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An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis

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Abstract

Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis.

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References

  1. Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453:869–878

    Article  CAS  PubMed  Google Scholar 

  2. Im CH (2007) Dealing with mismatched fMRI activations in fMRI constrained EEG cortical source imaging: a simulation study assuming various mismatch types. Med Bio Eng Comput 45:79–90

    Article  Google Scholar 

  3. Vargas ER, Mitchell DGV, Greening SG, Wahl LM (2016) Network analysis of human fMRI data suggests modular restructuring after simulated acquired brain injury. Med Bio Eng Comput 54:235–248

    Article  Google Scholar 

  4. Li KM, Guo L, Nie JX, Li G, Liu T (2009) Review of methods for functional brain connectivity detection using fMRI. Comput Med Imaging Graph 33:131–139

    Article  PubMed  Google Scholar 

  5. Li Z, Zang YF, Ding J, Wang Z (2017) Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity. Med Bio Eng Comput 55:631–640

    Article  Google Scholar 

  6. Sun F, Morris D, Babyn P (2009) The optimal linear transformation-based fMRI feature space analysis. Med Bio Eng Comput 47:1119–1129

    Article  Google Scholar 

  7. McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ (1998) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp 6:160–188

    Article  CAS  PubMed  Google Scholar 

  8. Zhang S, Tsai SJ, Hu S, Xu J, Chao HH, Calhoun VD, Li CR (2015) Independent component analysis of functional networks for response inhibition: Inter-subject variation in stop signal reaction time. Hum Brain Mapp 36:3289–3302

    Article  PubMed  PubMed Central  Google Scholar 

  9. Long Z, Chen K, Wu X, Reiman E, Peng D, Yao L (2009) Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques. Hum Brain Mapp 30:417–431

    Article  PubMed  Google Scholar 

  10. Long Z, Li R, Hui M, Jin Z, Yao L (2013) An improvement of independent component analysis with projection method applied to multi-task fMRI data. Comput Biol Med 43:200–210

    Article  PubMed  Google Scholar 

  11. Friston KJ, Frith CD, Turner R, Frackowiak RSJ (1995) Characterizing evoked hemodynamics with fMRI. NeuroImage 2:157–165

    Article  CAS  PubMed  Google Scholar 

  12. Damoiseaux JS, Rombouts S, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 103:13848–13853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A 104:13170–13175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Calhoun VD, Kiehl KA, Pearlson GD (2008) Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Hum Brain Mapp 29:828–838

    Article  PubMed  PubMed Central  Google Scholar 

  15. Schmithorst VJ (2005) Separate cortical networks involved in music perception: Preliminary functional MRI evidence for modularity of music processing. NeuroImage 25:444–451

    Article  PubMed  Google Scholar 

  16. Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140–151

    Article  CAS  PubMed  Google Scholar 

  17. Wang Z, Xia MG, Jin Z, Yao L, Long Z (2014) Temporally and spatially constrained ICA of fMRI data analysis. PLoS One 9:e94211

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ma X, Zhang H, Zhao X, Yao L, Long Z (2013) Semi-blind independent component analysis of fMRI based on real-time fMRI system. IEEE Trans Neural Syst Rehabil Eng 21:416–426

    Article  PubMed  Google Scholar 

  19. Liu H, Xie X, Xu S, Wan F, Hu Y (2013) One-unit second-order blind identification with reference for short transient signals. Inf Sci 227:90–101

    Article  Google Scholar 

  20. Lu W, Rajapakse JC (2005) Approach and applications of constrained ICA. IEEE Trans Neural Netw 16:203–212

    Article  PubMed  Google Scholar 

  21. Lu W, Rajapakse JC (2006) ICA with reference. Neurocomputing 69:2244–2257

    Article  Google Scholar 

  22. Barros AK, Vigario R, Jousmaki V, Ohnishi N (2000) Extraction of event related signals from multi-channel bioelectrical measurements. IEEE Trans Biomed Eng 47:583–588

