Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data
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 guide...
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| Format: | Article |
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Frontiers Media S.A.
2017-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fnins.2017.00267/full |
| _version_ | 1831737366340960256 |
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| author | Yuhui Du Yuhui Du Dongdong Lin Qingbao Yu Jing Sui Jing Sui Jiayu Chen Srinivas Rachakonda Tulay Adali Vince D. Calhoun Vince D. Calhoun |
| author_facet | Yuhui Du Yuhui Du Dongdong Lin Qingbao Yu Jing Sui Jing Sui Jiayu Chen Srinivas Rachakonda Tulay Adali Vince D. Calhoun Vince D. Calhoun |
| author_sort | Yuhui Du |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T13:02:32Z |
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| id | doaj.art-2d48188dfa0549efa6511b9912cc1f55 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-21T13:02:32Z |
| publishDate | 2017-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-2d48188dfa0549efa6511b9912cc1f552022-12-21T19:03:08ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2017-05-011110.3389/fnins.2017.00267250300Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI DataYuhui Du0Yuhui Du1Dongdong Lin2Qingbao Yu3Jing Sui4Jing Sui5Jiayu Chen6Srinivas Rachakonda7Tulay Adali8Vince D. Calhoun9Vince D. Calhoun10The Mind Research NetworkAlbuquerque, NM, USASchool of Computer and Information Technology, Shanxi UniversityTaiyuan, ChinaThe Mind Research NetworkAlbuquerque, NM, USAThe Mind Research NetworkAlbuquerque, NM, USAThe Mind Research NetworkAlbuquerque, NM, USABrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, ChinaThe Mind Research NetworkAlbuquerque, NM, USAThe Mind Research NetworkAlbuquerque, NM, USADepartment of Computer Science and Electrical Engineering, University of Maryland Baltimore CountyBaltimore, MD, USAThe Mind Research NetworkAlbuquerque, NM, USADepartment of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USASpatial 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.http://journal.frontiersin.org/article/10.3389/fnins.2017.00267/fullfunctional magnetic resonance imaging (fMRI)brain functional networksindependent component analysis (ICA)group information guided ICA (GIG-ICA)independent vector analysis (IVA) |
| spellingShingle | Yuhui Du Yuhui Du Dongdong Lin Qingbao Yu Jing Sui Jing Sui Jiayu Chen Srinivas Rachakonda Tulay Adali Vince D. Calhoun Vince D. Calhoun Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data Frontiers in Neuroscience functional magnetic resonance imaging (fMRI) brain functional networks independent component analysis (ICA) group information guided ICA (GIG-ICA) independent vector analysis (IVA) |
| title | Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data |
| title_full | Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data |
| title_fullStr | Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data |
| title_full_unstemmed | Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data |
| title_short | Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data |
| title_sort | comparison of iva and gig ica in brain functional network estimation using fmri data |
| topic | functional magnetic resonance imaging (fMRI) brain functional networks independent component analysis (ICA) group information guided ICA (GIG-ICA) independent vector analysis (IVA) |
| url | http://journal.frontiersin.org/article/10.3389/fnins.2017.00267/full |
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