title: Improved statistical evaluation of group differences in connectomes by screening-filtering strategy with application to study maturation of brain connections between childhood and adolescence.
abstract: Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase the power of detecting true between-group effects. We present an adaptive strategy that exploits the data structure and the prior information concerning positive dependence between nodes and connections, without relying on strong assumptions. As a first step, we decompose the brain network, i.e., the connectome, into subnetworks and we apply a screening at the subnetwork level. The subnetworks are defined either according to prior knowledge or by applying a data driven algorithm. Given the results of the screening step, a filtering is performed to seek real differences at the node/connection level. The proposed strategy could be used to strongly control either the family-wise error rate or the false discovery rate. We show by means of different simulations the benefit of the proposed strategy, and we present a real application of comparing connectomes of preschool children and adolescents.
authors.affiliation: Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland. Electronic address: djalel.meskaldji@epfl.ch.
authors.forename: Djalel-Eddine
authors.initials: DE
authors.lastname: Meskaldji
authors.affiliation: Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland.
authors.forename: Lana
authors.initials: L
authors.lastname: Vasung
authors.affiliation: LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
authors.forename: David
authors.initials: D
authors.lastname: Romascano
authors.affiliation: LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology, University Hospital Center and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thiran
authors.affiliation: Department of Radiology, University Hospital Center and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
authors.forename: Patric
authors.initials: P
authors.lastname: Hagmann
authors.affiliation: Applied Statistics, Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
authors.forename: Stephan
authors.initials: S
authors.lastname: Morgenthaler
authors.affiliation: Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
authors.forename: Dimitri
authors.initials: D
authors.lastname: Van De Ville
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001921:Brain / Q000254:growth & development*; D002648:Child; D063132:Connectome / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009434:Neural Pathways / Q000254:growth & development*
keywords: Adolescence; Brain connectivity; Complex networks; False discovery rate FDR; Family-wise error rate (FWER); Fronto-limbic circuitry; Multiple testing