Structural MRI connectome in development: challenges of the changing brain.

MRI connectomics is an emerging approach to study the brain as a network of interconnected brain regions. Understanding and mapping the development of the MRI connectome may offer new insights into the development of brain connectivity and plasticity, ultimately leading to improved understanding of normal development and to more effective diagnosis and treatment of developmental disorders. In this review, we describe the attempts made to date to map the whole-brain structural MRI connectome in the developing brain and pay a special attention to the challenges associated with the rapid changes that the brain is undergoing during maturation. The two main steps in constructing a structural brain network are (i) choosing connectivity measures that will serve as the network "edges" and (ii) finding an appropriate way to divide the brain into regions that will serve as the network "nodes". We will discuss how these two steps are usually performed in developmental studies and the rationale behind different strategies. Changes in local and global network properties that have been described during maturation in neonates and children will be reviewed, along with differences in network topology between typically and atypically developing subjects, for example, owing to pre-mature birth or hypoxic ischaemic encephalopathy. Finally, future directions of connectomics will be discussed, addressing important steps necessary to advance the study of the structural MRI connectome in development.

[1]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[2]  Christopher P. Hess,et al.  A DTI-Based Template-Free Cortical Connectome Study of Brain Maturation , 2013, PloS one.

[3]  Jeremy D. Schmahmann,et al.  Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers , 2008, NeuroImage.

[4]  Patric Hagmann,et al.  Comparing connectomes across subjects and populations at different scales , 2013, NeuroImage.

[5]  B. Peterson,et al.  Normal Development of Brain Circuits , 2010, Neuropsychopharmacology.

[6]  E. Ziv,et al.  Brain without Anatomy: Construction and Comparison of Fully Network-Driven Structural MRI Connectomes , 2014, PloS one.

[7]  Giorgio M. Innocenti,et al.  The neocortex : ontogeny and phylogeny , 1991 .

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

[9]  E. Ziv,et al.  A Machine Learning Approach to Automated Structural Network Analysis: Application to Neonatal Encephalopathy , 2013, PloS one.

[10]  P. Hagmann From diffusion MRI to brain connectomics , 2005 .

[11]  Yaniv Assaf,et al.  Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.

[12]  Daniel Rueckert,et al.  Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest , 2008, NeuroImage.

[13]  Daniel Rueckert,et al.  Identifying population differences in whole-brain structural networks: A machine learning approach , 2010, NeuroImage.

[14]  Steven P. Miller,et al.  Tractography‐based quantitation of diffusion tensor imaging parameters in white matter tracts of preterm newborns , 2005, Journal of magnetic resonance imaging : JMRI.

[15]  Dinggang Shen,et al.  Altered Modular Organization of Structural Cortical Networks in Children with Autism , 2013, PloS one.

[16]  Dinggang Shen,et al.  Development Trends of White Matter Connectivity in the First Years of Life , 2011, PloS one.

[17]  P. Huttenlocher Synaptic density in human frontal cortex - developmental changes and effects of aging. , 1979, Brain research.

[18]  Alan C. Evans,et al.  Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI , 2006, NeuroImage.

[19]  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.

[20]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[21]  Stephen E. Rose,et al.  Diffusion MRI of the neonate brain: acquisition, processing and analysis techniques , 2012, Pediatric Radiology.

[22]  Roland G. Henry,et al.  Diffusion tensor imaging: serial quantitation of white matter tract maturity in premature newborns , 2004, NeuroImage.

[23]  Alan C. Evans,et al.  Developmental changes in organization of structural brain networks. , 2013, Cerebral cortex.

[24]  Carl-Fredrik Westin,et al.  In Vivo Visualization of White Matter Fiber Tracts of Preterm- and Term-Infant Brains With Diffusion Tensor Magnetic Resonance Imaging , 2005, Investigative radiology.

[25]  J. Giedd,et al.  Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging , 2006, Neuroscience & Biobehavioral Reviews.

[26]  Jonathan D. Power,et al.  The Development of Human Functional Brain Networks , 2010, Neuron.

[27]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[28]  H. Kraemer,et al.  How can we learn about developmental processes from cross-sectional studies, or can we? , 2000, The American journal of psychiatry.

[29]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[30]  Yong He,et al.  Development of human brain structural networks through infancy and childhood. , 2015, Cerebral cortex.

[31]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[32]  Abraham Z. Snyder,et al.  Functional connectivity MRI in infants: Exploration of the functional organization of the developing brain , 2011, NeuroImage.

[33]  Christopher P. Hess,et al.  Towards the “Baby Connectome”: Mapping the Structural Connectivity of the Newborn Brain , 2012, PloS one.

