Paldino Michael J, Chu Zili D, Chapieski Mary L, Golriz Farahnaz, Zhang Wei
1 Department of Radiology, Texas Children's Hospital, Houston, TX, USA.
2 Department of Pediatric Medicine, Texas Children's Hospital, Houston, TX, USA.
Br J Radiol. 2017 Jun;90(1074):20160656. doi: 10.1259/bjr.20160656. Epub 2017 May 23.
To measure the repeatability of metrics that quantify brain network architecture derived from resting-state functional MRI in a cohort of paediatric patients with epilepsy.
We identified patients with: (1) epilepsy; (2) brain MRI at 3 T; (3) two identical resting-state functional MRI acquisitions performed on the same day. Undirected, weighted networks were constructed based on the resting-state time series using a range of processing parameters including parcellation size and graph threshold. The following topological properties were calculated: degree, strength, characteristic path length, global efficiency, clustering coefficient, modularity and small worldness. Based on repeated measures, we then calculated: (1) Pearson correlation coefficient; (2) intraclass correlation coefficient; (3) root-mean-square coefficient of variation; (4) repeatability coefficient; and (5) 95% confidence limits for change.
26 patients were included (age range: 4-21 years). Correlation coefficients demonstrated a highly consistent relationship between repeated observations for all metrics, and the intraclass correlation coefficients were generally in the excellent range. Repeatability in the data set was not significantly influenced by parcellation size. However, trends towards decreased repeatability were observed at higher graph thresholds.
These findings demonstrate the reliability of network metrics in a cohort of paediatric patients with epilepsy. Advances in knowledge: Our results point to the potential for graph theoretical analyses of resting-state data to provide reliable markers of network architecture in children with epilepsy. At the level of an individual patient, change over time greater than the repeatability coefficient or 95% confidence limits for change is unlikely to be related to intrinsic variability of the method.
测量量化癫痫患儿静息态功能磁共振成像（fMRI）所衍生脑网络结构的指标的可重复性。
我们纳入了符合以下条件的患者：（1）患有癫痫；（2）接受过3T脑磁共振成像（MRI）检查；（3）在同一天进行了两次相同的静息态功能MRI扫描。基于静息态时间序列，使用包括脑区划分大小和图谱阈值等一系列处理参数构建无向加权网络。计算以下拓扑属性：度、强度、特征路径长度、全局效率、聚类系数、模块度和小世界特性。然后基于重复测量，我们计算了：（1）皮尔逊相关系数；（2）组内相关系数；（3）均方根变异系数；（4）可重复性系数；以及（5）变化的95%置信区间。
纳入了26例患者（年龄范围：4至21岁）。相关系数表明所有指标的重复观测之间具有高度一致的关系，组内相关系数总体处于优秀范围。数据集中的可重复性不受脑区划分大小的显著影响。然而，在较高的图谱阈值下观察到可重复性有下降趋势。
这些发现证明了癫痫患儿队列中网络指标的可靠性。知识进展：我们的结果表明，对静息态数据进行图论分析有可能为癫痫患儿的网络结构提供可靠标记。在个体患者层面，随时间的变化大于可重复性系数或变化的95%置信区间，不太可能与该方法的内在变异性相关。
