Modelling the data and not the images in FMRI
Current approaches to the analysis of functional magnetic resonance imaging (FMRI) data apply various preprocessing steps to the original FMRI. These preprocessings lead to a general underestimation of residual variance in the downstream analysis. This negatively impacts the type I error of statistical tests and increases the risk for reporting false positive results.
This is the first statistical software tool which implements the model based (MB) estimator for FMRI data models. It is a new and original method for the statistical analysis of FMRI of brain scans. MB estimation combines all preprocessing steps of the standard approaches into one single modelling step. Without altering the original 4D-image, the method results in smooth fits of the underlying parameter fields. More importantly, the method yields a trustworthy estimate of the uncertainty in BOLD effect estimation.
The availability of these uncertainty fields allows to model FMRI studies by random effects meta regression models, acknowledging that individual subjects are random entities, and that the certainty at which the actual BOLD effect in an individual can be estimated from an FMRI varies across the brain and between the subjects.
MB estimation allows to process and report BOLD effects in ati units. In particular multicentre studies gain power by its use: if an effect is present in your data, you will be more likely to find it.
Citing the MB estimator and this software:
Thomas W. D. Möbius (2018) Modelling the data and not the images in FMRI, ArXiv e-prints, arXiv:1809.07232
Thomas W. D. Möbius (2018) fmristats: Modelling the data and not the images in FMRI (Version 0.0.4) [Computer program]. Available at http://fmristats.github.io/
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