Neurobiological origin of spurious brain morphological changes: A quantitative MRI study

The high gray‐white matter contrast and spatial resolution provided by T1‐weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1‐weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1‐weighted images (R1 (=1/T1), R2*, and PD) in a large cohort of healthy subjects (n = 120, aged 18–87 years). Synthetic T1‐weighted images were calculated from these quantitative maps and used to extract morphometry features—gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue—myelination, iron, and water content—on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801–1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

[1]  Robert Turner,et al.  Comparing Like with Like: The Power of Knowing Where You Are , 2014, Brain Connect..

[2]  John Ashburner,et al.  Computational anatomy with the SPM software. , 2009, Magnetic resonance imaging.

[3]  Karl J. Friston,et al.  MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: a prospective longitudinal study , 2013, The Lancet Neurology.

[4]  Oliver Speck,et al.  Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system , 2006, NeuroImage.

[5]  Julien Cohen-Adad,et al.  In vivo histology of the myelin g-ratio with magnetic resonance imaging , 2015, NeuroImage.

[6]  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.

[7]  J. Ghika,et al.  Paleoneurology: neurodegenerative diseases are age-related diseases of specific brain regions recently developed by Homo sapiens. , 2008, Medical hypotheses.

[8]  Christian Enzinger,et al.  Dynamics of brain iron levels in multiple sclerosis , 2015, Neurology.

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

[10]  P. Dechent,et al.  Increased SNR and reduced distortions by averaging multiple gradient echo signals in 3D FLASH imaging of the human brain at 3T , 2009, Journal of magnetic resonance imaging : JMRI.

[11]  Pierre-Louis Bazin,et al.  Anatomically motivated modeling of cortical laminae , 2014, NeuroImage.

[12]  Ralf Deichmann,et al.  Improved visibility of brain tumors in synthetic MP‐RAGE anatomies with pure T1 weighting , 2015, NMR in biomedicine.

[13]  André J. W. van der Kouwe,et al.  Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast , 2009, NeuroImage.

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

[15]  Pierre-Louis Bazin,et al.  Multi-contrast multi-scale surface registration for improved alignment of cortical areas , 2015, NeuroImage.

[16]  K. Uğurbil,et al.  Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo , 2007, Magnetic resonance in medicine.

[17]  Bruce Fischl,et al.  Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops , 2007, IEEE Transactions on Medical Imaging.

[18]  D. Aarsland,et al.  Grey Matter Changes in Cognitively Impaired Parkinson's Disease Patients , 2014, PloS one.

[19]  Robert Turner,et al.  Myelin and iron concentration in the human brain: A quantitative study of MRI contrast , 2014, NeuroImage.

[20]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[21]  Thomas H. B. FitzGerald,et al.  Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging , 2014, Neurobiology of Aging.

[22]  Nick C Fox,et al.  Computer-assisted imaging to assess brain structure in healthy and diseased brains , 2003, The Lancet Neurology.

[23]  Gen Sobue,et al.  Age-related changes of the myelinated fibers in the human corticospinal tract: a quantitative analysis , 2004, Acta Neuropathologica.

[24]  Arno Villringer,et al.  Advanced MRI techniques to improve our understanding of experience-induced neuroplasticity , 2016, NeuroImage.

[25]  P. Scheltens,et al.  A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging , 1993, Journal of the Neurological Sciences.

[26]  A. Dale,et al.  High consistency of regional cortical thinning in aging across multiple samples. , 2009, Cerebral cortex.

[27]  Maria Grazia Bruzzone,et al.  Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects. , 2009, Radiology.

[28]  R. Turner,et al.  Microstructural Parcellation of the Human Cerebral Cortex – From Brodmann's Post-Mortem Map to in vivo Mapping with High-Field Magnetic Resonance Imaging , 2011, Front. Hum. Neurosci..

[29]  Jean-Francois Mangin,et al.  R2* mapping for brain iron: associations with cognition in normal aging , 2015, Neurobiology of Aging.

[30]  Larson J. Hogstrom,et al.  The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. , 2013, Cerebral cortex.

[31]  M. Fukunaga,et al.  Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast , 2010, Proceedings of the National Academy of Sciences.

[32]  Derek K. Jones,et al.  Gleaning multicomponent T1 and T2 information from steady‐state imaging data , 2008, Magnetic resonance in medicine.

