Comparing connectomes across subjects and populations at different scales

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• 作者：Djalel Eddine Meskaldji ; Elda Fischi-Gomez ; Aless ; ra Griffa ; Patric Hagmann ; Stephan Morgenthaler ; Jean-Philippe Thiran
• 关键词：Brain connectivity ; Magnetic resonance imaging (MRI) ; Diffusion imaging ; Multiple testing ; Multiple comparisons ; Bonferroni ; Family-wise error rate (FWER) ; False discovery rate FDR ; Graph theory
• 刊名：NeuroImage
• 出版年：2013
• 出版时间：15 October, 2013
• 年：2013
• 卷：80
• 期：Complete
• 页码：416-425
• 全文大小：393 K

文摘

Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that summarizes the information of each brain is used in a statistical test; 2) the local analysis where a single test is performed either for each node/connection which implies a multiplicity correction, or for each group of nodes/connections where each subset is summarized by one single test in order to reduce the number of tests to avoid a penalizing multiplicity correction. We comment on the different levels of analysis and present some methods that have been proposed at each scale. We highlight as well the possible factors that could influence the statistical results and the questions that have to be addressed in such an analysis.