Abstract：
Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation.
摘要：
通过大数据分析和计算机模拟等新方法进行非侵入性脑分析的进步对于解释脑功能和相关病理至关重要。在这项研究中,我们使用新的组代理数据生成模型（GSDGM）将向量自回归代理技术从单个多变量时间序列扩展到组数据。这种方法使我们能够生成生物学上合理的人脑动力学，代表从人类Connectome项目获得的大型人类静息状态（rs-fMRI）数据集。同时,我们定义了一种新的相似性度量，称为多变量时间序列集合相似性得分(MTESS)。MTESS在受试者识别中表现出很高的准确性和测量，它可以直接比较两个多元时间序列之间的相似性。我们使用MTESS分析了人类和Marmosetrs-fMRI数据。我们的结果显示皮质和皮质下区域之间的相似性差异。我们还进行了MTESS和单代理技术和组代理技术之间的状态转换分析，并证实了群体替代方法可以生成似是而非的群体质心多元时间序列。最后,我们使用GSDGM和MTESS对人类rs-fMRI数据进行指纹分析，成功区分正常会话和异常会话。这些新技术将用于临床应用和计算机模拟。
