Joint modeling of anatomical and functional connectivity for population studies.
Joint modeling of anatomical and functional connectivity for population studies.
复制标题
人群研究的解剖和功能连通性的联合建模。
DOI：
10.1109/tmi.2011.2166083
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发表时间：
2012-02
影响因子：
10.6
通讯作者：
Golland P
中科院分区：
文献类型：
--
作者：
Venkataraman A;Rathi Y;Kubicki M;Westin CF;Golland P
我们提出了一种新颖的概率框架，用于合并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，以识别大脑中的连接模式。特别是，我们模拟了潜在的解剖学和功能连接之间的相互作用，并为人口研究提供了直观的扩展。我们采用 EM 算法通过最大化数据似然来估计模型参数。该方法同时推断每个群体的潜在连接性模板以及群体之间连接性的差异。我们在精神分裂症研究中展示了我们的方法。我们的模型发现精神分裂症患者顶叶/后扣带回区域和额叶之间的功能连接显着增加，而顶叶/后扣带回区域和颞叶之间的功能连接性减少。我们进一步确定，我们的模型可以学习对照人群和临床人群之间的预测差异，并且将两种方式结合起来比单独考虑每种方式能产生更好的结果。
We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation.