Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps

The trajectory of the developing brain is characterized by a sequence of complex, nonlinear patterns that occur at systematic stages of maturation. Although significant prior neuroimaging research has shed light on these patterns, the challenge of accurately characterizing brain maturation, and identifying areas of accelerated or delayed development, remains. Altered brain development, particularly during the earliest stages of life, is believed to be associated with many neurological and neuropsychiatric disorders. In this work, we develop a framework to construct voxel‐wise estimates of brain age based on magnetic resonance imaging measures sensitive to myelin content. 198 myelin water fraction (VFM) maps were acquired from healthy male and female infants and toddlers, 3 to 48 months of age, and used to train a sigmoidal‐based maturational model. The validity of the approach was then established by testing the model on 129 different VFM datasets. Results revealed the approach to have high accuracy, with a mean absolute percent error of 13% in males and 14% in females, and high predictive ability, with correlation coefficients between estimated and true ages of 0.945 in males and 0.94 in females. This work represents a new approach toward mapping brain maturity, and may provide a more faithful staging of brain maturation in infants beyond chronological or gestation‐corrected age, allowing earlier identification of atypical regional brain development. Hum Brain Mapp 36:1233–1244, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

[1]  Nadim Joni Shah,et al.  Fully-automated detection of cerebral water content changes: Study of age- and gender-related H2O patterns with quantitative MRI , 2006, NeuroImage.

[2]  Michael D. Greicius,et al.  Development of functional and structural connectivity within the default mode network in young children , 2010, NeuroImage.

[3]  Mark H. Johnson,et al.  Mapping Infant Brain Myelination with Magnetic Resonance Imaging , 2011, The Journal of Neuroscience.

[4]  Cedric E. Ginestet,et al.  White matter development and early cognition in babies and toddlers , 2014, Human brain mapping.

[5]  E. Courchesne,et al.  Brain growth across the life span in autism: Age-specific changes in anatomical pathology , 2011, Brain Research.

[6]  Eileen Luders,et al.  Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI , 2012, NeuroImage.

[7]  Jonathan O'Muircheartaigh,et al.  Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping , 2012, NeuroImage.

[8]  P. Yakovlev,et al.  The myelogenetic cycles of regional maturation of the brain , 1967 .

[9]  Shun Xu,et al.  Multivariate nonlinear mixed model to analyze longitudinal image data: MRI study of early brain development , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[10]  S. Bradley-Johnson Mullen Scales of Early Learning , 1997 .

[11]  Alan C. Evans,et al.  Maturation of white matter in the human brain: a review of magnetic resonance studies , 2001, Brain Research Bulletin.

[12]  Christian Gaser,et al.  Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2012 .

[13]  A. Toga,et al.  Mapping brain maturation , 2006, Trends in Neurosciences.

[14]  Dinggang Shen,et al.  Morphological classification of brains via high-dimensional shape transformations and machine learning methods , 2004, NeuroImage.

[15]  Benjamin Gompertz,et al.  On the Nature of the Function Expressive of the Law of Human Mortality , 1815 .

[16]  Daniel P. Kennedy,et al.  Mapping Early Brain Development in Autism , 2007, Neuron.

[17]  R. Fields,et al.  White matter in learning, cognition and psychiatric disorders , 2008, Trends in Neurosciences.

[18]  J K Smith,et al.  Temporal and Spatial Development of Axonal Maturation and Myelination of White Matter in the Developing Brain , 2008, American Journal of Neuroradiology.

[19]  D. Norman,et al.  Normal maturation of the neonatal and infant brain: MR imaging at 1.5 T. , 1988, Radiology.

[20]  T. Martin American Guidance Service , 2014 .

[21]  Mark H. Johnson,et al.  Processes of change in brain and cognitive development , 2005, Trends in Cognitive Sciences.

[22]  B. Wandell,et al.  Children's Reading Performance is Correlated with White Matter Structure Measured by Diffusion Tensor Imaging , 2005, Cortex.

