None (v1.2)

Nowadays, the connectivity patterns in brain networks are of special interest, as they may reflect the communication in the brain at the structural and functional levels. Their extraction, however, is a complex process that requires a deep knowledge of the magnetic resonance imaging (MRI) data processing methods. Furthermore, there is no consensus as to which parcellation of the brain is most suitable for a given analysis. Therefore, in this dataset 20 different state-of-the-art brain parcellations were used to reconstruct the region-based empirical structural connectivity (representing the anatomical axonal tracts) and functional connectivity (representing the temporal correlation between neuronal activity of brain regions) from diffusion-weighted (dwMRI) and resting-state functional magnetic resonance imaging (fMRI) data, respectively. The repository provides individual connectomes for 200 subjects from the Human Connectome Project. The data can be used by members of the neuroimaging community to investigate the structural and functional human connectomes, and to extend the investigation to the whole-brain models for further analyses of the brain structure and function.

当下，脑网络的连接模式因其可反映大脑结构与功能层面的信息交流而备受关注。然而，其提取流程较为复杂，需掌握磁共振成像（magnetic resonance imaging, MRI）数据处理的专业知识。此外，针对特定分析任务，何种脑区划分方案最为适宜，目前尚未形成统一共识。因此，本数据集采用20种不同的前沿脑区划分方案，分别基于弥散加权磁共振成像（diffusion-weighted MRI, dwMRI）数据重建基于脑区的经验性结构连接组（对应解剖学轴突束），并基于静息态功能磁共振成像（resting-state functional magnetic resonance imaging, fMRI）数据重建功能连接组（对应脑区神经元活动的时间相关性）。该数据集仓库提供了来自人类连接组计划（Human Connectome Project）的200名受试者的个体连接组数据。神经影像学界的研究者可借助该数据集探究人类结构与功能连接组，并可将研究拓展至全脑模型，以进一步开展大脑结构与功能的相关分析。