A Library of Cortical Morphology Analysis Tools to Study Development, Aging and Genetics of Cerebral Cortex

Sharing of analysis techniques and tools is among the main driving forces of modern neuroscience. We describe a library of tools developed to quantify global and regional differences in cortical anatomy in high resolution structural MR images. This library is distributed as a plug-in application for popular structural analysis software, BrainVisa (BV). It contains tools to measure global and regional gyrification, gray matter thickness and sulcal and gyral white matter spans. We provide a description of each tool and examples for several case studies to demonstrate their use. These examples show how the BV library was used to study cortical folding process during antenatal development and recapitulation of this process during cerebral aging. Further, the BV library was used to perform translation research in humans and non-human primates on the genetics of cerebral gyrification. This library, including source code and self-contained binaries for popular computer platforms, is available from the NIH-Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) resource (http://www.nitrc.org/projects/brainvisa_ext).

[1]  I. Steiner,et al.  Progressive brain atrophy during normal aging in man: a quantitative computerized tomography study. , 1985, Israel journal of medical sciences.

[2]  P. Fox,et al.  Global spatial normalization of human brain using convex hulls. , 1999, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[3]  Norbert Schuff,et al.  Deformation tensor morphometry of semantic dementia with quantitative validation , 2004, NeuroImage.

[4]  Peter Kochunov,et al.  Mapping Primary Gyrogenesis During Fetal Development in Primate Brains: High-Resolution in Utero Structural MRI of Fetal Brain Development in Pregnant Baboons , 2010, Front. Neurosci..

[5]  Nancy C Andreasen,et al.  Sexual dimorphism in the human brain: evaluation of tissue volume, tissue composition and surface anatomy using magnetic resonance imaging , 2000, Psychiatry Research: Neuroimaging.

[6]  Peter Kochunov,et al.  Blood Pressure and Cerebral White Matter Share Common Genetic Factors in Mexican Americans , 2011, Hypertension.

[7]  Sang Won Seo,et al.  Sulcal morphology changes and their relationship with cortical thickness and gyral white matter volume in mild cognitive impairment and Alzheimer's disease , 2008, NeuroImage.

[8]  R. Woods,et al.  Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. , 2007, Cerebral cortex.

[9]  Peter Kochunov,et al.  Development of structural MR brain imaging protocols to study genetics and maturation. , 2010, Methods.

[10]  Peter Kochunov,et al.  Cortical sulci and bipolar disorder , 2006, Neuroreport.

[11]  Paul M. Thompson,et al.  Increased local gyrification mapped in Williams syndrome , 2006, NeuroImage.

[12]  Jean-Francois Mangin,et al.  Coordinate-based versus structural approaches to brain image analysis , 2004, Artif. Intell. Medicine.

[13]  Denis Rivière,et al.  Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations , 2008, NeuroImage.

[14]  Alan C. Evans,et al.  Genetic Contributions to Human Brain Morphology and Intelligence , 2006, The Journal of Neuroscience.

[15]  Colin Studholme,et al.  A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation , 2010, NeuroImage.

[16]  D. Louis Collins,et al.  Object-Based Strategy for Morphometry of the Cerebral Cortex , 2003, IPMI.

[17]  Paul M. Thompson,et al.  A curvature-based approach to estimate local gyrification on the cortical surface , 2006, NeuroImage.

[18]  R. Porcher,et al.  Cortical changes in cerebral small vessel diseases: a 3D MRI study of cortical morphology in CADASIL. , 2008, Brain : a journal of neurology.

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

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

[21]  I. Smart,et al.  Gyrus formation in the cerebral cortex in the ferret. I. Description of the external changes. , 1986, Journal of anatomy.

[22]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[23]  A. Toga,et al.  Three-Dimensional Statistical Analysis of Sulcal Variability in the Human Brain , 1996, The Journal of Neuroscience.

[24]  Faith M. Gunning-Dixon,et al.  Neuroanatomical correlates of cognitive aging: evidence from structural magnetic resonance imaging. , 1998, Neuropsychology.

[25]  D. Louis Collins,et al.  Object-based morphometry of the cerebral cortex , 2004, IEEE Transactions on Medical Imaging.

[26]  D. V. van Essen,et al.  A tension-based theory of morphogenesis and compact wiring in the central nervous system. , 1997, Nature.