[33]  Nikolaus Weiskopf,et al.  An evaluation of prospective motion correction (PMC) for high resolution quantitative MRI , 2015, Front. Neurosci..

[34]  Anders M. Dale,et al.  A hybrid approach to the Skull Stripping problem in MRI , 2001, NeuroImage.

[35]  P. Hof,et al.  Cytoarchitecture of the human cerebral cortex: MR microscopy of excised specimens at 9.4 Tesla. , 2002, AJNR. American journal of neuroradiology.

[36]  Chris I. Baker,et al.  Teaching an adult brain new tricks: A critical review of evidence for training-dependent structural plasticity in humans , 2013, NeuroImage.

[37]  R. Deichmann,et al.  Influence of RF spoiling on the stability and accuracy of T1 mapping based on spoiled FLASH with varying flip angles , 2009, Magnetic resonance in medicine.

[38]  Nikolaus Weiskopf,et al.  Structure predicts function: Combining non-invasive electrophysiology with in-vivo histology , 2015, NeuroImage.

[39]  Nikolaus Weiskopf,et al.  Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation , 2013, Front. Neurosci..

[40]  Christine L. Tardif,et al.  A subject-specific framework for in vivo myeloarchitectonic analysis using high resolution quantitative MRI , 2016, NeuroImage.

[41]  Julien Cohen-Adad,et al.  What can we learn from T2* maps of the cortex? , 2014, NeuroImage.

[42]  Anders M. Dale,et al.  Consistent neuroanatomical age-related volume differences across multiple samples , 2011, Neurobiology of Aging.

[43]  Robert J. Zatorre,et al.  Predispositions and Plasticity in Music and Speech Learning: Neural Correlates and Implications , 2013, Science.

[44]  Kristian Bredies,et al.  Fast quantitative susceptibility mapping using 3D EPI and total generalized variation , 2015, NeuroImage.

[45]  J. Mugler,et al.  Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE) , 1990, Magnetic resonance in medicine.

[46]  Arvid Lundervold,et al.  Evaluation of automated brain MR image segmentation and volumetry methods , 2009, Human brain mapping.

[47]  F. Dick,et al.  In Vivo Functional and Myeloarchitectonic Mapping of Human Primary Auditory Areas , 2012, The Journal of Neuroscience.

[48]  P. Dechent,et al.  Quantitative FLASH MRI at 3T using a rational approximation of the Ernst equation , 2008, Magnetic resonance in medicine.

[49]  Richard S. Frackowiak,et al.  Disentangling in vivo the effects of iron content and atrophy on the ageing human brain , 2014, NeuroImage.

[50]  Anders M. Dale,et al.  Sequence-independent segmentation of magnetic resonance images , 2004, NeuroImage.

[51]  Richard S. Frackowiak,et al.  Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps , 2009, NeuroImage.

[52]  D. Louis Collins,et al.  Sensitivity of voxel-based morphometry analysis to choice of imaging protocol at 3 T , 2009, NeuroImage.

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

[54]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[55]  B. Pakkenberg,et al.  Aging and the human neocortex , 2003, Experimental Gerontology.

[56]  F. Dick,et al.  Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers , 2015, Front. Neurosci..

[57]  K. Uğurbil,et al.  Layer-Specific fMRI Reflects Different Neuronal Computations at Different Depths in Human V1 , 2012, PloS one.

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

[59]  Lawrence L. Wald,et al.  Laminar analysis of 7T BOLD using an imposed spatial activation pattern in human V1 , 2010, NeuroImage.

[60]  S. Lui,et al.  Regional increases of cortical thickness in untreated, first-episode major depressive disorder , 2014, Translational Psychiatry.

[61]  Juliane Dinse,et al.  A computational framework for ultra-high resolution cortical segmentation at 7Tesla , 2014, NeuroImage.

[62]  Steen Moeller,et al.  T 1 weighted brain images at 7 Tesla unbiased for Proton Density, T 2 ⁎ contrast and RF coil receive B 1 sensitivity with simultaneous vessel visualization , 2009, NeuroImage.

[63]  F. Dick,et al.  Mapping the Human Cortical Surface by Combining Quantitative T1 with Retinotopy† , 2012, Cerebral cortex.

