Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior

We perform a comprehensive integrative analysis of multiple structural MR-based brain features and find for the first-time strong evidence relating inter-individual brain structural variations to a wide range of demographic and behavioral variates across a large cohort of young healthy human volunteers. Our analyses reveal that a robust ‘positive-negative’ spectrum of behavioral and demographic variates, recently associated to covariation in brain function, can already be identified using only structural features, highlighting the importance of careful integration of structural features in any analysis of inter-individual differences in functional connectivity and downstream associations with behavioral/demographic variates.

[1]  Mark W. Woolrich,et al.  Multilevel linear modelling for FMRI group analysis using Bayesian inference , 2004, NeuroImage.

[2]  Stephen M Smith,et al.  The relationship between spatial configuration and functional connectivity of brain regions , 2017, bioRxiv.

[3]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[4]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[5]  B. Franke,et al.  Integrated analysis of gray and white matter alterations in attention-deficit/hyperactivity disorder , 2016, NeuroImage: Clinical.

[6]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[7]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[8]  Thomas E. Nichols,et al.  Rank-order versus mean based statistics for neuroimaging , 2007, NeuroImage.

[9]  M. Farah,et al.  Progress and challenges in probing the human brain , 2015, Nature.

[10]  D. Simpson,et al.  PHRENOLOGY AND THE NEUROSCIENCES: CONTRIBUTIONS OF F. J. GALL AND J. G. SPURZHEIM , 2005, ANZ journal of surgery.

[11]  Katrin Amunts,et al.  Cortical Folding Patterns and Predicting Cytoarchitecture , 2007, Cerebral cortex.

[12]  Mark W. Woolrich,et al.  Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure , 2012, NeuroImage.

[13]  L. Shah,et al.  Functional magnetic resonance imaging. , 2010, Seminars in roentgenology.

[14]  Stephen M. Smith,et al.  Multi-level block permutation , 2015, NeuroImage.

[15]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[16]  Karl J. Friston,et al.  Classical and Bayesian Inference in Neuroimaging: Applications , 2002, NeuroImage.

[17]  A. L. Arenas,et al.  Refinement by integration: aggregated effects of multimodal imaging markers on adult ADHD , 2017, Journal of psychiatry & neuroscience : JPN.

[18]  J. Fodor The Modularity of mind. An essay on faculty psychology , 1986 .

[19]  T. Paus,et al.  Studying neuroanatomy using MRI , 2017, Nature Neuroscience.

[20]  R. Collins What makes UK Biobank special? , 2012, The Lancet.

[21]  Stephen M. Smith,et al.  Permutation inference for the general linear model , 2014, NeuroImage.

[22]  Michael W. Spratling,et al.  Encyclopedia of Computational Neuroscience , 2013 .

[23]  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.

[24]  Christian Gaser,et al.  Brain structure in schizophrenia vs. psychotic bipolar I disorder: A VBM study , 2015, Schizophrenia Research.

[25]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[26]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

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

[28]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[29]  Margot J. Taylor,et al.  The centre of the brain: Topographical model of motor, cognitive, affective, and somatosensory functions of the basal ganglia , 2013, Human brain mapping.

[30]  Mark W. Woolrich,et al.  Linked independent component analysis for multimodal data fusion , 2011, NeuroImage.

[31]  David C. Van Essen,et al.  Human Connectome Project , 2014, Encyclopedia of Computational Neuroscience.

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

[33]  P. Matthews,et al.  A common brain network links development, aging, and vulnerability to disease , 2014, Proceedings of the National Academy of Sciences.

[34]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[35]  O. Andreassen,et al.  Dissociable diffusion MRI patterns of white matter microstructure and connectivity in Alzheimer’s disease spectrum , 2017, Scientific Reports.

[36]  Stephen J. Roberts,et al.  Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis , 2004, Deterministic and Statistical Methods in Machine Learning.

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

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

[39]  Aapo Hyvärinen,et al.  Pairwise likelihood ratios for estimation of non-Gaussian structural equation models , 2013, J. Mach. Learn. Res..

[40]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[41]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.