Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain.
Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain.
Sci Data. 2024 Feb 29;11(1):256. doi: 10.1038/s41597-024-03060-2.
The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.
人脑是一个极其复杂的结构和功能连接网络，在多个时空尺度上运作。研究这些多尺度连接之间的关系对于推进我们对大脑功能和障碍的理解至关重要。然而，准确地从功能对应物预测结构连接仍然是一个具有挑战性的追求。主要障碍之一是缺乏整合不同分辨率的结构和功能网络的公共存储库，以及模块转录组谱，这对于全面的生物学解释是必不可少的。为了减轻这一限制，我们的贡献包括提供一个开放访问的数据集，其中包含跨多个尺度的功能和结构连接的衍生矩阵，以及代码，以方便研究它们的相互关系。我们还提供了额外的资源，重点关注模块级网络指标的神经遗传关联，这为进一步推进网络神经科学领域的研究提供了有希望的机会，特别是在大脑障碍方面。
