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Conference papers

Validity of summary statistics-based mixed-effects group fMRI

Camille Maumet 1, * Thomas E. Nichols 1, 2 
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U1228, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Statistical analysis of multi-subject functional Magnetic Resonance Imaging (fMRI) data is traditionally done using either: 1) a mixed-effects GLM (MFX GLM) where within-subject variance estimates are used and incorporated into per-subject weights or 2) a random-effects General linear model (GLM) (RFX GLM) where within-subject variance estimates are not used. Both approaches are implemented and available in major neuroimaging software packages including: SPM (MFX analysis; 2nd-Level statistics), FSL (FLAME; OLS) and AFNI (3dMEMA; 3dttest++). While MFX GLM provides the most efficient statistical estimate, its properties are only guaranteed in large samples, and it has been shown that RFX GLM is a valid alternative for one-sample group analyses in fMRI [1]. We recently showed that MFX GLM for image-based meta-analysis could lead to invalid results in small-samples. Here, we investigate whether this issue also affects group fMRI.
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Conference papers
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Submitted on : Thursday, October 4, 2018 - 3:35:04 PM
Last modification on : Friday, April 8, 2022 - 4:04:03 PM
Long-term archiving on: : Saturday, January 5, 2019 - 4:26:15 PM


  • HAL Id : inserm-01887911, version 1


Camille Maumet, Thomas E. Nichols. Validity of summary statistics-based mixed-effects group fMRI. OHBM 2018 - 24th Annual Meeting of the Organization for Human Brain Mapping, Jun 2018, Singapore, Singapore. pp.1-2. ⟨inserm-01887911⟩



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