Signal Processing Laboratory, LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
PLoS One. 2011;6(8):e23009. doi: 10.1371/journal.pone.0023009. Epub 2011 Aug 4.
We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.
我们研究了一种自适应统计方法，用于分析由区域间连通性（连接组）的脑连接矩阵表示的脑网络。我们的方法处于全局分析和单连接分析之间的中间水平，通过考虑全局脑网络的子网来实现。这些子网表示两个大脑解剖区域之间的相互连通性，或者表示同一大脑解剖区域内的内部连通性。评估适当的摘要统计量，该统计量可以描述子网的有意义特征。基于此摘要统计量，执行相应的统计检验以得出相应的 p 值。通过这种方式重新表述问题，可以根据我们对问题的理解，有序地减少统计检验的数量。考虑全局检验问题，通过校正 p 值来控制错误发现的速率。最后，在显著子网内进行局部调查。我们在个体测量的基础上比较了这种策略，就功效而言。我们表明，该策略具有很大的潜力，特别是在子网定义明确且摘要统计量选择得当的情况下。作为应用示例，我们比较了具有 22q11.2 缺失综合征的两组受试者的结构脑连接矩阵，其特征在于 IQ 分数。
