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Cognitive Neurodynamics

, Volume 12, Issue 5, pp 461–470 | Cite as

Examining stability of independent component analysis based on coefficient and component matrices for voxel-based morphometry of structural magnetic resonance imaging

  • Qing Zhang
  • Guoqiang Hu
  • Lili Tian
  • Tapani Ristaniemi
  • Huili Wang
  • Hongjun Chen
  • Jianlin Wu
  • Fengyu Cong
Research Article
  • 129 Downloads

Abstract

Independent component analysis (ICA) on group-level voxel-based morphometry (VBM) produces the coefficient matrix and the component matrix. The former contains variability among multiple subjects for further statistical analysis, and the latter reveals spatial maps common for all subjects. ICA algorithms converge to local optimization points in practice and the mostly applied stability investigation approach examines the stability of the extracted components. We found that the practically stable components do not guarantee to produce the practically stable coefficients of ICA decomposition for the further statistical analysis. Consequently, we proposed a novel approach including two steps: (1), the stability index for the coefficient matrix and the stability index for the component matrix were examined, respectively; (2) the two indices were multiplied to analyze the stability of ICA decomposition. The proposed approach was used to study the sMRI data of Type II diabetes mellitus group and the healthy control group (HC). Group differences in VBM were found in the superior temporal gyrus. Besides, it was revealed that the VBMs of the region of the HC group were significantly correlated with Montreal Cognitive Assessment (MoCA) describing the level of cognitive disorder. In contrast to the widely applied approach to investigating the stability of the extracted components for ICA decomposition, we proposed to examine the stability of ICA decomposition by fusion the stability of both coefficient matrix and the component matrix. Therefore, the proposed approach can examine the stability of ICA decomposition sufficiently.

Keywords

Diabetes Voxel-based morphometry Independent component analysis Back-projection Montreal cognitive assessment Stability Coefficient matrix Component matrix 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 81471742, 81371526) and the Fundamental Research Funds for the Central Universities [DUT16JJ(G)03] in Dalian University of Technology in China. Gratitude goes forward to the Affiliated Zhongshan Hospital of Dalian University for DICOM data collection and Xichu Zhu and Jianrong Li in Dalian University of Technology for language editing.

