Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering
Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering
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DOI：
10.1007/978-3-642-33454-2_37
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发表时间：
2013-09-01
影响因子：
10.6
通讯作者：
Liu, Tianming
中科院分区：
文献类型：
--
作者：
Chen, Hanbo;Li, Kaiming;Liu, Tianming
基于弥散张量成像（DTI）/功能磁共振成像（fMRI）数据的结构和功能脑网络的定量建模和分析近年来受到了广泛的关注。然而，这些结构或功能性大脑网络在多种神经成像方式和个体之间的规律性在很大程度上是未知的。本文提出了一种新的方法来推断组明智的一致的大脑子网络从多模态DTI/fMRI数据集通过多视图谱聚类的皮层网络，这是我们最近开发的和广泛验证的大规模皮层地标。我们将该算法应用于40名健康受试者的80个多模态结构和功能脑网络，并获得了组内一致的多模态脑子网络。基于弥散张量成像（DTI）和功能磁共振成像（fMRI）数据的脑结构和功能网络的定量建模和分析近年来受到了广泛的关注。然而，这些结构和功能的大脑网络在多种神经成像模式和不同个体之间的规律性在很大程度上是未知的。本文提出了一种新的方法来推断组一致的大脑子网络从多模态DTI/静息状态fMRI数据集通过多视图谱聚类的皮层网络，这是我们最近开发和验证的大规模皮层地标-DIC-CCOL（密集个性化和共同的基于连接的皮层地标）。我们将这些算法应用于100名健康年轻女性和50名健康年轻男性的DTI数据，在多个组内和组间获得了一致的多模态脑网络，并进一步研究了这些网络的功能作用。我们的实验结果表明，衍生的大脑网络有显着改善的模态间和主体间的一致性。
Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI)/functional MRI (fMRI) data has received extensive interest recently. However, the regularity of these structural or functional brain networks across multiple neuroimaging modalities and across individuals is largely unknown. This paper presents a novel approach to infer group-wise consistent brain sub-networks from multimodal DTI/fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed on our recently developed and extensively validated large-scale cortical landmarks. We applied the proposed algorithm on 80 multimodal structural and functional brain networks of 40 healthy subjects, and obtained consistent multimodal brain sub-networks within the group. Our experiments demonstrated that the derived brain sub-networks have improved inter-modality and inter-subject consistency.Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks-DIC-CCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.