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Joint Modeling of Anatomical and Functional Connectivity for Population Studies

机译：人口研究的解剖学和功能连接的联合建模

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摘要

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.
机译：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术和静止状态功能磁共振成像相关性的信息，以识别大脑中的连通性模式。特别是，我们对潜在的解剖学和功能连接之间的相互作用进行建模，并提出了对人群研究的直观扩展。我们采用EM算法通过最大化数据似然来估计模型参数。该方法同时推断出每个群体的潜在连通性模板以及各组之间的连通性差异。我们在精神分裂症研究中证明了我们的方法。我们的模型确定了精神分裂症的顶叶/后扣带区域与额叶之间的功能连通性显着增加以及顶/后扣带区域与颞叶之间的功能连通性降低。我们进一步确定，我们的模型可了解对照人群和临床人群之间的预测差异，并且与孤立地考虑每种模式相比，将两种模式组合起来可获得更好的结果。

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