We present a statistical framework that jointly models brain shape and functional connectivity, which are two complex aspects of the brain that have been classically studied independently. We adopt a Riemannian modeling approach to account for the non-Euclidean geometry of the space of shapes and the space of connectivity that constrains trajectories of co-variation to be valid statistical estimates. In order to disentangle genetic sources of variability from those driven by unique environmental factors, we embed a functional random effects model in the Riemannian framework. We apply the proposed model to the Human Connectome Project dataset to explore spontaneous co-variation between brain shape and connectivity in young healthy individuals.
翻译:我们提出了一个统计框架,共同模拟大脑形状和功能互联互通,这是大脑的两个复杂方面,这些是经过传统独立研究的大脑的两个复杂方面。我们采用了里伊曼式模型法,以说明形状空间和连接空间的非欧几何学,这限制了共同变量的轨迹,使之成为有效的统计估计。为了分离由独特环境因素驱动的变异的遗传来源,我们在里伊曼尼框架内嵌了一个功能随机效应模型。我们将拟议的模型应用于人类连接项目数据集,以探索大脑形状与年轻健康人之间自发的共变。