National Institute of Mental Health, Klecany, Czech Republic.
Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Sci Data. 2022 Aug 9;9(1):486. doi: 10.1038/s41597-022-01596-9.
The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects.
人脑代表了一个复杂的计算系统，其功能和结构可以使用各种神经影像学技术来测量，这些技术专注于脑组织和活动的不同特性。我们使用概率扩散轨迹法来捕获通过扩散加权成像获得的白质纤维的组织。通过将轨迹的结果分割成更大的解剖学单位，可以推断出系统这些部分之间的结构关系。该流水线生成一个结构连接矩阵，其中包含所有区域之间连接强度的估计。然而，原始数据处理复杂、计算密集且需要专家质量控制，这可能会使该领域经验较少的研究人员望而却步。因此，我们以适合建模和分析的形式提供大脑结构连接矩阵，从而为广大科学家群体所使用。提供的数据集包含大脑结构连接矩阵以及基础的原始扩散和结构数据，以及 88 位健康受试者的基本人口统计学数据。
