Revisiting multi-subject random effects in fMRI: Advocating prevalence estimation: Advocating prevalence estimation

J.D. Rosenblatt, M. Vink, Y. Benjamini

Research output: Contribution to journalArticlepeer-review


Random effect analysis has been introduced into fMRI research in order to generalize findings from the study group to the whole population. Generalizing findings is obviously harder than detecting activation within the study group since in order to be significant, an activation has to be larger than the inter-subject variability. Indeed, detected regions are smaller when using random effect analysis versus fixed effects. The statistical assumptions behind the classic random effect model are that the effect in each location is normally distributed over subjects, and "activation" refers to a non-null mean effect. We argue that this model is unrealistic compared to the true population variability, where due to function-anatomy inconsistencies and registration anomalies, some of the subjects are active and some are not at each brain location.We propose a Gaussian-mixture-random-effect that amortizes between-subject spatial disagreement and quantifies it using the prevalence of activation at each location. We present a formal definition and an estimation procedure of this prevalence. The end result of the proposed analysis is a map of the prevalence at locations with significant activation, highlighting activation regions that are common over many brains.Prevalence estimation has several desirable properties: (a) It is more informative than the typical active/inactive paradigm. (b) In contrast to the usual display of p-values in activated regions - which trivially converge to 0 for large sample sizes - prevalence estimates converge to the true prevalence. © 2013 Elsevier Inc.
Original languageEnglish
Pages (from-to)113-121
Number of pages9
StatePublished - Jan 2014


FundersFunder number
Seventh Framework Programme294519
European Research Council


    • article
    • controlled study
    • data analysis
    • functional magnetic resonance imaging
    • functional neuroimaging
    • Gaussian mixture random effect
    • human
    • human experiment
    • measurement accuracy
    • nerve cell plasticity
    • prevalence
    • priority journal
    • sample size
    • statistical concepts
    • statistical model
    • task performance
    • algorithm
    • artifact
    • biological model
    • brain
    • brain mapping
    • computer assisted diagnosis
    • computer simulation
    • image enhancement
    • nuclear magnetic resonance imaging
    • physiology
    • procedures
    • reproducibility
    • sensitivity and specificity
    • statistical analysis
    • Algorithms
    • Artifacts
    • Brain
    • Brain Mapping
    • Computer Simulation
    • Data Interpretation
    • Statistical
    • Humans
    • Image Enhancement
    • Image Interpretation
    • Computer-Assisted
    • Magnetic Resonance Imaging
    • Models
    • Neurological
    • Reproducibility of Results
    • Sensitivity and Specificity


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