Rigorous statistical estimation of FMRI models
The current state-of-the-art approach to the statistical analysis of functional MR-images involves a variety of pre-processing steps, which alter the signal to noise ratio of the original data.
This is a new and original approach for the statistical analysis of functional MR-imaging data of brain scans. The method essentially fits a weighted least squares model to arbitrary points of a 3D-random field. Without prior spacial smoothing, i.e., without altering the original 4D-image, the method nevertheless results in a smooth fit of the underlying activation pattern. 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 this uncertainty field allows for the first time to model group studies and group-wise comparisons using random effects meta regression models, acknowledging the fact that (i) individual subjects are random entities in group studies, and that (ii) the variability in the estimated individual activation patterns varies across the brain and between subjects.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|fmristats-0.0.2-py3-none-any.whl (104.9 kB) Copy SHA256 hash SHA256||Wheel||py3||Apr 3, 2018|
|fmristats-0.0.2.tar.gz (56.2 kB) Copy SHA256 hash SHA256||Source||None||Apr 3, 2018|