College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 210029, China.
Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University Nanjing, 210029, China.
Med Image Anal. 2020 Oct;65:101755. doi: 10.1016/j.media.2020.101755. Epub 2020 Jun 17.
Brain networks based on various neuroimaging technologies, such as diffusion tensor image (DTI) and functional magnetic resonance imaging (fMRI), have been widely applied to brain disease analysis. Currently, there are several node-level structural measures (e.g., local clustering coefficients and node degrees) for representing and analyzing brain networks since they usually can reflect the topological structure of brain regions. However, these measures typically describe specific types of structural information, ignoring important network properties (i.e., small structural changes) that could further improve the performance of brain network analysis. To overcome this problem, in this paper, we first define a novel node-level structure embedding and alignment (nSEA) representation to accurately characterize the node-level structural information of the brain network. Different from existing measures that characterize a specific type of structural properties with a single value, our proposed nSEA method can learn a vector representation for each node, thus contain richer structure information to capture small structural changes. Furthermore, we develop an nSEA representation based learning (nSEAL) framework for brain disease analysis. Specifically, we first perform structural embedding to calculate node vector representations for each brain network and then align vector representations of all brain networks into the common space for two group-level network analyses, including a statistical analysis and brain disease classifications. Experiment results on a real schizophrenia dataset demonstrate that our proposed method not only discover disease-related brain regions that could help to better understand the pathology of brain diseases, but also improve the classification performance of brain diseases, compared with state-of-the-art methods.
基于各种神经影像学技术的大脑网络，如弥散张量成像（DTI）和功能磁共振成像（fMRI），已广泛应用于大脑疾病分析。目前，有几种节点级别的结构测量方法（例如，局部聚类系数和节点度）用于表示和分析大脑网络，因为它们通常可以反映大脑区域的拓扑结构。然而，这些方法通常描述特定类型的结构信息，忽略了可能进一步提高大脑网络分析性能的重要网络特性（即小结构变化）。为了解决这个问题，在本文中，我们首先定义了一种新的节点级结构嵌入和对齐（nSEA）表示方法，以准确刻画大脑网络的节点级结构信息。与仅用一个值来描述特定类型结构特性的现有方法不同，我们提出的 nSEA 方法可以为每个节点学习一个向量表示，从而包含更丰富的结构信息来捕捉小结构变化。此外，我们开发了一种基于 nSEA 表示的学习（nSEAL）框架用于大脑疾病分析。具体来说，我们首先进行结构嵌入，为每个大脑网络计算节点向量表示，然后将所有大脑网络的向量表示对齐到公共空间中，进行两种组级网络分析，包括统计分析和大脑疾病分类。在一个真实的精神分裂症数据集上的实验结果表明，与最先进的方法相比，我们提出的方法不仅发现了与疾病相关的大脑区域，有助于更好地理解大脑疾病的病理，而且还提高了大脑疾病的分类性能。