    Article  CAS  PubMed  Google Scholar 

  23. Lin QH, Zheng YR, Yin FL, Liang H, Calhoun VD (2007) A fast algorithm for one unit ICA-R. Inf Sci 177:1265–1275

    Article  Google Scholar 

  24. Calhoun VD, Adali T, Stevens MC, Kiehl KA, Pekar JJ (2005) Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis. NeuroImage 25:527–538

    Article  CAS  PubMed  Google Scholar 

  25. Lin QH, Liu JY, Zheng YR, Liang H, Calhoun VD (2010) Semiblind spatial ICA of fMRI using spatial constraints. Hum Brain Mapp 31:1076–1088

    Article  PubMed  PubMed Central  Google Scholar 

  26. Sun ZL, Shang L (2010) An improved constrained ICA with reference based unmixing matrix initialization. Neurocomputing 73:1013–1017

    Article  Google Scholar 

  27. Li CL, Liao GS, Shen YL (2010) An improved method for independent component analysis with reference. Digit Signal Process 20:575–580

    Article  Google Scholar 

  28. Mi JX (2014) A novel algorithm for independent component analysis with reference and methods for its applications. PLoS One 9:e93984

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mi JX, Xu Y (2014) A comparative study and improvement of two ICA using reference signal methods. Neurocomputing 137:157–164

    Article  Google Scholar 

  30. Valente G, De Martino F, Filosa G, Balsi M, Formisano E (2009) Optimizing ICA in fMRI using information on spatial regularities of the sources. Magn Reson Imaging 27:1110–1119

    Article  PubMed  Google Scholar 

  31. Zhang ZL (2008) Morphologically constrained ICA for extracting weak temporally correlated signals. Neurocomputing 71:1669–1679

    Article  CAS  Google Scholar 

  32. James CJ, Gibson OJ (2003) Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis. IEEE Trans Biomed Eng 50:1108–1116

    Article  PubMed  Google Scholar 

  33. Shi YH, Zeng WM, Wang NZ, Chen DTL (2015) A novel fMRI group data analysis method based on data-driven reference extracting from group subjects. Comput Methods Prog Biomed 122:362–371

    Article  Google Scholar 

  34. Bell AJ, Sejnowski TJ (1995) An information maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159

    Article  CAS  PubMed  Google Scholar 

  35. Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9:1483–1492

    Article  Google Scholar 

  36. Du YH, Fan Y (2013) Group information guided ICA for fMRI data analysis. NeuroImage 6:157–197

    Article  Google Scholar 

  37. Klamroth K, Tind J (2007) Constrained optimization using multiple objective programming. J Glob Optim 37:325–355

    Article  Google Scholar 

  38. Correa N, Adali T, Li YO, Calhoun VD (2005) Comparison of blind source separation algorithms for FMRI using a new Matlab toolbox: Gift. IEEE Int Conf Acoust Speech Signal Process 5:401–404

    Google Scholar 

  39. Shi YH, Zeng WM, Wang NZ, Zhao L (2017) A new method for independent component analysis with priori information based on multi-objective optimization. J Neurosci Methods 283:72–82

    Article  PubMed  Google Scholar 

  40. Himberg J, Hyvarinen A, Esposito F (2004) Validating the independent components of neuro- imaging time series via clustering and visualization. NeuroImage 22:1214–1222

    Article  PubMed  Google Scholar 

  41. Li YO, Adali T, Calhoun VD (2007) Estimating the number of independent components for functional magnetic resonance imaging data. Hum Brain Mapp 28:1251–1266

    Article  PubMed  Google Scholar 

  42. Wang NZ, Zeng WM, Chen L (2013) SACICA: a sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis. J Neurosci Methods 216:49–61

    Article  PubMed  Google Scholar 

  43. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26:369–395

    Article  Google Scholar 

  44. Andersen AH, Rayens WS (2004) Structure-seeking multilinear methods for the analysis of fMRI data. NeuroImage 22:728–739

