Metadata-Version: 1.1
Name: fmristats
Version: 0.0.3
Summary: Data oriented method for fitting FMRI models
Home-page: https://fmristats.github.io/
Author: Thomas W. D. Möbius
Author-email: moebius@medinfo.uni-kiel.de
License: GPLv3+
Description: Data oriented method for the fitting of 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.
        
Keywords: fmri mri statistics meta-analysis meta-regression imaging neuroimaging
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3.5
