Missouri University of Science and Technology, Department of Mechanical and Aerospace Engineering, Rolla, MO, 65409, USA.
Sci Rep. 2019 Apr 5;9(1):5729. doi: 10.1038/s41598-019-42090-4.
Functional magnetic resonance imaging has revealed correlated activities in brain regions even in the absence of a task. Initial studies assumed this resting-state functional connectivity (FC) to be stationary in nature, but recent studies have modeled these activities as a dynamic network. Dynamic spatiotemporal models better model the brain activities, but are computationally more involved. A comparison of static and dynamic FCs was made to quantitatively study their efficacies in identifying intrinsic individual connectivity patterns using data from the Human Connectome Project. Results show that the intrinsic individual brain connectivity pattern can be used as a 'fingerprint' to distinguish among and identify subjects and is more accurately captured with partial correlation and assuming static FC. It was also seen that the intrinsic individual brain connectivity patterns were invariant over a few months. Additionally, biological sex identification was successfully performed using the intrinsic individual connectivity patterns, and group averages of male and female FC matrices. Edge consistency, edge variability and differential power measures were used to identify the major resting-state networks involved in identifying subjects and their sex.
功能磁共振成像已经揭示了即使在没有任务的情况下，大脑区域也存在相关活动。最初的研究假设这种静息状态功能连接（FC）是稳定的，但最近的研究已经将这些活动建模为一个动态网络。动态时空模型可以更好地模拟大脑活动，但计算量更大。通过比较静态和动态 FC，使用来自人类连接组计划的数据来定量研究它们在识别内在个体连接模式方面的功效。结果表明，内在个体脑连接模式可用作区分和识别受试者的“指纹”，并且通过部分相关和假设的静态 FC 更准确地捕捉到。还发现，内在个体脑连接模式在几个月内是不变的。此外，使用内在个体连接模式以及男性和女性 FC 矩阵的组平均值成功地进行了生物性别识别。边缘一致性、边缘可变性和差分功率测量用于识别涉及识别受试者及其性别的主要静息状态网络。
