Data oriented method for fitting FMRI models
Most current approaches to the statistical analysis of functional magnetic resonance imaging (FMRI) data involve varieties of preprocessing steps which alter the signal to noise ratio of the original data.
Enhancing the SNR prior to a formal analysis, though, shakes at primary principles of statistical decision making and it will generally inflate the type I error of the analysis.
This is the first statistical software tool which implements the data oriented method (DOM) estimator for FMRI data models, a new and original method for the statistical analysis of FMRI data of brain scans. The method fits a weighted least squares model to points of a random vector field. Without prior spacial smoothings, i.e. without altering the original 4D-image, the method nevertheless results in smooth fits of the underlying activation parameter fields. More importantly, though, the method yields a trustworthy estimate of the uncertainty of the estimated activation field for each subject in a study. 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 variability in the estimated individual activation patterns vary across the brain and between subjects.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|fmristats-0.0.3-py3-none-any.whl (113.8 kB) Copy SHA256 hash SHA256||Wheel||py3||Aug 10, 2018|
|fmristats-0.0.3.tar.gz (62.3 kB) Copy SHA256 hash SHA256||Source||None||Aug 10, 2018|