Distance-based analysis of variance for brain connectivity.
biostatistics
distance statistics
kernel ANOVA
neuroimaging
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
14
03
2018
accepted:
12
07
2019
pubmed:
28
7
2019
medline:
19
8
2021
entrez:
28
7
2019
Statut:
ppublish
Résumé
The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that might mask the salient features of high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within-group and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder.
Identifiants
pubmed: 31350904
doi: 10.1111/biom.13123
pmc: PMC7653688
mid: NIHMS1638086
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
257-269Subventions
Organisme : NICHD NIH HHS
ID : R01 HD089390
Pays : United States
Organisme : NIH HHS
ID : S10 OD023495
Pays : United States
Informations de copyright
© 2019 The International Biometric Society.
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