Sadeghi Neda, Prastawa Marcel, Gilmore John H, Lin Weili, Gerig Guido
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah 84112.
Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599.
Conf Rec Asilomar Conf Signals Syst Comput. 2010;2010:777-781. doi: 10.1109/ACSSC.2010.5757670.
Analysis of human brain development is a crucial step for improved understanding of neurodevelopmental disorders. We focus on normal brain development as is observed in the multimodal longitudinal MRI/DTI data of neonates to two years of age. We present a spatio-temporal analysis framework using Gompertz function as a population growth model with three different spatial localization strategies: voxel-based, data driven clustering and atlas driven regional analysis. Growth models from multimodal imaging channels collected at each voxel form feature vectors which are clustered using the Dirichlet Process Mixture Models (DPMM). Clustering thus combines growth information from different modalities to subdivide the image into voxel groups with similar properties. The processing generates spatial maps that highlight the dynamic progression of white matter development. These maps show progression of white matter maturation where primarily, central regions mature earlier compared to the periphery, but where more subtle regional differences in growth can be observed. Atlas based analysis allows a quantitative analysis of a specific anatomical region, whereas data driven clustering identifies regions of similar growth patterns. The combination of these two allows us to investigate growth patterns within an anatomical region. Specifically, analysis of anterior and posterior limb of internal capsule show that there are different growth trajectories within these anatomies, and that it may be useful to divide certain anatomies into subregions with distinctive growth patterns.
对人类大脑发育的分析是增进对神经发育障碍理解的关键一步。我们专注于在新生儿至两岁的多模态纵向MRI/DTI数据中观察到的正常大脑发育情况。我们提出了一个时空分析框架，使用冈珀茨函数作为种群增长模型，并采用三种不同的空间定位策略：基于体素的、数据驱动聚类和图谱驱动区域分析。在每个体素处收集的多模态成像通道的生长模型形成特征向量，这些特征向量使用狄利克雷过程混合模型（DPMM）进行聚类。聚类从而将来自不同模态的生长信息结合起来，将图像细分为具有相似属性的体素组。该处理生成突出白质发育动态进展的空间图。这些图显示了白质成熟的进展，主要是中央区域比周边区域成熟得更早，但在生长方面可以观察到更细微的区域差异。基于图谱的分析允许对特定解剖区域进行定量分析，而数据驱动聚类则识别具有相似生长模式的区域。这两者的结合使我们能够研究解剖区域内的生长模式。具体而言，对内囊前肢和后肢的分析表明，这些解剖结构内存在不同的生长轨迹，并且将某些解剖结构划分为具有独特生长模式的子区域可能是有用的。
