Comparing connectomes across subjects and populations at different scales

Elda Fischi Gomez, Alessandra Griffa, Patric Hagmann, Djalel Eddine Meskaldji, Stephan Morgenthaler, Jean-Philippe Thiran
Elsevier, 2013
Article

Résumé

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. (C) 2013 Elsevier Inc. All rights reserved.

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Statistical methods for comparing brain connectomes at different scales

Djalel Eddine Meskaldji, Stephan Morgenthaler, Dimitri Nestor Alice Van De Ville

Physiological Brain connectivity and spontaneous interaction between regions of interest of the brain can be represented by a matrix (full or sparse) or equivalently by a complex network called connectome. This representation of brain connectivity is adopted when comparing different patterns of structural and functional connectivity to null models or between groups of individuals. Two levels of comparison could be considered when analyzing brain connectivity: the global level and the local level. In the global level, the whole brain information is summarized by one summary statistic, whereas in the local analysis, each region of interest of the brain is summarized by a specific statistic. We show that these levels are mutually informatively integrative in some extent. We present different methods of analysis at both levels, the most relevant global and local network measures. We discuss as well the assumptions to be satisfied for each method; the error rates controlled by each method, and the challenges to overcome, especially, in the local case. We also highlight the possible factors that could influence the statistical results and the questions that have to be addressed in such analyses.

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Comparing connectomes across subjects and populations at different scales

Elda Fischi Gomez, Alessandra Griffa, Patric Hagmann, Djalel Eddine Meskaldji, Stephan Morgenthaler, Jean-Philippe Thiran

Brain connectivity can be represented by a network that enables the comparison of the different patterns of struc- tural and functional connectivity among individuals. In the literature, two levels ofstatistical analysis have been con- sidered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that summarizes the information ofeach brain is used in a statistical test; 2) the local analysis where a sin- gle 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

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EPFL2013