Tuesday, February 07, 2012

PLoS ONE - Towards the “Baby Connectome”: Mapping the Structural Connectivity of the Newborn Brain


Very cool - if we can begin to map and understand the connectome of the infant brain, and find some form of average, we are that much closer to being able to see how "nurture," or the environment, impacts brain development into adulthood. This may allow us to identify how attachment changes the brain, how neglect changes the brain, and how abuse changes the brain in very distinct ways.

Towards the “Baby Connectome”: Mapping the Structural Connectivity of the Newborn Brain

Olga Tymofiyeva1*, Christopher P. Hess1, Etay Ziv1, Nan Tian1, Sonia L. Bonifacio2, Patrick S. McQuillen2, Donna M. Ferriero2, A. James Barkovich1, Duan Xu1

1 Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America, 2 Department of Pediatrics, University of California San Francisco, San Francisco, California, United States of America

Abstract

Defining the structural and functional connectivity of the human brain (the human “connectome”) is a basic challenge in neuroscience. Recently, techniques for noninvasively characterizing structural connectivity networks in the adult brain have been developed using diffusion and high-resolution anatomic MRI. The purpose of this study was to establish a framework for assessing structural connectivity in the newborn brain at any stage of development and to show how network properties can be derived in a clinical cohort of six-month old infants sustaining perinatal hypoxic ischemic encephalopathy (HIE). Two different anatomically unconstrained parcellation schemes were proposed and the resulting network metrics were correlated with neurological outcome at 6 months. Elimination and correction of unreliable data, automated parcellation of the cortical surface, and assembling the large-scale baby connectome allowed an unbiased study of the network properties of the newborn brain using graph theoretic analysis. In the application to infants with HIE, a trend to declining brain network integration and segregation was observed with increasing neuromotor deficit scores.
Citation: Tymofiyeva O, Hess CP, Ziv E, Tian N, Bonifacio SL, et al. (2012). Towards the “Baby Connectome”: Mapping the Structural Connectivity of the Newborn Brain. PLoS ONE, 7(2): e31029. doi:10.1371/journal.pone.0031029

Introduction

During brain maturation, structural and functional pathways are formed and reshaped in cases of prenatal, perinatal or early childhood brain injury. Studying these pathways in vivo remains a challenge. With advances in MRI, it has become possible over the last decade to noninvasively characterize large white matter bundles using diffusion MRI. The technique has been widely applied to both the adult and the baby brain [1], and has led to new insights into the tissue microstructure of individual tracts. Tractography has been extensively used to visualize white matter tracts and offer tract-based regional analyses. More recently, studies in the adult brain [2][4] have attempted to provide a more complete description of the brain's structural connectivity by assembling the “connectome,” a term introduced by Sporns et al. [5] in analogy to the human genome. In these recent studies, the analysis included not only single tracks and regions-of-interest (ROIs) but also the whole brain structural network topology, as assessed at the scale possible using diffusion MRI techniques. The brain network describes interregional mesoscale connectivity patterns of the brain and can be represented by the connectivity matrix (also called “adjacency matrix”) of size n2, where n is the number of brain regions (nodes). Graph theoretic analysis can be applied to the connectivity matrices in order to extract important network characteristics [6], [7]. Key concepts to describe and quantify complex brain networks include local topological parameters, such as node centrality, and global (aggregate) parameters, such as characteristic path length and average clustering coefficient that in concert may indicate the presence of so called “small-world” network characteristics [8]. Studying the human connectome using network science offers a unique opportunity to better understand inter-individual differences in neural connectivity.

The purpose of this study was to establish a framework for assessing structural connectivity in the newborn brain at any stage of development, starting with premature neonates, and to show how such a framework could be used to characterize structural network properties in a cohort of six-month old infants with hypoxic ischemic encephalopathy (HIE). Babies with neonatal encephalopathy face a much higher risk of neurological and developmental deficits that are difficult to predict on an individual basis [9]. Characterization of individual structural connectivity networks, together with conventional anatomic MRI imaging, may provide valuable anticipatory information about the potential for encountering abnormalities at a later stage in development. Our hypothesis in this work was that the topological trajectory of the baby brain network is altered by perinatal HIE, and as a result the observed clinical severity of injury would correlate to different structural network phenotypes at 6 months.

Imaging newborn infants poses several unique technical challenges. For reliable structural connectivity network construction and characterization, the following issues had to be addressed:
  1. - data quality assurance. Data quality suffers from bulk motion, particularly in unsedated infants. Therefore, it is necessary to analyze the occurrence of corrupted diffusion-weighted images and develop an algorithm for their correction or rejection, as in the case of information loss due to motion during half-Fourier acquisition [10]
  2. - automated and unbiased definition of network nodes of the connectome. Another challenge that had to be addressed for the proposed work was the need for an automated and yet unbiased cortical parcellation scheme suitable for objective evaluation in the developing brain. No single universally accepted parcellation scheme currently exists for human brain regions [5]. In previous studies of the adult human brain [3], [4], [11], [12] parcellation of the brain into nodes was based on anatomic templates and landmarks or functional architecture. Also, a recent study of white matter connectivity in the first years of life [13] used an anatomic template to map the brain at ages of 2 weeks, 1 year, and 2 years. We believe that rapidly changing newborn brains require an unbiased parcellation scheme that does not rely on (adult) brain atlases. This is crucial for the design of both cross-sectional and longitudinal brain imaging studies during the course of development in order to account for the neural plasticity of the pediatric brain. As a part of this work, we propose two different template-free parcellation schemes, and demonstrate their relationship to derived brain network parameters in infants after neonatal HIE.

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