4.4 Article

Group-level comparison of brain connectivity networks

期刊

BMC MEDICAL RESEARCH METHODOLOGY
卷 22, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12874-022-01712-8

关键词

Connectivity; Subject heterogeneity; fMRI; Statistical power; Type I error rate

向作者/读者索取更多资源

This study proposes a new statistical method to compare functional connectivity networks between subgroups, taking into account the network topological structure of brain regions and subject heterogeneity. The results from simulation data show that the proposed model has high power and near-nominal type I error rates.
Background Functional connectivity (FC) studies are often performed to discern different patterns of brain connectivity networks between healthy and patient groups. Since many neuropsychiatric disorders are related to the change in these patterns, accurate modelling of FC data can provide useful information about disease pathologies. However, analysing functional connectivity data faces several challenges, including the correlations of the connectivity edges associated with network topological characteristics, the large number of parameters in the covariance matrix, and taking into account the heterogeneity across subjects. Methods This study provides a new statistical approach to compare the FC networks between subgroups that consider the network topological structure of brain regions and subject heterogeneity. Results The power based on the heterogeneity structure of identity scaled in a sample size of 25 exhibited values greater than 0.90 without influencing the degree of correlation, heterogeneity, and the number of regions. This index had values above 0.80 in the small sample size and high correlation. In most scenarios, the type I error was close to 0.05. Moreover, the application of this model on real data related to autism was also investigated, which indicated no significant difference in FC networks between healthy and patient individuals. Conclusions The results from simulation data indicated that the proposed model has high power and near-nominal type I error rates in most scenarios.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据