[27]  Manuel Desco,et al.  Gyral and Sulcal Cortical Thinning in Adolescents with First Episode Early-Onset Psychosis , 2009, Biological Psychiatry.

[28]  T. Paus,et al.  Brain size and folding of the human cerebral cortex. , 2008, Cerebral cortex.

[29]  Elena Prieto,et al.  Cortical atrophy and language network reorganization associated with a novel progranulin mutation. , 2009, Cerebral cortex.

[30]  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.

[31]  J. Mazziotta,et al.  Age‐related morphology trends of cortical sulci , 2005, Human brain mapping.

[32]  D. V. von Cramon,et al.  Sulcal variability of twins. , 1999, Cerebral cortex.

[33]  Jack L. Lancaster,et al.  Processing speed is correlated with cerebral health markers in the frontal lobes as quantified by neuroimaging , 2010, NeuroImage.

[34]  P. Manger,et al.  Order‐specific quantitative patterns of cortical gyrification , 2007, The European journal of neuroscience.

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

[36]  Tyrone D. Cannon,et al.  Mapping genetic influences on human brain structure , 2002, Annals of medicine.

[37]  Po-Shan Wang,et al.  Morphological regionalization using fetal magnetic resonance images of normal developing brains , 2009, The European journal of neuroscience.

[38]  Y. Burnod,et al.  A morphogenetic model for the development of cortical convolutions. , 2005, Cerebral cortex.

[39]  Paul M. Thompson,et al.  Mapping hippocampal and ventricular change in Alzheimer disease , 2004, NeuroImage.

[40]  J. Hutsler,et al.  Comparative analysis of cortical layering and supragranular layer enlargement in rodent carnivore and primate species , 2005, Brain Research.

[41]  Paul M. Thompson,et al.  Relationship between white matter fractional anisotropy and other indices of cerebral health in normal aging: Tract-based spatial statistics study of aging , 2007, NeuroImage.

[42]  A. Schleicher,et al.  The human pattern of gyrification in the cerebral cortex , 2004, Anatomy and Embryology.

[43]  Tao Liu,et al.  The relationship between cortical sulcal variability and cognitive performance in the elderly , 2011, NeuroImage.

[44]  David C. Van Essen,et al.  Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..

[45]  Paul M. Thompson,et al.  Fractional anisotropy of cerebral white matter and thickness of cortical gray matter across the lifespan , 2011, NeuroImage.

[46]  Hsiao-Ying Wey,et al.  Baseline CBF, and BOLD, CBF, and CMRO2 fMRI of Visual and Vibrotactile Stimulations in Baboons , 2011, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[47]  Feng Gao,et al.  Retrospective motion correction protocol for high‐resolution anatomical MRI , 2006, Human brain mapping.

[48]  HighWire Press.  Philosophical Transactions of the Royal Society of London , 1781, The London Medical Journal.

[49]  P. Thompson,et al.  Relationship among neuroimaging indices of cerebral health during normal aging , 2008, Human brain mapping.

[50]  Stephen Lawrie,et al.  Pre-frontal lobe gyrification index in schizophrenia, mental retardation and comorbid groups: An automated study , 2007, NeuroImage.

[51]  Peter T. Fox,et al.  Can structural MRI indices of cerebral integrity track cognitive trends in executive control function during normal maturation and adulthood? , 2009, Human brain mapping.

[52]  Janice E. Knoefel,et al.  Clinical neurology of aging , 1985 .

[53]  T. Cizadlo,et al.  Quantitative in vivo measurement of gyrification in the human brain: changes associated with aging. , 1999, Cerebral cortex.

[54]  Isabelle Bloch,et al.  A generic framework for the parcellation of the cortical surface into gyri using geodesic Voronoı̈ diagrams , 2003, Medical Image Anal..

[55]  Paul M. Thompson,et al.  Genetics of Primary Cerebral Gyrification: Heritability of Length, Depth and Area of Primary Sulci in an Extended Pedigree of Papio Baboons , 2022 .

[56]  D. Head,et al.  Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. , 1997, Cerebral cortex.

[57]  Isabelle Bloch,et al.  From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations , 1995, Journal of Mathematical Imaging and Vision.

[58]  W. Welker,et al.  Why Does Cerebral Cortex Fissure and Fold ? A Review of Determinants of Gyri and Sulci , 2022 .

[59]  A. Schleicher,et al.  Gyrification in the cerebral cortex of primates. , 1989, Brain, behavior and evolution.

