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Validity of summary statistics-based mixed-effects group fMRI

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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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Dates and versions

inserm-01887911 , version 1 (04-10-2018)

Identifiers

  • HAL Id : inserm-01887911 , version 1

Cite

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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