Economics at your fingertips  

Multiscale null hypothesis testing for network‐valued data: Analysis of brain networks of patients with autism

Ilenia Lovato, Alessia Pini, Aymeric Stamm, Maxime Taquet and Simone Vantini

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 2, 372-397

Abstract: Networks are a natural way of representing the human brain for studying its structure and function and, as such, have been extensively used. In this framework, case–control studies for understanding autism pertain to comparing samples of healthy and autistic brain networks. In order to understand the biological mechanisms involved in the pathology, it is key to localize the differences on the brain network. Motivated by this question, we hereby propose a general non‐parametric finite‐sample exact statistical framework that allows to test for differences in connectivity within and between prespecified areas inside the brain network, with strong control of the family‐wise error rate. We demonstrate unprecedented ability to differentiate children with non‐syndromic autism from children with both autism and tuberous sclerosis complex using electroencephalography data. The implementation of the method is available in the R package nevada.

Date: 2021
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

Page updated 2021-05-12
Handle: RePEc:bla:jorssc:v:70:y:2021:i:2:p:372-397