Rekik Islem, Li Gang, Lin Weili, Shen Dinggang
BASIRA lab, CVIP group, School of Science and Engineering, Computing, University of Dundee, UK.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:985-989. doi: 10.1109/ISBI.2018.8363736. Epub 2018 May 24.
brain connectomics have heavily relied on using functional and diffusion Magnetic Resonance Imaging (MRI) modalities to examine functional and structural relationships between pairs of anatomical regions in the brain. However, research work on brain (i.e., shape-to-shape) connections, which can be derived from T1-w and T2-w MR images, in both typical and atypical development or ageing is very scarce. Furthermore, the brain cannot be only regarded as a static shape, since it is a dynamic complex system that changes at functional, structural and morphological levels. Hence, examining the 'connection' between brain and its changes with time (e.g., ) may help advance our understanding of connectomic brain dynamics as well as disorders that may affect it. To address these limitations, we unprecedentedly introduce two shape and growth connectivity analysis tools that further extend the field of connectomics to brain morphology and dynamics: . Specifically, for a population of anatomically labelled shapes, the identifies a network of anatomical shape regions that are connected when morphologically similar at a single timepoint, whereas the identifies anatomical shape regions that elicit similar evolution dynamics across successive timepoints. These proposed tools can be easily invested to examine how a baseline shape influences its deformation trajectory at later timepoints using longitudinal shape data. We evaluated these tools on 23 infants, with right and left cortical surfaces reconstructed at birth, 3, 6, 9 and 12 months of age. Investigating the relationship between the and the (from birth to 1 year of age) gave insights into brain connectivity at birth and how it develops over time.
脑连接组学在很大程度上依赖于使用功能磁共振成像和扩散磁共振成像（MRI）模态来检查大脑中各对解剖区域之间的功能和结构关系。然而，关于在典型和非典型发育或衰老过程中，可从T1加权和T2加权MR图像得出的脑（即形状与形状之间）连接的研究工作非常稀少。此外，大脑不能仅仅被视为一个静态形状，因为它是一个在功能、结构和形态层面都会发生变化的动态复杂系统。因此，研究大脑形状及其随时间的变化（例如）之间的“连接”，可能有助于推进我们对连接组脑动力学以及可能影响它的疾病的理解。为了解决这些局限性，我们前所未有的引入了两种形状和生长连通性分析工具，它们将连接组学领域进一步扩展到脑形态学和动力学：。具体来说，对于一组经解剖学标记的形状，在单个时间点形态相似时，识别出相互连接的解剖形状区域网络，而则识别出在连续时间点引发相似演化动力学的解剖形状区域。利用纵向形状数据，这些提出的工具可以很容易地用于研究基线形状如何影响其在后续时间点的变形轨迹。我们在23名婴儿身上评估了这些工具，这些婴儿在出生时、3个月、6个月、9个月和12个月大时重建了左右皮质表面。研究出生至1岁期间和之间的关系，有助于深入了解出生时的脑连接性及其随时间的发展情况。
