Comparison of cortical folding measures for evaluation of developing human brain

We evaluated 22 measures of cortical folding, 20 derived from local curvature (curvature-based measures) and two based on other features (sulcal depth and gyrification index), for their capacity to distinguish between normal and aberrant cortical development. Cortical surfaces were reconstructed from 12 term-born control and 63 prematurely-born infants. Preterm infants underwent 2-4 MR imaging sessions between 27 and 42weeks postmenstrual age (PMA). Term infants underwent a single MR imaging session during the first postnatal week. Preterm infants were divided into two groups. One group (38 infants) had no/minimal abnormalities on qualitative assessment of conventional MR images. The second group (25 infants) consisted of infants with injury on conventional MRI at term equivalent PMA. For both preterm infant groups, all folding measures increased or decreased monotonically with increasing PMA, but only sulcal depth and gyrification index differentiated preterm infants with brain injury from those without. We also compared scans obtained at term equivalent PMA (36-42weeks) for all three groups. No curvature-based measured distinguished between the groups, whereas sulcal depth distinguished term control from injured preterm infants and gyrification index distinguished all three groups. When incorporating total cerebral volume into the statistical model, sulcal depth no longer distinguished between the groups, though gyrification index distinguished between all three groups and positive shape index distinguished between the term control and uninjured preterm groups. We also analyzed folding measures averaged over brain lobes separately. These results demonstrated similar patterns to those obtained from the whole brain analyses. Overall, though the curvature-based measures changed during this period of rapid cerebral development, they were not sensitive for detecting the differences in folding associated with brain injury and/or preterm birth. In contrast, gyrification index was effective in differentiating these groups.

[1]  F Barkhof,et al.  Normal gyration and sulcation in preterm and term neonates: appearance on MR images. , 1996, Radiology.

[2]  Bruce Fischl,et al.  Gray matter myelination of 1555 human brains using partial volume corrected MRI images , 2015, NeuroImage.

[3]  F. Lazeyras,et al.  Primary cortical folding in the human newborn: an early marker of later functional development. , 2008, Brain : a journal of neurology.

[4]  A. James Barkovich,et al.  Malformations of cortical development and epilepsy , 2001, Brain and Development.

[5]  F. Di Salle,et al.  Brain Changes in Kallmann Syndrome , 2014, American Journal of Neuroradiology.

[6]  David C. Van Essen,et al.  A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex , 2005, NeuroImage.

[7]  M I Levene,et al.  Cerebral maturation in premature infants: quantitative assessment using MR imaging. , 2001, AJNR. American journal of neuroradiology.

[8]  David Van Essen,et al.  Regional impairments of cortical folding in premature infants , 2014, Annals of neurology.

[9]  Timothy S. Coalson,et al.  A Surface-Based Analysis of Hemispheric Asymmetries and Folding of Cerebral Cortex in Term-Born Human Infants , 2010, The Journal of Neuroscience.

[10]  F. Lazeyras,et al.  Mapping the early cortical folding process in the preterm newborn brain. , 2008, Cerebral cortex.

[11]  J. Gilmore,et al.  Mapping Longitudinal Development of Local Cortical Gyrification in Infants from Birth to 2 Years of Age , 2014, The Journal of Neuroscience.

[12]  F. Cowan,et al.  Reduced development of cerebral cortex in extremely preterm infants , 2000, The Lancet.

[13]  J. Hajnal,et al.  Abnormal Cortical Development after Premature Birth Shown by Altered Allometric Scaling of Brain Growth , 2006, PLoS medicine.

[14]  J. Neil,et al.  New MR Imaging Assessment Tool to Define Brain Abnormalities in Very Preterm Infants at Term , 2013, American Journal of Neuroradiology.

[15]  Andrea J. van Doorn,et al.  Surface shape and curvature scales , 1992, Image Vis. Comput..

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

[17]  Jean-Francois Mangin,et al.  Larger is twistier: Spectral analysis of gyrification (SPANGY) applied to adult brain size polymorphism , 2012, NeuroImage.

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

[19]  Fabio Mosca,et al.  Magnetic resonance imaging assessment of brain maturation in preterm neonates with punctate white matter lesions , 2007, Neuroradiology.

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

[21]  J. Lefévre,et al.  Are Developmental Trajectories of Cortical Folding Comparable Between Cross-sectional Datasets of Fetuses and Preterm Newborns? , 2016, Cerebral cortex.

[22]  W. Engle.  Age Terminology During the Perinatal Period , 2004, Pediatrics.

[23]  Jian Wang,et al.  Comparison of Medial Temporal Measures between Binswanger's Disease and Alzheimer's Disease , 2014, PloS one.

[24]  Colin Studholme,et al.  A framework for in vivo quantification of regional brain folding in premature neonates , 2008, NeuroImage.

[25]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[26]  Andrew K Knutsen,et al.  Spatial and temporal variations of cortical growth during gyrogenesis in the developing ferret brain. , 2013, Cerebral cortex.

[27]  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..

[28]  Lana Vasung,et al.  Insights from in vitro fetal magnetic resonance imaging of cerebral development. , 2009, Seminars in perinatology.

[29]  An allometric scaling relationship in the brain of preterm infants , 2014, Annals of clinical and translational neurology.

[30]  Meritxell Bach Cuadra,et al.  A Surface-Based Approach to Quantify Local , 2008 .

[31]  David J. Hawkes,et al.  Measures of folding applied to the development of the human fetal brain , 2002, IEEE Transactions on Medical Imaging.

[32]  D. V. van Essen,et al.  Symmetry of Cortical Folding Abnormalities in Williams Syndrome Revealed by Surface-Based Analyses , 2006, The Journal of Neuroscience.

[33]  D. V. van Essen,et al.  Structural and Functional Analyses of Human Cerebral Cortex Using a Surface-Based Atlas , 1997, The Journal of Neuroscience.

[34]  L. Ment,et al.  Imaging biomarkers of outcome in the developing preterm brain , 2009, The Lancet Neurology.

[35]  Suyash P. Awate,et al.  Cerebral cortical folding analysis with multivariate modeling and testing: Studies on gender differences and neonatal development , 2010, NeuroImage.

[36]  W. Welker.  Why Does Cerebral Cortex Fissure and Fold , 1990 .