Statistical Parametric Maps for Functional MRI Experiments inR: The Packagefmri
Author(s) -
Karsten Tabelow,
Jörg Polzehl
Publication year - 2011
Publication title -
journal of statistical software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.636
H-Index - 145
ISSN - 1548-7660
DOI - 10.18637/jss.v044.i11
Subject(s) - computer science , statistical parametric mapping , functional magnetic resonance imaging , smoothing , r package , parametric statistics , artificial intelligence , general linear model , noise (video) , pipeline (software) , signal (programming language) , pattern recognition (psychology) , nonparametric statistics , data mining , computer vision , linear model , machine learning , magnetic resonance imaging , image (mathematics) , mathematics , statistics , medicine , computational science , radiology , neuroscience , programming language , biology
The purpose of the package fmri is the analysis of single subject functional magnetic resonance imaging (fMRI) data. It provides fMRI analysis from time series modeling by a linear model to signal detection and publication quality images. Specifically, it implements structural adaptive smoothing methods with signal detection for adaptive noise reduction which avoids blurring of activation areas. Within this paper we describe the complete pipeline for fMRI analysis using fmri. We describe data reading from various medical imaging formats and the linear modeling used to create the statistical parametric maps. We review the rationale behind the structural adaptive smoothing algorithms and explain their usage from the package fmri. We demonstrate the results of such analysis using two experimental datasets. Finally, we report on the usage of a graphical user interface for some of the package functions.
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