References

  1. Alagiakrishnan K, Zhao N, Mereu L, Senior PA, Senthilselvan A (2014) Montreal cognitive assessment is superior to standardized mini-mental status exam in detecting mild cognitive impairment in the middle-aged and elderly patients with type 2 diabetes mellitus (vol 2013, 186106, 2013). Biomed Res Int 2013:648472Google Scholar
  2. Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11:805–821CrossRefGoogle Scholar
  3. Barnathan M, Megalooikonomou V, Faloutsos C, Faro S, Mohamed FB (2011) TWave: high-order analysis of functional MRI. Neuroimage 58:537–548CrossRefGoogle Scholar
  4. Calhoun VD, Adali T, Giuliani NR, Pekar JJ, Kiehl KA, Pearlson GD (2006) Method for multimodal analysis of independent source differences in schizophrenia: Combining gray matter structural and auditory oddball functional data. Human Brain Mapp 27(1):47-62CrossRefGoogle Scholar
  5. Chen Z, Li L, Sun J, Ma L (2012) Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL. Eur J Radiol 81:1870–1876CrossRefGoogle Scholar
  6. Cong F, Kalyakin I, Ristaniemi T (2011a) Can back-projection fully resolve polarity indeterminacy of independent component analysis in study of event-related potential? Biomed Signal Process Control 6:422–426CrossRefGoogle Scholar
  7. Cong F, Kalyakin I, Chang Z, Ristaniemi T (2011b) Analysis on subtracting projection of extracted independent components from EEG recordings. Biomed Eng-Biomed Te 56(4):223–234CrossRefGoogle Scholar
  8. Cong F, Puoliväli T, Alluri V, Sipola T, Burunat I, Toiviainen P, Nandi AK, Brattico E, Ristaniemi T (2014) Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis. J Neurosci Methods 223:74–84CrossRefGoogle Scholar
  9. Correa N, Adalı T, Calhoun VD (2007) Performance of blind source separation algorithms for fMRI analysis using a group ICA method. Magn Reson Imaging 25(5):684–694CrossRefGoogle Scholar
  10. Cong F, Lin Q-H, Kuang L-D, Gong X-F, Astikainen P, Ristaniemi T (2015) Tensor decomposition of EEG signals: a brief review. J Neurosci Methods 248:59–69CrossRefGoogle Scholar
  11. Déli E, Tozzi A, Peters JF (2017) Relationships between short and fast brain timescales. Cogn Neurodyn 11:539–552CrossRefGoogle Scholar
  12. Eklund A, Nichols TE, Knutsson H (2016) Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci 113:201602413CrossRefGoogle Scholar
  13. Erus G, Battapady H, Zhang T, Lovato J, Miller ME, Williamson JD, Launer LJ, Bryan RN, Davatzikos C (2015) Spatial patterns of structural brain changes in type 2 diabetic patients and their longitudinal progression with intensive control of blood glucose. Diabetes Care 38:97–104CrossRefGoogle Scholar
  14. Gupta CN, Calhoun VD, Rachakonda S, Chen J, Patel V, Liu J, Segall J, Franke B, Zwiers MP, Arias-Vasquez A, Buitelaar J, Fisher SE, Fernandez G, van Erp TG, Potkin S, Ford J, Mathalon D, McEwen S, Lee HJ, Mueller BA, Greve DN, Andreassen O, Agartz I, Gollub RL, Sponheim SR, Ehrlich S, Wang L, Pearlson G, Glahn DC, Sprooten E, Mayer AR, Stephen J, Jung RE, Canive J, Bustillo J, Turner JA (2015) Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis. Schizophr Bull 41(5):1133–1142CrossRefGoogle Scholar
  15. Himberg J, Hyvärinen A, Esposito F (2004) Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage 22:1214–1222CrossRefGoogle Scholar
  16. Hyvarinen A (1999) Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans Neural Netw Learn Syst 10:626–634CrossRefGoogle Scholar
  17. Hyvärinen A, Karhunenen J, Oja E (2001) Independent component analysis. Neural Comput 13:504Google Scholar
  18. Kim DJ, Yu JH, Shin MS, Shin YW, Kim MS (2016) Hyperglycemia reduces efficiency of brain networks in subjects with type 2 diabetes. PLoS ONE 11:1–14Google Scholar
  19. Kurth F, Luders E, Angeles L (2015) Voxel-based morphometry. Neuroimage 1:345–349Google Scholar
  20. Li Y-O, Adalı T, Calhoun VD (2007) Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapp 28(11):1251–1266CrossRefGoogle Scholar
  21. Luo L, Xu L, Jung R, Pearlson G, Adali T, Calhoun VD (2012) Constrained Source-Based Morphometry Identifies Structural Networks Associated with Default Mode Network. Brain Connect 2(1):33–43CrossRefGoogle Scholar
  22. Ma S, Correa NM, Li X-L, Eichele T, Calhoun VD, Adali T (2011) Automatic identification of functional clusters in fMRI data using spatial dependence. IEEE Trans Biomed Eng 58:3406–3417CrossRefGoogle Scholar
  23. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH (2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19:1233–1239CrossRefGoogle Scholar
  24. Mccrimmon RJ, Phd R, Mccrimmon RJ, Ryan CM, Frier BM (2012) Diabetes 2 Diabetes and cognitive dysfunction. www.thelancet.com. Lancet 379:2291–2299CrossRefGoogle Scholar
  25. Moran C, Phan TG, Chen J, Blizzard L, Beare R, Venn A, Münch G, Wood AG, Forbes J, Greenaway TM, Pearson S, Srikanth V (2013) Brain atrophy in type 2 diabetes: regional distribution and influence on cognition. Diabetes Care 36:4036–4042CrossRefGoogle Scholar
  26. Segall JM, Allen EA, Jung RE, Erhardt EB, Arja SK, Kiehl K, Calhoun VD (2012) Correspondence between structure and function in the human brain at rest. Front Neuroinform 6:10.  https://doi.org/10.3389/fninf.2012.00010 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Sejnowski TJ, Bell AJ (1995) Information-maximization approach to blind separation and blind deconvolution. Neural Computation 7:1129–1159CrossRefGoogle Scholar
  28. Spauwen PJJ, Köhler S, Verhey FRJ, Stehouwer CDA, Van Boxtel MPJ (2013) Effects of type 2 diabetes on 12-year cognitive change: results from the maastricht aging study. Diabetes Care 36:1554–1561CrossRefGoogle Scholar
  29. Sui J, Adali T, Yu Q, Chen J, Calhoun VD (2012) A review of multivariate methods for multimodal fusion of brain imaging data. J Neurosci Methods 204(1):68–81CrossRefGoogle Scholar
  30. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289CrossRefGoogle Scholar
  31. Xu L, Groth KM, Pearlson G, Schretlen DJ, Vince D (2009) Source-Based morphometry: the use of independent component analysis to identify gray matter differences with application to Schizophrenia. Human Brain Mapp 30:711–724CrossRefGoogle Scholar
  32. Yan CG, Wang XD, Zuo XN, Zang YF (2016) DPABI: data processing and analysis for (resting-state) brain imaging. Neuroinformatics 14:339–351CrossRefGoogle Scholar
  33. Zhang Y, Zhang X, Zhang J, Liu C, Yuan Q, Yin X, Wei L, Cui J, Tao R, Wei P, Wang J (2014) Gray matter volume abnormalities in type 2 diabetes mellitus with and without mild cognitive impairment. Neurosci Lett 562:1–6CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of RadiologyAffiliated Zhongshan Hospital of Dalian UniversityDalianChina
  2. 2.Department of Biomedical Engineering, Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina
  3. 3.Department of PsychologyUniversity of JyvaskylaJyvaskylaFinland
  4. 4.Faculty of Information TechnologyUniversity of JyvaskylaJyvaskylaFinland
  5. 5.School of Foreign LanguagesDalian University of TechnologyDalianChina

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