## Adaptive strategy for the statistical analysis of connectomes.

### Détails

Télécharger : BIB_670C3496024C.P001.pdf (571.30 [Ko])

Etat: Serval

Version: de l'auteur

Etat: Serval

Version: de l'auteur

ID Serval

serval:BIB_670C3496024C

Type

**Article**: article d'un périodique ou d'un magazine.

Collection

Publications

Fonds

Titre

Adaptive strategy for the statistical analysis of connectomes.

Périodique

Plos One

ISSN

1932-6203 (Electronic)

ISSN-L

1932-6203

Statut éditorial

Publié

Date de publication

2011

Volume

6

Numéro

8

Pages

e23009

Langue

anglais

Notes

Publication types: Journal ArticlePublication Status: ppublish

Résumé

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.

Pubmed

Web of science

Création de la notice

16/08/2011 9:19

Dernière modification de la notice

18/11/2016 14:51