Comparing connectomes across subjects and populations at different scales

• Published in: NeuroImage (Orlando, Fla.) Vol. 80; pp. 416 - 425
• Main Authors: Meskaldji, Djalel Eddine, Fischi-Gomez, Elda, Griffa, Alessandra, Hagmann, Patric, Morgenthaler, Stephan, Thiran, Jean-Philippe
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.10.2013; Elsevier Limited
• Subjects: Animals; Attention deficit hyperactivity disorder; Bonferroni; Brain; Brain - physiology; Brain connectivity; Classification; Connectome - methods; Data Interpretation, Statistical; Diffusion imaging; Efficiency; False discovery rate FDR; Family-wise error rate (FWER); Graph theory; Humans; Hypotheses; Magnetic resonance imaging (MRI); Models, Anatomic; Models, Neurological; Models, Statistical; Multiple comparisons; Multiple testing; Nerve Net - physiology; Neurosciences; Statistical methods; Brain connectivity; Multiple comparisons; Family-wise error rate (FWER); Bonferroni; Magnetic resonance imaging (MRI); Diffusion imaging; Multiple testing; Graph theory; False discovery rate FDR
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.04.084

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. •We review the statistical methods proposed to compare brain connectomes.•We classified the different methods into two scales of analysis.•We highlighted the potential presence of multiple testing when comparing connectomes.•We reviewed the most important aspects of multiple testing.•We discussed the possible factors that could influence the statistical results.