FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.
ABCD
longitudinal analysis
mixed models
vertex-wise
voxel-wise
whole brain
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
01 Feb 2024
01 Feb 2024
Historique:
revised:
08
12
2023
received:
28
04
2023
accepted:
17
12
2023
medline:
10
2
2024
pubmed:
10
2
2024
entrez:
10
2
2024
Statut:
ppublish
Résumé
The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive Development
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e26579Subventions
Organisme : NIMH NIH HHS
ID : R01MH118281
Pays : United States
Organisme : NIMH NIH HHS
ID : R01MH122688
Pays : United States
Organisme : NIMH NIH HHS
ID : RF1MH120025
Pays : United States
Informations de copyright
© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
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