Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
IEEE Trans Med Imaging. 2012 Feb;31(2):164-82. doi: 10.1109/TMI.2011.2166083. Epub 2011 Aug 30.
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.
我们提出了一种新的概率框架，以融合扩散加权成像轨迹和静息态功能磁共振成像相关性的信息，从而识别大脑中的连接模式。具体来说，我们对潜在的解剖和功能连接之间的相互作用进行建模，并提出了一种直观的扩展，用于群体研究。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模板以及组间连接的差异。我们在精神分裂症研究中展示了我们的方法。我们的模型确定了精神分裂症患者顶叶/后扣带回区域与额叶之间的功能连接显著增加，而顶叶/后扣带回区域与颞叶之间的功能连接减少。我们进一步证明，我们的模型可以学习控制组和临床组之间的预测差异，并且将两种模式结合起来比单独考虑每一种模式效果更好。
