title: Comparing connectomes across subjects and populations at different scales.
abstract: 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.
authors: Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. djalel.meskaldji@epfl.ch
authors: Djalel Eddine
authors: DE
authors: Meskaldji
authors: Elda
authors: E
authors: Fischi-Gomez
authors: Alessandra
authors: A
authors: Griffa
authors: Patric
authors: P
authors: Hagmann
authors: Stephan
authors: S
authors: Morgenthaler
authors: Jean-Philippe
authors: JP
authors: Thiran
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008953:Models, Anatomic; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*
keywords: Bonferroni; Brain connectivity; Diffusion imaging; False discovery rate FDR; Family-wise error rate (FWER); Graph theory; Magnetic resonance imaging (MRI); Multiple comparisons; Multiple testing