[23]  Lindsay Walker,et al.  Modeling healthy male white matter and myelin development: 3 through 60 months of age , 2014, NeuroImage.

[24]  C. Lebel,et al.  Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood , 2011, The Journal of Neuroscience.

[25]  S. Deoni,et al.  Correction of main and transmit magnetic field (B0 and B1) inhomogeneity effects in multicomponent‐driven equilibrium single‐pulse observation of T1 and T2 , 2011, Magnetic resonance in medicine.

[26]  D. Louis Collins,et al.  Application of Information Technology: A Four-Dimensional Probabilistic Atlas of the Human Brain , 2001, J. Am. Medical Informatics Assoc..

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

[28]  Suzanne E. Welcome,et al.  Mapping cortical change across the human life span , 2003, Nature Neuroscience.

[29]  Alfred Anwander,et al.  Neuroanatomical prerequisites for language functions in the maturing brain. , 2011, Cerebral cortex.

[30]  S. Deoni,et al.  Interactions between White Matter Asymmetry and Language during Neurodevelopment , 2013, The Journal of Neuroscience.

[31]  J. Hesselink,et al.  Neurological and MRI profiles of children with developmental language impairment , 2000, Developmental medicine and child neurology.

[32]  Shannon H Kolind,et al.  One component? Two components? Three? The effect of including a nonexchanging “free” water component in multicomponent driven equilibrium single pulse observation of T1 and T2 , 2013, Magnetic resonance in medicine.

[33]  Holly Dirks,et al.  Pediatric neuroimaging using magnetic resonance imaging during non-sedated sleep , 2013, Pediatric Radiology.

[34]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[35]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[36]  Vicente L. Malave,et al.  Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity , 2012, Neuroscience & Biobehavioral Reviews.

[37]  Benjamin Gompertz,et al.  XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. In a letter to Francis Baily, Esq. F. R. S. &c , 1825, Philosophical Transactions of the Royal Society of London.

[38]  S. Jacobson,et al.  Iron deficiency and infant motor development. , 2008, Early human development.

[39]  Richard N Aslin,et al.  Is myelination the precipitating neural event for language development in infants and toddlers? , 2006, Neurology.

[40]  Paul M. Thompson,et al.  Sexual dimorphism of brain developmental trajectories during childhood and adolescence , 2007, NeuroImage.

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

[42]  John H. Gilmore,et al.  Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain , 2013, NeuroImage.

[43]  Alan C. Evans,et al.  Brain development during childhood and adolescence: a longitudinal MRI study , 1999, Nature Neuroscience.

[44]  J. Kiefer,et al.  Sequential minimax search for a maximum , 1953 .

[45]  B. J. Casey,et al.  Imaging the developing brain: what have we learned about cognitive development? , 2005, Trends in Cognitive Sciences.

[46]  Guido Gerig,et al.  Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. , 2012, The American journal of psychiatry.

[47]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[48]  Vijay K. Venkatraman,et al.  Neuroanatomical Assessment of Biological Maturity , 2012, Current Biology.

[49]  B. Rutt,et al.  Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state , 2003, Magnetic resonance in medicine.

[50]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[51]  J. Pujol,et al.  Myelination of language-related areas in the developing brain , 2006, Neurology.

[52]  Rebecca S. Samson,et al.  Intra and inter-site reproducibility of myelin water volume fraction values derived using McDESPOT. , 2009 .

[53]  Khader M Hasan,et al.  Human brain atlas‐based volumetry and relaxometry: Application to healthy development and natural aging , 2010, Magnetic resonance in medicine.

[54]  J. Rapoport,et al.  Structural MRI of Pediatric Brain Development: What Have We Learned and Where Are We Going? , 2010, Neuron.

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

[56]  Rhoshel K. Lenroot,et al.  Sex differences in the adolescent brain , 2010, Brain and Cognition.

[57]  R. Poldrack,et al.  Microstructure of Temporo-Parietal White Matter as a Basis for Reading Ability Evidence from Diffusion Tensor Magnetic Resonance Imaging , 2000, Neuron.

[58]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.