Yoo Kwangsun, Lee Peter, Chung Moo K, Sohn William S, Chung Sun Ju, Na Duk L, Ju Daheen, Jeong Yong
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Hum Brain Mapp. 2017 Jan;38(1):165-181. doi: 10.1002/hbm.23352. Epub 2016 Sep 4.
Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass-univariate hypothesis testing. Although, several cluster-wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster-forming threshold. In this study, we propose a novel method, degree-based statistic (DBS), performing cluster-wise inference. DBS is designed to overcome the above-mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known "ground truth" simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165-181, 2017. © 2016 Wiley Periodicals, Inc.
大脑连接性分析已被广泛用于研究大脑的组织和功能，或观察神经或精神疾病中的变化。然而，连接性分析不可避免地引入了多变量假设检验的问题。虽然已经提出了几种聚类校正方法来解决这个问题，并显示出具有高灵敏度，但这些方法从根本上有两个缺点：缺乏空间特异性（定位能力）和初始聚类形成阈值的任意性。在本研究中，我们提出了一种新的方法，基于度的统计量（DBS），用于进行聚类推断。DBS旨在克服上述两个缺点。从网络的角度来看，一些脑区至关重要，并被认为在网络整合中起关键作用。基于这一概念，DBS将一个聚类定义为一组共享一个末端节点的边。这种定义能够有效地检测聚类及其中心节点。此外，还引入了一种新的聚类度量，中心持久性（CP）。通过已知“真实情况”的模拟证明了DBS的有效性。然后他们将DBS应用于两个实验数据集，并表明DBS成功地检测到了持久聚类。总之，通过采用图论中的度的概念并借鉴代数拓扑中的持久性概念，DBS能够灵敏地识别出具有中心节点的聚类，这些中心节点在感兴趣的效应中起关键作用。DBS可能广泛适用于各种认知或临床情况，并使我们能够获得统计上可靠且易于解释的结果。《人类大脑图谱》38:165 - 181, 20I7。© 2016威利期刊公司。
