TY - JOUR
T1 - Performance evaluation of nonnegative matrix factorization algorithms to estimate task-related neuronal activities from fMRI data
AU - Ding, Xiaoyu
AU - Lee, Jong Hwan
AU - Lee, Seong Whan
N1 - Funding Information:
This research was supported in part by the World Class University (WCU) Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology, under Grant R31-10008; and in part by the Korea Science and Engineering Foundation (KOSEF) Grant funded by the Ministry of Education, Science, and Technology, under Grant 2012-005741.
PY - 2013/4
Y1 - 2013/4
N2 - Nonnegative matrix factorization (NMF) is a blind source separation (BSS) algorithm which is based on the distinct constraint of nonnegativity of the estimated parameters as well as on the measured data. In this study, according to the potential feasibility of NMF for fMRI data, the four most popular NMF algorithms, corresponding to the following two types of (1) least-squares based update [i.e., alternating least-squares NMF (ALSNMF) and projected gradient descent NMF] and (2) multiplicative update (i.e., NMF based on Euclidean distance and NMF based on divergence cost function), were investigated by using them to estimate task-related neuronal activities. These algorithms were applied firstly to individual data from a single subject and, subsequently, to group data sets from multiple subjects. On the single-subject level, although all four algorithms detected task-related activation from simulated data, the performance of multiplicative update NMFs was significantly deteriorated when evaluated using visuomotor task fMRI data, for which they failed in estimating any task-related neuronal activities. In group-level analysis on both simulated data and real fMRI data, ALSNMF outperformed the other three algorithms. The presented findings may suggest that ALSNMF appears to be the most promising option among the tested NMF algorithms to extract task-related neuronal activities from fMRI data.
AB - Nonnegative matrix factorization (NMF) is a blind source separation (BSS) algorithm which is based on the distinct constraint of nonnegativity of the estimated parameters as well as on the measured data. In this study, according to the potential feasibility of NMF for fMRI data, the four most popular NMF algorithms, corresponding to the following two types of (1) least-squares based update [i.e., alternating least-squares NMF (ALSNMF) and projected gradient descent NMF] and (2) multiplicative update (i.e., NMF based on Euclidean distance and NMF based on divergence cost function), were investigated by using them to estimate task-related neuronal activities. These algorithms were applied firstly to individual data from a single subject and, subsequently, to group data sets from multiple subjects. On the single-subject level, although all four algorithms detected task-related activation from simulated data, the performance of multiplicative update NMFs was significantly deteriorated when evaluated using visuomotor task fMRI data, for which they failed in estimating any task-related neuronal activities. In group-level analysis on both simulated data and real fMRI data, ALSNMF outperformed the other three algorithms. The presented findings may suggest that ALSNMF appears to be the most promising option among the tested NMF algorithms to extract task-related neuronal activities from fMRI data.
KW - Blind source separation (BSS)
KW - Functional magnetic resonance imaging (fMRI)
KW - Nonnegative matrix factorization (NMF)
KW - Visuomotor task
UR - http://www.scopus.com/inward/record.url?scp=84875262877&partnerID=8YFLogxK
U2 - 10.1016/j.mri.2012.10.003
DO - 10.1016/j.mri.2012.10.003
M3 - Article
C2 - 23200679
AN - SCOPUS:84875262877
SN - 0730-725X
VL - 31
SP - 466
EP - 476
JO - Magnetic Resonance Imaging
JF - Magnetic Resonance Imaging
IS - 3
ER -