论文标题
三维数据表的个体间变异性研究：检测不稳定变量和样品
Study of inter-individual variability of three-dimensional data table: detection of unstable variables and samples
论文作者
论文摘要
我们提出了两种方法，以便更好地了解静止状态功能磁共振成像（fMRI）脑数据的个体间变异性。该研究的目的是量化平均树状图是否代表了初始人群，并确定其可能的不稳定性来源。平均树状图基于静息状态网络之间的Pearson相关性。第一个方法标识了可以导致平均树状图的不稳定分区的网络。第二种方法确定了参与者的同质子样本，其相关的平均树状图比整个样本中的样本更稳定。这两种建议的方法已经显示出可显着的可量化行为数据结果，当噪声水平不隐藏数据结构时，检测到不稳定的网络或亚群的存在。这两种方法已成功地用于建立成年后期的大脑图集。第一种方法清楚地表明，地图集网络之间没有不稳定的网络。第二种方法强调了与不同年龄相关的大脑组织的两个不同的子人群的存在。
We propose two methodologies in order to better understand the inter-individual variability of resting-state functional Magnetic Resonance Imaging (fMRI) brain data. The aim of the study was to quantify whether the average dendrogram is representative of the initial population and to identify its possible sources of instability. The average dendrogram is based on the Pearson correlation between resting-state networks. The first method identifies networks that can lead to unstable partitions of the average dendrogram. The second method identified homogeneous sub-samples of participants for whom their associated average dendrograms were more stable than that of the whole sample. The two suggested methods have shown significant quantifiable behavioral data results with regards to detecting an unstable network or presence of subpopulations when the noise level does not conceal the structure of the data. These two methods have been successfully applied to establish a cerebral atlas for late adulthood. The first method made it clear that there was no unstable network among the atlas networks. The second method highlighted the presence of two distinct sub-populations with different age-related brain organizations.