[60]  Edith V. Sullivan,et al.  A quantitative analysis of CT and cognitive measures in normal aging and Alzheimer's disease , 1990, Psychiatry Research: Neuroimaging.

[61]  Tao Liu,et al.  The effects of age and sex on cortical sulci in the elderly , 2010, NeuroImage.

[62]  Jean-Francois Mangin,et al.  Automatic recognition of cortical sulci of the human brain using a congregation of neural networks , 2002, Medical Image Anal..

[63]  Colin Studholme,et al.  Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change , 2006, IEEE Transactions on Medical Imaging.

[64]  H Regis,et al.  Computerized axial tomography. , 1976, Journal of neuroradiology. Journal de neuroradiologie.

[65]  V. Caviness,et al.  Mechanical model of brain convolutional development. , 1975, Science.

[66]  Colin Studholme,et al.  Measures for Characterizing Directionality Specific Volume Changes in TBM of Brain Growth , 2010, MICCAI.

[67]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[68]  Peter Kochunov,et al.  Heritability of brain volume, surface area and shape: An MRI study in an extended pedigree of baboons , 2007, Human brain mapping.

[69]  Peter Kochunov,et al.  Whole Brain and Regional Hyperintense White Matter Volume and Blood Pressure: Overlap of Genetic Loci Produced by Bivariate, Whole-Genome Linkage Analyses , 2010, Stroke.

[70]  K. Amunts,et al.  Interhemispheric asymmetry of the human motor cortex related to handedness and gender , 2000, Neuropsychologia.

[71]  J. Cheverud,et al.  Heritability of brain size and surface features in rhesus macaques (Macaca mulatta). , 1990, Journal of Heredity.

[72]  P Kochunov,et al.  Evaluation of octree regional spatial normalization method for regional anatomical matching , 2000, Human brain mapping.

[73]  D. Barker.  The developmental origins of well-being. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[74]  Anders M. Dale,et al.  Prospective motion correction of high-resolution magnetic resonance imaging data in children , 2010, NeuroImage.

[75]  P. Huttenlocher,et al.  Regional differences in synaptogenesis in human cerebral cortex , 1997, The Journal of comparative neurology.

[76]  R. Woods,et al.  Cortical change in Alzheimer's disease detected with a disease-specific population-based brain atlas. , 2001, Cerebral cortex.

[77]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[78]  D. V. von Cramon,et al.  Deep sulcal landmarks provide an organizing framework for human cortical folding. , 2008, Cerebral cortex.

[79]  R. Woods,et al.  Gender effects on cortical thickness and the influence of scaling , 2006, Human brain mapping.

[80]  Karl J. Friston,et al.  Automatic Differentiation of Anatomical Patterns in the Human Brain: Validation with Studies of Degenerative Dementias , 2002, NeuroImage.

[81]  P. Thompson,et al.  Neuroimaging endophenotypes: Strategies for finding genes influencing brain structure and function , 2007, Human brain mapping.

[82]  Peter Kochunov,et al.  Analysis of Genetic Variability and Whole Genome Linkage of Whole-Brain, Subcortical, and Ependymal Hyperintense White Matter Volume , 2009, Stroke.

[83]  Alan C. Evans,et al.  Statistical Sulcal Shape Comparisons: Application to the Detection of Genetic Encoding of the Central Sulcus Shape , 2000, NeuroImage.

[84]  Jack L. Lancaster,et al.  Loss of cerebral white matter structural integrity tracks the gray matter metabolic decline in normal aging , 2009, NeuroImage.

[85]  Arthur W. Toga,et al.  Anatomical Global Spatial Normalization , 2010, Neuroinformatics.

[86]  Katrin Amunts,et al.  The central sulcus: an observer-independent characterization of sulcal landmarks and depth asymmetry. , 2008, Cerebral cortex.

[87]  D. Louis Collins,et al.  Automated extraction and variability analysis of sulcal neuroanatomy , 1999, IEEE Transactions on Medical Imaging.

[88]  Paul M. Thompson,et al.  On the Genetic Architecture of Cortical Folding and Brain Volume in Primates , 2022 .

[89]  小野 道夫,et al.  Atlas of the Cerebral Sulci , 1990 .

[90]  Rachel Grossman,et al.  Quantitative MRI measurements of human fetal brain development in utero , 2006, NeuroImage.