IEEE J Biomed Health Inform. 2019 Jul;23(4):1479-1489. doi: 10.1109/JBHI.2018.2854659. Epub 2018 Jul 9.
Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies. With the development of functional magnetic resonance imaging (fMRI), further exploration of brain connectivity was made possible. Region-based networks are commonly used for mapping brain connectivity. However, they fail to illustrate the connectivity within regions of interest (ROIs) and lose precise location information. Voxel-based networks provide higher precision, but are difficult to construct and interpret due to the high dimensionality of the data. In this paper, we adopt a novel high-dimensional Gaussian graphical model - ψ-learning method, which can help ease computational burden and provide more accurate inference for the underlying networks. This method has been proven to be an equivalent measure of the partial correlation coefficient and, thus, is flexible for network comparison through statistical tests. The fMRI data we used were collected by the mind clinical imaging consortium using an auditory task in which there are 92 SZ patients and 116 healthy controls. We compared the networks at three different scales by using global measurements, community structure, and edge-wise comparisons within the networks. Our results reveal, at the highest voxel resolution, sets of distinct aberrant patterns for the SZ patients, and more precise local structures are provided within ROIs for further investigation.
精神分裂症（SZ）是一种慢性且严重的精神障碍，会影响一个人的思维、感觉和行为方式。有人提出，这种疾病与大脑连接的中断有关，许多研究已经验证了这一点。随着功能磁共振成像（fMRI）的发展，对大脑连接的进一步探索成为可能。基于区域的网络通常用于绘制大脑连接图。然而，它们无法说明感兴趣区域（ROI）内的连接，并且会丢失精确的位置信息。体素基于网络提供了更高的精度，但由于数据的高维性，构建和解释起来很困难。在本文中，我们采用了一种新颖的高维高斯图模型——ψ 学习方法，该方法可以帮助缓解计算负担，并为潜在网络提供更准确的推断。该方法已被证明是偏相关系数的等效度量，因此可以通过统计检验灵活地进行网络比较。我们使用的 fMRI 数据是由思维临床成像联盟使用听觉任务收集的，其中包括 92 名 SZ 患者和 116 名健康对照者。我们通过全局测量、社区结构和网络内的边缘比较，在三个不同的尺度上比较了网络。我们的结果揭示了在最高体素分辨率下，SZ 患者存在一系列独特的异常模式，并且在 ROI 内提供了更精确的局部结构，以便进一步研究。
