Langs Georg, Lashkari Danial, Sweet Andrew, Tie Yanmei, Rigolo Laura, Golby Alexandra J, Golland Polina
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Inf Process Med Imaging. 2011;22:135-46. doi: 10.1007/978-3-642-22092-0_12.
In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a low-dimensional embedding space derived from a diffusion process on a graph that represents correlations of fMRI time courses. The atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects.
在本文中，我们构建了一个图谱，该图谱从一群个体中捕捉认知过程的功能特征。功能连接性编码在一个低维嵌入空间中，该空间源自对表示功能磁共振成像（fMRI）时间序列相关性的图上的扩散过程。该图谱由所有受试者嵌入的fMRI信号的共同先验分布表示。该图谱不直接与解剖空间耦合，并且可以表示其空间分布可变的功能网络。我们推导了一种算法，用于将这种生成模型拟合到群体中的观测数据。我们在一项语言fMRI研究中的结果表明，该方法能够识别不同受试者之间连贯且功能等效的区域。
