TY - JOUR
T1 - Comparison of IVA and GIG-ICA in brain functional network estimation using fMRI data
AU - Du, Yuhui
AU - Lin, Dongdong
AU - Yu, Qingbao
AU - Sui, Jing
AU - Chen, Jiayu
AU - Rachakonda, Srinivas
AU - Adali, Tulay
AU - Calhoun, Vince D.
N1 - Funding Information:
This work was partially supported by National Institutes of Health grants R01EB006841 (to VC) and R01REB020407 (to VC), National Science Foundation (NSF) grants 1016619 and 1539067, and a Centers of Biomedical Research Excellence (COBRE) grant P20RR021938/P20GM103472 (to VC), and by the NSF grants 1618551 and 1631838 (to TA), and by natural science foundation of Shanxi (2016021077, to YH).
Publisher Copyright:
© 2017 Du, Lin, Yu, Sui, Chen, Rachakonda, Adali and Calhoun.
PY - 2017/5/19
Y1 - 2017/5/19
N2 - Spatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as brain functional networks. Group information guided independent component analysis (GIG-ICA) as a variant of GICA has been proposed to improve the accuracy of the subject-specific ICs estimation by optimizing their independence. Independent vector analysis (IVA) is another method which optimizes the independence among each subject's components and the dependence among corresponding components of different subjects. Both methods are promising in neuroimaging study and showed a better performance than the traditional GICA. However, the difference between IVA and GIG-ICA has not been well studied. A detailed comparison between them is demanded to provide guidance for functional network analyses. In this work, we employed multiple simulations to evaluate the performances of the two approaches in estimating subject-specific components and time courses under conditions of different data quality and quantity, varied number of sources generated and inaccurate number of components used in computation, as well as the presence of spatially subject-unique sources. We also compared the two methods using healthy subjects' test-retest resting-state fMRI data in terms of spatial functional networks and functional network connectivity (FNC). Results from simulations support that GIG-ICA showed better recovery accuracy of both components and time courses than IVA for those subject-common sources, and IVA outperformed GIG-ICA in component and time course estimation for the subject-unique sources. Results from real fMRI data suggest that GIG-ICA resulted in more reliable spatial functional networks and yielded higher and more robust modularity property of FNC, compared to IVA. Taken together, GIG-ICA is appropriate for estimating networks which are consistent across subjects, while IVA is able to estimate networks with great inter-subject variability or subject-unique property.
AB - Spatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as brain functional networks. Group information guided independent component analysis (GIG-ICA) as a variant of GICA has been proposed to improve the accuracy of the subject-specific ICs estimation by optimizing their independence. Independent vector analysis (IVA) is another method which optimizes the independence among each subject's components and the dependence among corresponding components of different subjects. Both methods are promising in neuroimaging study and showed a better performance than the traditional GICA. However, the difference between IVA and GIG-ICA has not been well studied. A detailed comparison between them is demanded to provide guidance for functional network analyses. In this work, we employed multiple simulations to evaluate the performances of the two approaches in estimating subject-specific components and time courses under conditions of different data quality and quantity, varied number of sources generated and inaccurate number of components used in computation, as well as the presence of spatially subject-unique sources. We also compared the two methods using healthy subjects' test-retest resting-state fMRI data in terms of spatial functional networks and functional network connectivity (FNC). Results from simulations support that GIG-ICA showed better recovery accuracy of both components and time courses than IVA for those subject-common sources, and IVA outperformed GIG-ICA in component and time course estimation for the subject-unique sources. Results from real fMRI data suggest that GIG-ICA resulted in more reliable spatial functional networks and yielded higher and more robust modularity property of FNC, compared to IVA. Taken together, GIG-ICA is appropriate for estimating networks which are consistent across subjects, while IVA is able to estimate networks with great inter-subject variability or subject-unique property.
KW - Brain functional networks
KW - Functional magnetic resonance imaging (fMRI)
KW - Group information guided ICA (GIG-ICA)
KW - Independent component analysis (ICA)
KW - Independent vector analysis (IVA)
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U2 - 10.3389/fnins.2017.00267
DO - 10.3389/fnins.2017.00267
M3 - Article
C2 - 28579940
AN - SCOPUS:85019590376
SN - 1662-4548
VL - 11
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
IS - MAY
M1 - 267
ER -