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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Meskaldji DE,Vasung L,Romascano D,Thiran JP,Hagmann P,Morgenthaler S,Van De Ville Ddoi
10.1016/j.neuroimage.2014.11.059subject
Has Abstractpub_date
2015-03-01 00:00:00pages
251-64eissn
1053-8119issn
1095-9572pii
S1053-8119(14)00989-6journal_volume
108pub_type
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