    Article  PubMed  Google Scholar 

  45. Beckmann CF, Smith SM (2005) Tensorial extensions of independent component analysis for multi-subject fMRI analysis. NeuroImage 25:294–311

    Article  CAS  PubMed  Google Scholar 

  46. Kuang LD, Lin QH, Gong XF, Cong FY, Calhoun VD (2013) Multi-subject fMRI data analysis: shift-invariant tensor factorization vs. group independent component analysis. In: 2013 I.E. China summit and international conference on signal and information processing, 269–272

  47. Cichocki A, Mandic D, Phan AH, Caiafa C, Zhou G, Zhao Q, Lathauwer L (2015) Tensor decompositions for signal processing applications from two-way to multiway component analysis. IEEE Signal Process Mag 32:145–163

    Article  Google Scholar 

  48. Kuang LD, Lin QH, Gong XF, Cong F, Sui J, Calhoun VD (2015) Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition. J Neurosci Methods 256:127–140

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grants No. 31470954, No. 61271446), the Research Foundation from Shanghai Science and Technology Project (Grant No. 14590501700), the Innovation Program of Shanghai Municipal Education Commission (Grant No.15ZZ079), the Programs for Graduate Special Endowment Fund for Innovative Developing (Grant No. 2015ycx081), and Excellent Doctoral Dissertation Cultivation (Grant No. 2015bxlp005) of Shanghai Maritime University.

Author information

Authors and Affiliations

  1. Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China

    Yuhu Shi, Weiming Zeng, Xiaoyan Tang, Wei Kong & Jun Yin

  2. Information Engineering College, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China

    Weiming Zeng

Authors
  1. Yuhu Shi
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  2. Weiming Zeng
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  3. Xiaoyan Tang
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  4. Wei Kong
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  5. Jun Yin
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Corresponding author

Correspondence to Weiming Zeng.

Appendices

Appendix 1

In this section, we provide a detailed description of extracting spatial a priori information from group data [33]. Similar to extracting temporal a priori information from group data as described in this paper, we first need to implement ICA at the single-subject level. Now assuming that we have obtained the ICs, S i (i = 1, 2, …, K), of each subject in the group using formula (6), then these ICs will be used to extract the spatial a priori information by principal component analysis (PCA). For simplicity, we consider the situation in which each subject in the group has one IC of interest corresponding to the group IC (GIC). The correspondence of the ICs across different subjects corresponding to the GIC can be measured using the absolute value of the spatial correlation [36].

We denote the location set of voxels in the mask of subject i as VLS i ( i = 1, 2, …, K), and \( {\boldsymbol{s}}_{\boldsymbol{i}{\boldsymbol{n}}_{\boldsymbol{i}}}\left(\ i=1,2,\dots, K\right) \) denotes the n i th IC of subject i corresponding to the GIC of interest. Then, we can calculate the location set of common activated voxels in all \( {\boldsymbol{s}}_{\boldsymbol{i}{\boldsymbol{n}}_{\boldsymbol{i}}}\left(\ i=1,2,\dots, K\right) \) at the same threshold θ and denote it as CAVLS:

$$ \boldsymbol{CAVLS}=\left\{j| abs\left({\boldsymbol{s}}_{\boldsymbol{i}{\boldsymbol{n}}_{\boldsymbol{i}}}\left(\boldsymbol{j}\right)\right)\ge \theta, j\in \boldsymbol{V}{\boldsymbol{LS}}_{\boldsymbol{i}},i=1,2,\dots, K\right\} $$
(s1)