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

[35]  Carola van Pul,et al.  Fractional anisotropy in white matter tracts of very-low-birth-weight infants , 2007, Pediatric Radiology.

[36]  A. Snyder,et al.  Normal brain in human newborns: apparent diffusion coefficient and diffusion anisotropy measured by using diffusion tensor MR imaging. , 1998, Radiology.

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

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

[39]  O. Sporns,et al.  Network hubs in the human brain , 2013, Trends in Cognitive Sciences.

[40]  Dinggang Shen,et al.  Brain anatomical networks in early human brain development , 2011, NeuroImage.

[41]  Michael I. Miller,et al.  Multi-contrast human neonatal brain atlas: Application to normal neonate development analysis , 2011, NeuroImage.

[42]  Danielle S Bassett,et al.  Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.

[43]  Paul M. Thompson,et al.  Development of the “rich club” in brain connectivity networks from 438 adolescents & adults aged 12 to 30 , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[44]  Jun Li,et al.  Brain Anatomical Network and Intelligence , 2009, NeuroImage.

[45]  Olaf Sporns,et al.  Making sense of brain network data , 2013, Nature Methods.

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

[47]  Paul M. Thompson,et al.  Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults , 2013, NeuroImage.

[48]  Marcus Kaiser,et al.  A tutorial in connectome analysis: Topological and spatial features of brain networks , 2011, NeuroImage.

[49]  Lucie Hertz-Pannier,et al.  Assessment of the early organization and maturation of infants' cerebral white matter fiber bundles: A feasibility study using quantitative diffusion tensor imaging and tractography , 2006, NeuroImage.

[50]  Emma Muñoz-Moreno,et al.  Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome , 2012, NeuroImage.

[51]  J Mazziotta,et al.  A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[52]  P. Colditz,et al.  Assessment of Structural Connectivity in the Preterm Brain at Term Equivalent Age Using Diffusion MRI and T2 Relaxometry: A Network-Based Analysis , 2013, PloS one.

[53]  I. S. Gousias,et al.  Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth. , 2014, Cerebral cortex.

[54]  Paul M. Thompson,et al.  Mapping connectivity in the developing brain , 2013, International Journal of Developmental Neuroscience.

[55]  Derek K. Jones Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI , 2010 .

[56]  P. Hagmann,et al.  MR connectomics: a conceptual framework for studying the developing brain , 2012, Front. Syst. Neurosci..

[57]  H. Kinney,et al.  Sequence of Central Nervous System Myelination in Human Infancy. II. Patterns of Myelination in Autopsied Infants , 1988, Journal of neuropathology and experimental neurology.

[58]  M. P. van den Heuvel,et al.  The Ontogeny of the Human Connectome , 2013, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[59]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[60]  Daniel Rueckert,et al.  The influence of preterm birth on the developing thalamocortical connectome , 2013, Cortex.

[61]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[62]  O. Sporns,et al.  White matter maturation reshapes structural connectivity in the late developing human brain , 2010, Proceedings of the National Academy of Sciences.

[63]  Alessandro Vespignani,et al.  Detecting rich-club ordering in complex networks , 2006, physics/0602134.

[64]  Karina J. Kersbergen,et al.  On development of functional brain connectivity in the young brain , 2013, Front. Hum. Neurosci..

[65]  Petra S. Hüppi,et al.  Neuroimaging of cortical development and brain connectivity in human newborns and animal models , 2010, Journal of anatomy.

[66]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[67]  D. Levine,et al.  Cortical maturation in normal and abnormal fetuses as assessed with prenatal MR imaging. , 1999, Radiology.

[68]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[69]  J. Gilmore,et al.  Infant Brain Atlases from Neonates to 1- and 2-Year-Olds , 2011, PloS one.

[70]  Jean-Philippe Thiran,et al.  Structural connectomics in brain diseases , 2013, NeuroImage.

[71]  Hangyi Jiang,et al.  Pediatric diffusion tensor imaging: Normal database and observation of the white matter maturation in early childhood , 2006, NeuroImage.

[72]  Xiaoping Hu,et al.  The effects of connection reconstruction method on the interregional connectivity of brain networks via diffusion tractography , 2012, Human brain mapping.

[73]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[74]  S. Maier,et al.  Microstructural Development of Human Newborn Cerebral White Matter Assessed in Vivo by Diffusion Tensor Magnetic Resonance Imaging , 1998, Pediatric Research.

[75]  P. Basser,et al.  Axcaliber: A method for measuring axon diameter distribution from diffusion MRI , 2008, Magnetic resonance in medicine.