×

Multilevel dynamic generalized structured component analysis for brain connectivity analysis in functional neuroimaging data. (English) Zbl 1345.62151

Psychometrika 81, No. 2, 565-581 (2016); erratum ibid. 81, No. 2, 582–584 (2016).
Summary: We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

MSC:

62P15 Applications of statistics to psychology
62H25 Factor analysis and principal components; correspondence analysis
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62P10 Applications of statistics to biology and medical sciences; meta analysis
92C55 Biomedical imaging and signal processing

Software:

GeSCA
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Cairo, T. A., Woodward, T. S., & Ngan, E. T. (2006). Decreased encoding efficiency in schizophrenia. Biological Psychiatry, 59, 740-746. · doi:10.1016/j.biopsych.2005.08.009
[2] Chen, G., Glen, D. R., Saad, Z. S., Hamilton, J. P., Thomason, M. E., Gotlib, I. H., et al. (2011). Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis. Computers in Biology and Medicine, 41, 1142-1155. · doi:10.1016/j.compbiomed.2011.09.004
[3] deLeeuw, J., Young, F. W., & Takane, Y. (1976). Additive structure in qualitative data: An alternating least squares method with optimal scaling features. Psychometrika, 41, 471-503. · Zbl 0351.92031 · doi:10.1007/BF02296971
[4] Efron, B. (1982). The jackknife, the bootstrap and other resampling plans. Philadelphia: SIAM. · Zbl 0496.62036 · doi:10.1137/1.9781611970319
[5] Friston, K. J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping, 2, 56-78. · doi:10.1002/hbm.460020107
[6] Gates, K. M., Molenaar, P. C., Hillary, F. G., & Slobounov, S. (2011). Extended unified SEM approach for modeling event-related fMRI data. NeuroImage, 54, 1151-1158. · doi:10.1016/j.neuroimage.2010.08.051
[7] Greve, D. N., Brown, G. G., Mueller, B. A., Glover, G., & Liu, T. T. (2013). A survey of the sources of noise in fMRI. Psychometrika, 78, 396-416. · Zbl 1284.62709 · doi:10.1007/s11336-012-9294-0
[8] Hwang, H., & Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69, 81-99. · Zbl 1306.62437 · doi:10.1007/BF02295841
[9] Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A component-based approach to structural equation modeling. Boca Raton, FL: Chapman & Hall/CRC Press. (forthcoming). · Zbl 1341.62033
[10] Hwang, H., Ho, R. M., & Lee, J. (2010). Generalized structured component analysis with latent interactions. Psychometrika, 75, 228-242. · Zbl 1234.62104 · doi:10.1007/s11336-010-9157-5
[11] Jung, K., Takane, Y., Hwang, H., & Woodward, T. S. (2012). Dynamic GSCA (Generalized Structured Component Analysis) with applications to the analysis of effective connectivity in functional neuroimaging data. Psychometrika, 77, 827-848. · Zbl 1284.62718 · doi:10.1007/s11336-012-9284-2
[12] Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping, 28, 85-93. · doi:10.1002/hbm.20259
[13] Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage Publication. · Zbl 0953.62127
[14] Takane, Y. (2013). Constrained principal component analysis and related techniques. Boca Raton, FL: Chapman and Hall/CRC Press. · Zbl 1282.62150
[15] Takane, Y., & Hunter, M. A. (2001). Constrained principal component analysis: A comprehensive theory. Applicable Algebra in Engineering, Communication and Computing, 12, 391-419. · Zbl 1040.62050 · doi:10.1007/s002000100081
[16] Takane, Y., & Shibayama, T. (1991). Principal component analysis with external information on both subjects and variables. Psychometrika, 56, 97-120. · Zbl 0725.62055 · doi:10.1007/BF02294589
[17] Van Den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20, 519-534. · doi:10.1016/j.euroneuro.2010.03.008
[18] Woodward, T. S., Cairo, T. A., Ruff, C. C., Takane, Y., Hunter, M. A., & Ngan, E. T. C. (2006). Functional connectivity reveals load dependent neural systems underlying encoding and maintenance in verbal working memory. Neuroscience, 139, 317-325. · doi:10.1016/j.neuroscience.2005.05.043
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.