Department of Maternal-Fetal Medicine, Institut Clinic de Ginecologia, Obstetricia i Neonatologia-ICGON, Hospital Clinic and Institut d'Investigacions Biomediques August Pi i Sunyer- IDIBAPS, University of Barcelona, Barcelona, Spain.
Neuroimage. 2012 Apr 2;60(2):1352-66. doi: 10.1016/j.neuroimage.2012.01.059. Epub 2012 Jan 18.
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.
由于胎盘功能不全导致的宫内生长受限（IUGR）影响了所有妊娠的 5-10%，并与广泛的短期和长期神经发育障碍有关。预测 IUGR 的神经发育结局是现代胎儿医学和儿科学的临床挑战之一。近年来，许多研究使用磁共振成像（MRI）来证明 IUGR 受试者大脑结构的差异，但 MRI 用于 IUGR 个体预测的能力有限。最近的研究表明，MRI 活体脑连接评估可能有助于了解认知和神经发育过程。具体来说，基于 MRI 的连接组学是一种从 MRI 数据中提取信息的新兴方法，它可以全面描绘大脑内区域间的连接，构建大脑神经网络的图形模型，即大脑网络。在本研究中，我们使用基于弥散 MRI 的连接组学方法获得了一岁婴儿的前瞻性队列（32 名对照组和 24 名 IUGR 组）的结构脑网络，并通过脑网络的全局和区域图论特征分析 IUGR 组中白质连接电路的可量化脑重组。基于脑网络拓扑的全局和区域分析，我们证明了 IUGR 婴儿在一岁时存在脑重组。具体来说，IUGR 婴儿的全局和局部加权效率降低，区域图论特征改变。通过二项逻辑回归，我们还证明了连接测量与两年后贝利婴幼儿发育量表第三版（BSID-III）评估的神经发育结局异常有关。这些发现表明，基于弥散 MRI 的连接组学和基于图论的网络特征具有估计产前疾病婴儿神经回路结构差异和开发神经发育不良成像生物标志物的潜力。
