Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation
Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation
复制标题
用于评估数据驱动分区的功能网络的测试再测试可靠性
DOI：
10.1007/978-3-030-32391-2_10
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jianfeng Zeng, Anh The
中科院分区：
文献类型：
--
作者：
Jianfeng Zeng, Anh The
脑包裹在功能性连接组学中起着关键作用。在大多数研究中，通常使用一套标准的神经解剖脑图谱。此外，还使用了使用各种聚类算法从fMRI数据计算的数据驱动的包裹。最近的研究开始确定最好的包裹在质量和可靠性方面仍然没有定论，没有一个明确的赢家。在这项工作中，我们研究了效用的功能连接的重测信度作为评价指标比较包裹。具体来说，使用来自人类连接体项目的数据，我们比较了数据驱动的分组和使用组内相关系数（ICC）的几何分组。我们还研究了包裹粒度对重测信度的影响。我们观察到，ICC的几何parcellation比那些数据驱动的parcellation，这表明使用规则的地块在几何地图集计算的FC比使用数据驱动的parcellation计算的更可靠。
Brain parcellations play a key role in functional connectomics. A set of standard neuro-anatomical brain atlases are in common use in most studies. In addition, data-driven parcellations computed from fMRI data using a variety of clustering algorithms have also been used. Recent studies set out to determine the best parcellation in terms of quality and reliability have remained inconclusive without a clear winner. In this work, we investigated the utility of test-retest reliability of functional connectivity as an evaluation metric for comparing parcellations. Specifically, using data from the human connectome project, we compared a data-driven parcellation and a geometric parcellation using Intraclass Correlation Coefficient (ICC). We also investigated the impact of parcellation granularity on the test-retest reliability. We observed that the ICCs for geometric parcellation are better than those of a data-driven parcellation, suggesting that the FCs computed using regular parcels in the geometric atlases are more reliable than those computed using a data-driven parcellation.
影响因子：
5.7
作者：
Van Essen, David C.;Smith, Stephen M.;Barch, Deanna M.;Behrens, Timothy E. J.;Yacoub, Essa;Ugurbil, Kamil
通讯作者：
Ugurbil, Kamil
影响因子：
16.2
作者：
Bearden CE;Thompson PM
通讯作者：
Thompson PM