功能磁共振成像/扩散加权成像联合生成模型及其在人群研究中的应用
Joint generative model for fMRI/DWI and its application to population studies.
作者信息
Venkataraman Archana, Rathi Yogesh, Kubicki Marek, Westin Carl-Fredrik, Golland Polina
机构信息
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
出版信息
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):191-9. doi: 10.1007/978-3-642-15705-9_24.
Abstract
We propose a novel probabilistic framework to merge information from DWI tractography and resting-state fMRI correlations. In particular, we model the interaction of latent anatomical and functional connectivity templates between brain regions and present an intuitive extension to population studies. We employ a mean-field approximation to fit the new model to the data. The resulting algorithm identifies differences in latent connectivity between the groups. We demonstrate our method on a study of normal controls and schizophrenia patients.
摘要
我们提出了一种新颖的概率框架，用于融合来自扩散加权成像纤维束成像和静息态功能磁共振成像相关性的信息。具体而言，我们对脑区之间潜在的解剖和功能连接模板的相互作用进行建模，并为群体研究提供了一种直观的扩展。我们采用平均场近似将新模型拟合到数据中。所得算法可识别组间潜在连接的差异。我们在一项针对正常对照和精神分裂症患者的研究中展示了我们的方法。
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