Han Cheol E, Yoo Sang Wook, Seo Sang Won, Na Duk L, Seong Joon-Kyung
Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
PLoS One. 2013 Aug 19;8(8):e72332. doi: 10.1371/journal.pone.0072332. eCollection 2013.
Graph theoretical approaches have successfully revealed abnormality in brain connectivity, in particular, for contrasting patients from healthy controls. Besides the group comparison analysis, a correlational study is also challenging. In studies with patients, for example, finding brain connections that indeed deepen specific symptoms is interesting. The correlational study is also beneficial since it does not require controls, which are often difficult to find, especially for old-age patients with cognitive impairment where controls could also have cognitive deficits due to normal ageing. However, one of the major difficulties in such correlational studies is too conservative multiple comparison correction. In this paper, we propose a novel method for identifying brain connections that are correlated with a specific cognitive behavior by employing cluster-based statistics, which is less conservative than other methods, such as Bonferroni correction, false discovery rate procedure, and extreme statistics. Our method is based on the insight that multiple brain connections, rather than a single connection, are responsible for abnormal behaviors. Given brain connectivity data, we first compute a partial correlation coefficient between every edge and the behavioral measure. Then we group together neighboring connections with strong correlation into clusters and calculate their maximum sizes. This procedure is repeated for randomly permuted assignments of behavioral measures. Significance levels of the identified sub-networks are estimated from the null distribution of the cluster sizes. This method is independent of network construction methods: either structural or functional network can be used in association with any behavioral measures. We further demonstrated the efficacy of our method using patients with subcortical vascular cognitive impairment. We identified sub-networks that are correlated with the disease severity by exploiting diffusion tensor imaging techniques. The identified sub-networks were consistent with the previous clinical findings having valid significance level, while other methods did not assert any significant findings.
图论方法已成功揭示了大脑连接的异常，特别是在对比患者与健康对照方面。除了组间比较分析外，相关性研究也具有挑战性。例如，在针对患者的研究中，找到确实与特定症状相关的大脑连接是很有趣的。相关性研究也很有益，因为它不需要对照组，而对照组往往很难找到，尤其是对于患有认知障碍的老年患者，由于正常衰老，对照组也可能存在认知缺陷。然而，此类相关性研究的主要困难之一是多重比较校正过于保守。在本文中，我们提出了一种新颖的方法，通过采用基于聚类的统计来识别与特定认知行为相关的大脑连接，该方法比其他方法（如Bonferroni校正、错误发现率程序和极端统计）保守性更低。我们的方法基于这样一种见解，即多种大脑连接而非单一连接导致异常行为。给定大脑连接数据，我们首先计算每条边与行为指标之间的偏相关系数。然后，我们将具有强相关性的相邻连接聚在一起形成聚类，并计算它们的最大规模。对行为指标的随机排列分配重复此过程。从聚类规模的零分布估计所识别子网的显著性水平。该方法独立于网络构建方法：结构或功能网络均可与任何行为指标结合使用。我们进一步使用皮质下血管性认知障碍患者证明了我们方法的有效性。我们通过利用扩散张量成像技术识别了与疾病严重程度相关的子网。所识别的子网与先前具有有效显著性水平的临床发现一致，而其他方法未得出任何显著发现。
