标题： 解耦脑图：关于网络和连接分析混淆的一点说明 显示英文标题

标题： Disentangling Brain Graphs: A Note on the Conflation of Network and Connectivity Analyses

摘要： 理解人脑仍然是生物医学科学的圣杯，而且可以说是所有科学领域的圣杯。 我们的大脑代表了世界上最为复杂的系统（一些人认为是宇宙），由近一千亿个神经元组成，它们之间有 septillions 种可能的连接。 这些连接的结构产生了一个高效的分层系统，能够实现意识，以及复杂的思想、情感和行为。 在过去十年中，脑连接性和网络分析迅速发展，因为它们有助于我们理解正常和异常的大脑功能。 功能性连接（FC）分析研究指定脑体素或区域之间的时间序列对的功能关联。 脑网络分析是连接性分析的一个独立子领域，在该领域中，所有时间序列对的关联都被量化，以创建大脑的相互连接表示（一个脑网络），这使得可以研究其系统属性。 虽然连接性分析是网络分析的基础，但这两个研究领域之间的细微区别在文献中通常被忽视，经常被当作同义词使用。 然而，开发更有用的分析方法并允许更精确的生物学解释需要区分这两个互补的领域。 显示英文摘要

摘要： Understanding the human brain remains the Holy Grail in biomedical science, and arguably in all of the sciences. Our brains represent the most complex systems in the world (and some contend the universe) comprising nearly one hundred billion neurons with septillions of possible connections between them. The structure of these connections engenders an efficient hierarchical system capable of consciousness, as well as complex thoughts, feelings, and behaviors. Brain connectivity and network analyses have exploded over the last decade due to their potential in helping us understand both normal and abnormal brain function. Functional connectivity (FC) analysis examines functional associations between time series pairs in specified brain voxels or regions. Brain network analysis serves as a distinct subfield of connectivity analysis in which associations are quantified for all time series pairs to create an interconnected representation of the brain (a brain network), which allows studying its systemic properties. While connectivity analyses underlie network analyses, the subtle distinction between the two research areas has generally been overlooked in the literature, with them often being referred to synonymously. However, developing more useful analytic methods and allowing for more precise biological interpretations requires distinguishing these two complementary domains.