TY - GEN
T1 - Capturing group variability using IVA
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
AU - Ma, Sai
AU - Phlypo, Ronald
AU - Calhoun, Vince D.
AU - Adali, Tulay
PY - 2013/10/18
Y1 - 2013/10/18
N2 - When applied to functional magnetic resonance imaging (fMRI) data, independent vector analysis (IVA) provides superior performance in capturing subject variability within one group, as compared to the widely used group independent component analysis (ICA) approach. However, the effectiveness of IVA algorithms in preserving variability between different groups of subjects has not been studied yet, although it is of great interest in most fMRI studies, especially for identifying biomarkers for diagnosis of mental disorders. In this paper, we introduce a methodology that uses graph-theoretical analysis and statistical analysis for assessing the ability of IVA algorithms to capture group variability. We generate multi-subject fMRI-like datasets with increasing spatial variability for a selected component between two groups and compare a robust IVA algorithm to group ICA approach. Our experimental results show that IVA can successfully preserve group variability, indicating its potential in extracting biomarkers across groups of subjects in fMRI analysis.
AB - When applied to functional magnetic resonance imaging (fMRI) data, independent vector analysis (IVA) provides superior performance in capturing subject variability within one group, as compared to the widely used group independent component analysis (ICA) approach. However, the effectiveness of IVA algorithms in preserving variability between different groups of subjects has not been studied yet, although it is of great interest in most fMRI studies, especially for identifying biomarkers for diagnosis of mental disorders. In this paper, we introduce a methodology that uses graph-theoretical analysis and statistical analysis for assessing the ability of IVA algorithms to capture group variability. We generate multi-subject fMRI-like datasets with increasing spatial variability for a selected component between two groups and compare a robust IVA algorithm to group ICA approach. Our experimental results show that IVA can successfully preserve group variability, indicating its potential in extracting biomarkers across groups of subjects in fMRI analysis.
KW - ICA
KW - IVA
KW - graph-theoretical analysis
KW - group variability
KW - multi-subject fMRI-like data
UR - http://www.scopus.com/inward/record.url?scp=84890470961&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890470961&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638234
DO - 10.1109/ICASSP.2013.6638234
M3 - Conference contribution
AN - SCOPUS:84890470961
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3128
EP - 3132
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Y2 - 26 May 2013 through 31 May 2013
ER -