[64]  Antoine Lutti,et al.  Computational anatomy for studying use-dependant brain plasticity , 2014, Front. Hum. Neurosci..

[65]  Steen Moeller,et al.  Evaluation of slice accelerations using multiband echo planar imaging at 3T , 2013, NeuroImage.

[66]  Christian Langkammer,et al.  MRI for Iron Mapping in Alzheimer's Disease , 2013, Neurodegenerative Diseases.

[67]  H. Lee Seldon.  Extended neocortical maturation time encompasses speciation, fatty acid and lateralization theories of the evolution of schizophrenia and creativity. , 2007, Medical hypotheses.

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

[69]  Bernhard Preim,et al.  A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI , 2015, NeuroImage.

[70]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.

[71]  John W. Harwell,et al.  Similar patterns of cortical expansion during human development and evolution , 2010, Proceedings of the National Academy of Sciences.

[72]  M. Feldman,et al.  Loss of dendritic spines in aging cerebral cortex , 1975, Anatomy and Embryology.

[73]  A. Lutti,et al.  A General Linear Relaxometry Model of R1 Using Imaging Data , 2014, Magnetic resonance in medicine.

[74]  N. K. Focke,et al.  Multi-site voxel-based morphometry — Not quite there yet , 2011, NeuroImage.

[75]  Jeff H. Duyn,et al.  Susceptibility contrast in high field MRI of human brain as a function of tissue iron content , 2009, NeuroImage.

[76]  L. Westlye,et al.  Differential Longitudinal Changes in Cortical Thickness, Surface Area and Volume across the Adult Life Span: Regions of Accelerating and Decelerating Change , 2014, The Journal of Neuroscience.

[77]  Jörn Diedrichsen,et al.  A spatially unbiased atlas template of the human cerebellum , 2006, NeuroImage.

[78]  S. T. Govindarajan,et al.  Beyond focal cortical lesions in MS , 2015, Neurology.

[79]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[80]  H. Seldon,et al.  Does brain white matter growth expand the cortex like a balloon? Hypothesis and consequences , 2005, Laterality.

[81]  J. Finsterbusch,et al.  Optimization and Validation of Methods for Mapping of the Radiofrequency Transmit Field at 3T , 2010, Magnetic resonance in medicine.

[82]  O. Josephs,et al.  Robust and Fast Whole Brain Mapping of the RF Transmit Field B1 at 7T , 2012, PloS one.

[83]  A. Lutti,et al.  Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. , 2015, Current opinion in neurology.

[84]  Richard S. Frackowiak,et al.  Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ) , 2011, NeuroImage.

[85]  Nick C Fox,et al.  Magnetic resonance imaging evidence for presymptomatic change in thalamus and caudate in familial Alzheimer’s disease , 2013, Brain : a journal of neurology.

[86]  Thomas E. Nichols,et al.  Common genetic variants influence human subcortical brain structures , 2015, Nature.

[87]  B. Hallgren,et al.  THE EFFECT OF AGE ON THE NON‐HAEMIN IRON IN THE HUMAN BRAIN , 1958, Journal of neurochemistry.

[88]  I. McKeith,et al.  Cerebral atrophy in Parkinson's disease with and without dementia: a comparison with Alzheimer's disease, dementia with Lewy bodies and controls. , 2004, Brain : a journal of neurology.

[89]  Hong Yu,et al.  Region of interest template for the human basal ganglia: Comparing EPI and standardized space approaches , 2008, NeuroImage.

[90]  R. Turner,et al.  Optimization of 3-D MP-RAGE Sequences for Structural Brain Imaging , 2000, NeuroImage.

[91]  S. Ropele,et al.  Quantitative MR imaging of brain iron: a postmortem validation study. , 2010, Radiology.

[92]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[93]  Ferath Kherif,et al.  In vivo assessment of use-dependent brain plasticity—Beyond the “one trick pony” imaging strategy , 2013, NeuroImage.

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

[95]  P. Dechent,et al.  High‐resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI , 2008, Magnetic resonance in medicine.

[96]  R A Knight,et al.  MR imaging of human brain at 3.0 T: preliminary report on transverse relaxation rates and relation to estimated iron content. , 1999, Radiology.

[97]  Nikolaus Weiskopf,et al.  Using high-resolution quantitative mapping of R1 as an index of cortical myelination , 2014, NeuroImage.