Authors:
Troels Bjerre
1
;
Jonas Henriksen
1
;
Carsten Haagen Nielsen
2
;
Peter Mondrup Rasmussen
2
;
Lars Kai Hansen
2
and
Kristoffer Hougaard Madsen
3
Affiliations:
1
DTU Electrical Engineering, Technical University of Denmark, Denmark
;
2
Technical University of Denmark, Denmark
;
3
DRCMR, Copenhagen University Hospital Hvidovre, Denmark
Keyword(s):
Bayesian Information Criterion (BIC), functional Magnetic Resonance Imaging (fMRI), General linear model
(GLM), ica4spm, Independent Component Analysis (ICA), Statistical Parameter Mapping (SPM).
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
We present a toolbox for exploratory analysis of functional magnetic resonance imaging (fMRI) data using
independent component analysis (ICA) within the widely used SPM analysis pipeline. The toolbox enables
dimensional reduction using principal component analysis, ICA using several different ICA algorithms, selection
of the number of components using the Bayesian information criterion (BIC), visualization of ICA
components, and extraction of components for subsequent analysis using the standard general linear model.
We demonstrate how the toolbox is capable of identifying activity and nuisance effects in fMRI data from a
visual experiment.