000203691 001__ 203691
000203691 005__ 20181203023711.0
000203691 0247_ $$2doi$$a10.1016/j.neuroimage.2014.11.059
000203691 022__ $$a1053-8119
000203691 02470 $$2ISI$$a000349618600027
000203691 037__ $$aARTICLE
000203691 245__ $$aImproved statistical evaluation of group differences in connectomes by screening-filtering strategy with application to study maturation of brain connections between childhood and adolescence
000203691 269__ $$a2015
000203691 260__ $$aSan Diego$$bElsevier$$c2015
000203691 300__ $$a14
000203691 336__ $$aJournal Articles
000203691 520__ $$aDetecting 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.
000203691 6531_ $$aMultiple testing
000203691 6531_ $$aComplex networks
000203691 6531_ $$aBrain connectivity
000203691 6531_ $$aFWER
000203691 6531_ $$aFDR
000203691 6531_ $$aAdolescence
000203691 6531_ $$aFronto limbic circuity
000203691 6531_ $$acibm-spc
000203691 6531_ $$aLTS5
000203691 700__ $$0242939$$aMeskaldji, Djalel Eddine$$g120480
000203691 700__ $$aVasung, Lana
000203691 700__ $$0247422$$aRomascano, David Paul Roger$$g175245
000203691 700__ $$0240323$$aThiran, Jean-Philippe$$g115534
000203691 700__ $$aHagmann, Patric
000203691 700__ $$0241889$$aMorgenthaler, Stephan$$g105911
000203691 700__ $$0240173$$aVan De Ville, Dimitri$$g152027
000203691 773__ $$j108$$q251–264$$tNeuroimage
000203691 909C0 $$0252169$$pMIPLAB$$xU12143
000203691 909C0 $$0252517$$pCNP$$xU12599
000203691 909C0 $$0252209$$pSTAP$$xU10127
000203691 909C0 $$0252394$$pLTS5$$xU10954
000203691 909CO $$ooai:infoscience.tind.io:203691$$pSB$$pSTI$$particle
000203691 917Z8 $$x120480
000203691 917Z8 $$x120480
000203691 917Z8 $$x120480
000203691 917Z8 $$x152027
000203691 917Z8 $$x120480
000203691 917Z8 $$x120480
000203691 917Z8 $$x120480
000203691 937__ $$aEPFL-ARTICLE-203691
000203691 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000203691 980__ $$aARTICLE