Wang Chendi, Ng Bernard, Garbi Rafeef
University of British Columbia, Electrical and Computer Engineering , ICICS x421-2366 Main Mall , Vancouver, British Columbia, Canada , V6T 1Z4 ;
University of British Columbia, Department of Statistics , Vancouver, British Columbia, Canada ;
Brain Connect. 2018 Nov 30. doi: 10.1089/brain.2017.0576.
Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.
由于神经影像数据的高维度所带来的统计挑战、计算负担和解释困难，脑部分割通常是网络分析的先决条件。主要方法大多是单模态的，功能磁共振成像（fMRI）是主要使用的模态。因此，这些方法忽略了与脑组织结构相关的其他脑属性。在本文中，我们提出了一种整合fMRI和扩散磁共振成像（dMRI）数据的方法。我们的方法引入了两种模态连通性值之间的非线性映射，并基于体素级的重测信度自适应地平衡它们的权重。进一步提出了一种有效的区域级扩展方法，该方法额外纳入了脑回和脑沟的结构信息。为了进行验证，我们在人类连接组计划数据上，将多模态分割与单模态分割以及现有图谱进行了比较。我们表明，多模态分割具有更高的可重复性、相当/更高的功能同质性以及相当/更高的留一法数据似然性。观察到多模态脑区的边界与基于细胞结构的边界对齐，并且从多模态脑区提取的子网络与已建立的脑系统匹配良好。因此，我们的结果表明多模态信息改善了脑部分割。
