Conti Allegra, Duggento Andrea, Guerrisi Maria, Passamonti Luca, Indovina Iole, Toschi Nicola
Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy.
Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy.
Entropy (Basel). 2019 Jul 6;21(7):661. doi: 10.3390/e21070661.
A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.
越来越多的研究聚焦于估计和分析人类大脑功能连接组的方法。图论测量方法通常用于解释和综合复杂的网络相关信息。虽然静息态功能磁共振成像（rsfMRI）在此背景下经常被使用，但众所周知，它的可重复性较差，而这一关键因素在使用连接组学相关测量作为生物标志物的典型队列研究中通常被忽视。我们旨在通过分析和比较在一个大型（n = 1003）年轻健康受试者数据库中，经过连续四次rsfMRI扫描的连接矩阵以及图论测量的受试者间和受试者内变异性，来填补这一空白。我们分析了有向（格兰杰因果关系和转移熵）和无向（皮尔逊相关和偏相关）时间序列关联测量以及相关的全局和局部图论测量。虽然矩阵权重在无向方法中比在有向方法中表现出更高的可重复性，但在查看全局图指标时这种差异消失了，并且在局部图指标中又表现出强烈的区域依赖性。我们的结果提醒在解释连接性研究时要谨慎，并通过为有向和无向连接组测量中的受试者间和受试者内变异性提供定量估计，为未来的研究提供了一个基准。
