Ge Bao, Tian Yin, Hu Xintao, Chen Hanbo, Zhu Dajiang, Zhang Tuo, Han Junwei, Guo Lei, Liu Tianming
Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi'an, China; School of Physics & Information Technology, Shaanxi Normal University, Xi'an, China.
Department of Communication, Xi'an Communications Institute, Xi'an, China.
PLoS One. 2015 Apr 13;10(4):e0118175. doi: 10.1371/journal.pone.0118175. eCollection 2015.
Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.
绘制人类脑网络为研究脑功能和功能障碍提供了基础，因此近年来受到了广泛关注。然而，对人类脑网络进行建模仍然面临若干挑战，包括在多个空间尺度上构建网络以及找到个体间共同对应的网络。因此，尽管脑网络本质上是多尺度的，但许多先前的方法都是针对单一分辨率或尺度的脑网络设计的。为了解决这个问题，本文提出了一种新方法，通过对我们最近开发并验证的基于密集个体化和共同连通性的皮质地标（DICCCOLs）应用改进的多尺度谱聚类，从扩散张量成像（DTI）数据构建多尺度共同结构脑网络。由于DICCCOL地标在个体和群体间具有内在的结构对应关系，我们采用多尺度谱聚类算法将DICCCOL地标及其连接分组为子网，同时保留多尺度上内在建立的对应关系。实验结果表明，所提出的方法能够生成跨个体的多尺度一致且共同的结构脑网络，并且其可重复性已通过多个独立数据集得到验证。作为一种应用，这些多尺度网络被用于指导多尺度纤维束的聚类，并比较精神分裂症患者和健康对照者的纤维完整性。总体而言，我们的方法为脑网络建模和基于纤维束的DTI数据分析提供了一个新颖且有效的框架。