Let \( {\boldsymbol{s}}_{{\boldsymbol{i}\boldsymbol{n}}_{\boldsymbol{i}}}^{\boldsymbol{c}}\left(i=1,2,\dots, K\right) \) denote the common voxels from \( {\boldsymbol{s}}_{\boldsymbol{i}{\boldsymbol{n}}_{\boldsymbol{i}}} \) with regard to the index CAVLS where \( {\boldsymbol{s}}_{{\boldsymbol{i}\boldsymbol{n}}_{\boldsymbol{i}}}^{\boldsymbol{c}} \)is a column vector of size v × 1 and can be retrieved as

$$ {\boldsymbol{s}}_{{\boldsymbol{i}\boldsymbol{n}}_{\boldsymbol{i}}}^{\boldsymbol{c}}=\left\{ abs\left({\boldsymbol{s}}_{\boldsymbol{i}{\boldsymbol{n}}_{\boldsymbol{i}}}(j)\right)|j\in \boldsymbol{CAVLS}\right\},\left(i=1,2,\dots, K\right) $$
(s2)

Here, the absolute value in \( {\boldsymbol{s}}_{\boldsymbol{i}{\boldsymbol{n}}_{\boldsymbol{i}}} \) is used in formula (s1) and formula (s2) due to the network of interest possibly having negative activation in the IC. Although it may mean that \( {\boldsymbol{s}}_{{\boldsymbol{i}\boldsymbol{n}}_{\boldsymbol{i}}}^{\boldsymbol{c}}\left(\ i=1,2,\dots, K\right) \) contains some noise, the spatial reference is extracted from all \( {\boldsymbol{s}}_{{\boldsymbol{i}\boldsymbol{n}}_{\boldsymbol{i}}}^{\boldsymbol{c}}\left(\ i=1,2,\dots, K\right) \) by PCA, which has the ability to reduce the noise.

Now we use PCA to calculate the spatial reference signal from the K × v matrix R which consists of all\( {\boldsymbol{s}}_{{\boldsymbol{i}\boldsymbol{n}}_{\boldsymbol{i}}}^{\boldsymbol{c}}\left(i=1,2,\dots, K\right) \):

$$ \boldsymbol{R}={\left[{\boldsymbol{s}}_{1{\boldsymbol{n}}_1}^{\boldsymbol{c}},{\boldsymbol{s}}_{2{\boldsymbol{n}}_2}^{\boldsymbol{c}},\dots, {\boldsymbol{s}}_{{\boldsymbol{K}\boldsymbol{n}}_{\boldsymbol{K}}}^{\boldsymbol{c}}\right]}^{\prime } $$
(s3)

Then, the eigenvalue λ k  (k = 1, 2, …, K) such that λ 1 ≥ λ 2 ≥  ⋯  ≥ λ K  ≥ 0, and the corresponding eigenvectors e k  (k = 1, 2, …, K) of the covariance matrix C = E[RR ′] can be calculated, where e k  is a column vector of size K × 1. Finally, we selected the first principal component as the spatial reference r:

$$ \boldsymbol{r}={\boldsymbol{e}}_1^{\prime}\boldsymbol{R} $$
(s4)

where r is a row vector of size 1 × v and the corresponding contribution of r can be calculated by \( {c}_r={\lambda}_1/\sum_{k=1}^K{\lambda}_k \). In particular, if all subjects in the group have the same mask, then the spatial a priori information can be obtained directly through formulas (s3) and (s4).

Appendix 2

Table 1 The evaluation results of the nine situations of the improved CICA method obtained by formula (14) on the 20 datasets in the simulated-data experiment
Full size table

aThe bold numbers indicated the "index" values of the best situation obtained by formula (14) in each simulated dataset.

Table 2 The evaluation results of the nine situations of the improved CICA method obtained by formula (14) in the real-data experiment
Full size table

bThe bold number indicated the "index" value of the best situation obtained by formula (14) in the real-data experiment.

Table 3 The average of AUCs and CCs of the nine situations of the proposed improved CICA method on the 20 datasets in the simulated-data experiment
Full size table

cThe bold numbers indicated the average of AUCs and CCs of the best situation in each simulated dataset.

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Shi, Y., Zeng, W., Tang, X. et al. An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis. Med Biol Eng Comput 56, 683–694 (2018). https://doi.org/10.1007/s11517-017-1716-9

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  • DOI: https://doi.org/10.1007/s11517-017-1716-9

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