基于多维连通性分析的贝叶斯预测建模。
Bayesian predictive modeling based on multidimensional connectivity profiling.
作者信息
Chen Rong, Herskovits Edward
机构信息
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, School of Medicine; Baltimore, Maryland, USA
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, School of Medicine; Baltimore, Maryland, USA.
出版信息
Neuroradiol J. 2015 Feb;28(1):5-11. doi: 10.15274/NRJ-2014-10111.
Dysfunction of brain structural and functional connectivity is increasingly being recognized as playing an important role in many brain disorders. Diffusion tensor imaging (DTI) and functional magnetic resonance (fMR) imaging are widely used to infer structural and functional connectivity, respectively. How to combine structural and functional connectivity patterns for predictive modeling is an important, yet open, problem. We propose a new method, called Bayesian prediction based on multidimensional connectivity profiling (BMCP), to distinguish subjects at the individual level based on structural and functional connectivity patterns. BMCP combines finite mixture modeling and Bayesian network classification. We demonstrate its use in distinguishing young and elderly adults based on DTI and resting-state fMR data.
大脑结构和功能连接的功能障碍在许多脑部疾病中发挥重要作用这一点日益得到认可。扩散张量成像（DTI）和功能磁共振成像（fMR）分别被广泛用于推断结构和功能连接。如何将结构和功能连接模式结合起来进行预测建模是一个重要但尚未解决的问题。我们提出了一种新方法，称为基于多维连接剖析的贝叶斯预测（BMCP），用于基于结构和功能连接模式在个体水平上区分受试者。BMCP结合了有限混合建模和贝叶斯网络分类。我们展示了其在基于DTI和静息态fMR数据区分年轻人和老年人方面的应用。
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