Name
Abstract | ||
|---|---|---|
Due to its relatively few assumptions, independent component analysis (ICA) has become a widely-used tool for the analysis of functional magnetic resonance imaging (fMRI) data. In its application, Infomax, has been by far the most frequently used ICA algorithm, primarily because it is the first ICA algorithm applied to fMRI analysis. However, now there are a number of more flexible ICA algorithms, which can exploit multiple types of statistical properties of the signals with fewer assumptions. In this work, we investigate the performance of Infomax and two of the more recent ICA algorithms, entropy bound minimization (EBM) and entropy rate bound minimization (ERBM), on resting state fMRI data derived from a large number of patients with schizophrenia (SZs) and healthy controls (HCs). In order to overcome the difficulty of directly comparing the performances of different ICA algorithms on real fMRI data, we propose the use of graph theoretic (GT) metrics to assess the quality of an ICA decomposition by measuring an algorithm's ability to capture the inherent differences between SZs and HCs. Our results show that ERBM, the algorithm which incorporates the greatest number of statistical properties of the signals, provides the best performance for fMRI analysis. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CISS.2017.7926108 | 2017 51st Annual Conference on Information Sciences and Systems (CISS) |
Keywords | Field | DocType |
fMRI analysis,independent component analysis,functional magnetic resonance imaging,fMRI data,ICA algorithm,entropy rate bound minimization,schizophrenia,graph theoretic metrics,ICA decomposition | Entropy rate,Algorithm design,Functional magnetic resonance imaging,Pattern recognition,Computer science,Resting state fMRI,Minification,Independent component analysis,Artificial intelligence,Cluster analysis,Infomax | Conference |
ISBN | Citations | PageRank |
978-1-5090-2697-5 | 0 | 0.34 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qunfang Long | 1 | 0 | 3.38 |
| Suchita Bhinge | 2 | 3 | 3.80 |
| Yuri Levin-Schwartz | 3 | 25 | 5.21 |
| Vince D Calhoun | 4 | 2769 | 268.91 |
| Tülay Adali | 5 | 1690 | 126.40 |