language disorders after brain damage help in elucidating the neural basis of verbal behavior , 1970 .

[119]  G. Yule.  NOTES ON THE THEORY OF ASSOCIATION OF ATTRIBUTES IN STATISTICS , 1903 .

[120]  Frederick Tiedemann.  The Brain of the Negro Compared with That of the European and the Orang-Outang , 1839, The British and foreign medical review.