文章基本信息

标题：Human brain structural connectivity matrices–ready for modelling
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作者：antonín Škoch ; Barbora Rehák Bučková ; Jan Mareš 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-9
DOI：10.1038/s41597-022-01596-9
语种：English
出版社：Nature Publishing Group
摘要：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 fbers acquired by difusion-weighted imaging using probabilistic difusion 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 feld . 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 difusion and structural data, as well as basic demographic data of 88 healthy subjects .