Elsevier

NeuroImage

Volume 60, Issue 2, 2 April 2012, Pages 1352-1366
NeuroImage

Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome

https://doi.org/10.1016/j.neuroimage.2012.01.059Get rights and content

Abstract

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5–10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the architecture of neural circuitry and developing imaging biomarkers of poor neurodevelopment outcome in infants with prenatal diseases.

Introduction

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5–10% of all pregnancies and it is a leading cause of fetal morbidity and mortality (Jarvis et al., 2003, Kady and Gardosi, 2004). Reduction of placental blood flow results in sustained exposure to hypoxemia and undernutrition (Baschat, 2004) and this has profound consequences on the developing brain (Rees et al., 2011). A substantial number of studies have described associations between IUGR and short (Bassan et al., 2011, Figueras et al., 2009) and long-term neurodevelopmental and cognitive dysfunctions (Bassan et al., 2011, Eixarch et al., 2008, Feldman and Eidelman, 2006, Geva et al., 2006a, Geva et al., 2006b, Leitner et al., 2007, McCarton et al., 1996, Scherjon et al., 2000). Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. This goal is currently hampered by the limited understanding of the brain reorganization processes leading to poor neurodevelopment in IUGR children and the lack of suitable imaging biomarkers in fetal or early life.

In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects. Studies in term neonates have reported decreased volume in gray matter (Tolsa et al., 2004) and hippocampus (Lodygensky et al., 2008), and major delays in cortical development, with discordant patterns of gyrification and a pronounced reduction in cortical expansion (Dubois et al., 2008). Persistence of structural changes at 1-year of age has been recently reported demonstrating reduced volumes of gray matter (GM) in the temporal, parietal, frontal, and insular regions (Padilla et al., 2011) and decrease in fractal dimension of both gray and white matter (WM) which correlate with specific developmental difficulties (Esteban et al., 2010). Despite these studies are useful to demonstrate disease-related differences, the ability to use MRI information to generate individual predictive biomarkers in IUGR is limited.

Recent research suggests that MRI in vivo access to brain connectivity might help understanding cognitive and neurodevelopment processes (Sporns et al., 2005). Connectomics (Hagmann, 2005) is an emerging approach to extract information from different modalities, including MRI data, exhaustively mapping inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network or connectome (Bullmore and Sporns, 2009, Sporns et al., 2005). Particularly, connectomics extracts a number of “image features” after intensive processing that integrates structural information of the individual, related to anatomical brain regions and neuronal connectivity, to compute adjacency matrices that represent brain networks or graph models of a particular subject brain. Connectomics provides a framework to compare architecture of brain circuits among different individuals in a direct and elegant manner. In order to assess brain organization, graph theory tools have been proposed to allow quantifying brain network infrastructure, integration and segregation of the global functioning of a brain network (Rubinov and Sporns, 2009). Of further interest is the ability to explore regional differences by assessing graph theory characteristics of the regional networks associated to a given region.

Connectomics has been successfully utilized in different sets of data, including functional MRI, structural MRI and diffusion MRI, to report altered group connectivity parameters in adults and adolescents undergoing diseases such as schizophrenia (Alexander-Bloch et al., 2010, Bassett et al., 2008, Liu et al., 2008), Parkinson's disease (Wu et al., 2009), Alzheimer's disease (He et al., 2008, He et al., 2009a, Lo et al., 2010), attention deficit hyperactivity disorder (Wang et al., 2009), and in non-clinical samples as in the study of synesthesia (Hänggi et al., 2011). These studies suggest the potential of MRI based connectomics to develop biomarkers for disease diagnosis and treatment effects monitoring. Among the above MRI modalities diffusion imaging can be of particular interest for the study of the developing brain. Diffusion MRI allows non-invasively assessing in-vivo WM fiber orientation in the brain. In recent years diffusion MRI based connectomics has been used to construct structural brain networks in healthy populations (Gong et al., 2009, Hagmann et al., 2008, Iturria-Medina et al., 2008), being its network properties associated with sex and brain size (Yan et al., 2010), intelligence (Li et al., 2009) and specific cognitive abilities in old age (Wen et al., 2011) and to report altered group network topology features in Alzheimer's disease (Lo et al., 2010, Wee et al., 2010), multiple sclerosis (Shu et al., 2011), schizophrenia (Wang et al., 2012) and early blindness (Shu et al., 2009). Connectomics from diffusion MRI has been applied to assess normal development of the human brain during childhood and adolescence, including subjects from 2 to 18 years of age (Hagmann et al., 2010). Diffusion MRI connectomics in younger children has been used to study a healthy longitudinal cohort of 2 weeks, 1 year and 2 years of age (Yap et al., 2011), but no studies in infants with perinatal conditions have been conducted.

In the present study, we evaluated the hypothesis that diffusion MRI based connectomics could determine quantifiable changes resulting from the existence of brain reorganization in children who suffered IUGR. We used diffusion MRI based connectomics to obtain structural brain networks in one year old infants with and without growth restriction. We analyzed global and regional graph theory features of brain networks explored in previous studies such as infrastructure, integration, segregation and centrality. We evaluated the ability of diffusion MRI based connectomics to demonstrate group differences in global brain network features and localize altered regional networks. Finally, we also explored whether brain network features at one year would be associated with neurodevelopmental outcome at two years of age.

Section snippets

Subjects

This study was part of a larger prospective research program on IUGR involving fetal assessment and short- and long-term postnatal follow-up at Hospital Clinic (Barcelona-Spain). The study design involved recruitment of a consecutive sample of 83 fetuses: 42 IUGR singleton infants and 41 control fetuses appropriate for gestational age. All individuals were born between October 2007 and November 2009. IUGR was defined as a fetal estimated weight below 10th centile according to local reference

Basic clinical features in the study population

Structural MRI evaluation revealed the presence of anomalies in 8 IUGR (two increased cisterna magna, four ventricular dilatations and two WM lesions) and in 5 controls (two increased cisterna magna and three ventricular dilatations) that were excluded from the final analysis. In addition, 4 controls and 10 IUGR did not pass quality criteria concerning motion artifacts or correct tractography reconstruction that prevented performing further analysis. Thus, the final sample included 24 IUGR

Discussion

This study describes the use of diffusion MRI based connectomics in one-year-old infants, with particular focus on the analysis of structural brain networks. The study provides evidence that diffusion MRI based connectomics can demonstrate large-scale brain reorganization of the structural brain network in one year old children who suffered IUGR by means of global as well as regional brain network feature analysis. Finally, it was demonstrated that structural brain network features evaluated at

Conclusion

In conclusion, MRI connectomics is an emerging technique that is suitable for the assessment of brain reorganization in IUGR infants by means of global and regional graph theory based network features, which are related to different levels of organizational complexity. We could demonstrate altered brain network topology in one-year-old IUGR infants and its association with abnormal performance in neurodevelopmental scales (BSID-III) at two years of age. Larger studies are required to validate

Acknowledgments

This work was supported by grants: Obra Social "la Caixa", Barcelona, Spain; The Cerebra Foundation for the Brain-Injured Child, Carmarthen, Wales, UK; The Thrasher Research Fund, Salt Lake City, USA; Rio Hortega and Sara Borrell grants from Carlos III Institute of Health, Spain (CM08/00105, to E.E., CM11/00032 to M.I. and CD11/00048 to E.M.); Emili Letang fellowship by Hospital Clinic, Barcelona, Spain (to M.I.) and Marie Curie Industry-Academia Partnerships and Pathways (IAPP) (

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