Comparing the reliability of different ICA algorithms for fMRI analysis

Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2022-06, Vol.17 (6), p.e0270556-e0270556
Main Authors: Wei, Pengxu, Bao, Ruixue, Fan, Yubo
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Biology and Life Sciences
Component reliability
Computer and Information Sciences
Correlation coefficient
Correlation coefficients
Datasets
Electric Power Supplies
Engineering and Technology
Estimates
Evaluation
Experiments
Functional magnetic resonance imaging
Independent component analysis
Magnetic resonance
Magnetic Resonance Imaging
Medicine and Health Sciences
Normal distribution
Physical Sciences
Reliability analysis
Reproducibility
Reproducibility of Results
Research and Analysis Methods
Signal processing
Social Sciences
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0270556