참고문헌
- Absher, J. R. and Benson, D. F. (1993). Disconnection syndromes: an overview of Geschwind's contributions. Neurology, 43, 862-867. https://doi.org/10.1212/WNL.43.5.862
- Astolfi, L., Cincotti, F., Mattia, D., Salinari, S., Babiloni, C., Basilisco, A., Rossini, P. M., Ding, L., Ni, Y., He, B., Marciani, M. G., and Babiloni., F. (2004). Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG. Magnetic Resonance Imaging, 22, 1457-1470. https://doi.org/10.1016/j.mri.2004.10.006
- Buchel, C., Friston, K. J. (1997). Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cerebral Cortex, 7, 768-778. https://doi.org/10.1093/cercor/7.8.768
- Bullmore, E. and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186-198. https://doi.org/10.1038/nrn2575
- Churchland, P. S. and Sejnowski, T. J. (1998). Perspectives on cognitive neuroscience. Science, 242, 741-745.
- Daunizeau, J., Kiebel, S. J., and Friston, K. J. (2009). Dynamic causal modelling of distributed electromagnetic responses. Neuroimage, 47, 590-601. https://doi.org/10.1016/j.neuroimage.2009.04.062
- Deshpande, G., Sathian, K., and Hu, X. (2010). Assessing and compensating for zero-lag correlation effects in time-lagged Granger causality analysis of FMRI. IEEE Transactions on Biomedical Engineering, 57, 1446-1456. https://doi.org/10.1109/TBME.2009.2037808
- Friston, K. J. (2005). Models of brain function in neuroimaging. Annual Review of Psychology, 56, 57-87. https://doi.org/10.1146/annurev.psych.56.091103.070311
- Friston, K. J. (2011). Functional and effective connectivity: a review. Brain Connectivity, 1, 13-36. https://doi.org/10.1089/brain.2011.0008
- Friston, K. J., Bastos, A. M., Oswal, A., van Wijk, B., Richter, C., and Litvak, V. (2014). Granger causality revisited. NeuroImage, 101, 796-808. https://doi.org/10.1016/j.neuroimage.2014.06.062
- Friston, K. J., Harrison, L., and Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19, 1273-1302. https://doi.org/10.1016/S1053-8119(03)00202-7
- Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. B., Frith, C. D., and Frackowiak, R. S. J. (1995). Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2, 189-210.
- Genovese, C. R., Lazar, N. A., and Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage, 15, 870-878. https://doi.org/10.1006/nimg.2001.1037
- Goebel, R., Roebroeck, A., Kim, D. S., and Formisano, E. (2003). Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magnetic Resonance Imaging, 21, 1251-1261. https://doi.org/10.1016/j.mri.2003.08.026
- Goltz, F. (1881). Transactions of the 7th International Medical Congress (MacCormac ed.), I, 218-228, JW Kolkmann, London.
- Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M., and Ramadge, P. J. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72, 404-416. https://doi.org/10.1016/j.neuron.2011.08.026
- Huettel, S. A., Song, A. W., and McCarthy, G. (2009). Functional Magnetic Resonance Imaging (Vol. 1), Sinauer Associates, Sunderland.
- Kim, J., Zhu, W., Chang, L., Bentlerm P. M., and Ernst, T. (2006). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping, 28, 85-93.
- Lee, N., Choi, H., and Kim, S.-H. (2016a). Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise. Computational Statistics & Data Analysis, 101, 250-276. https://doi.org/10.1016/j.csda.2016.03.007
- Lee, N., Kim, A.-Y., Park, C.-H., and Kim, S.-H. (2016b). An Improvement on Local FDR Analysis Applied to Functional MRI Data. Journal of Neuroscience Methods, 267, 115-125. https://doi.org/10.1016/j.jneumeth.2016.04.013
- Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453, 869-878. https://doi.org/10.1038/nature06976
- Marreiros, A. C., Kiebel, S. J., and Friston, K. J. (2008). Dynamic causal modelling for fMRI: A two-state model. NeuroImage, 39, 269-278. https://doi.org/10.1016/j.neuroimage.2007.08.019
- Marrelec, G., Kim, J., Doyon, J., and Horwitz, B. (2009). Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI. Human Brain Mapping, 30, 941-950. https://doi.org/10.1002/hbm.20555
- McIntosh, A. and Gonzales-Lima, F.(1994). Structural equation modeling and its application to network analysis in functional brain imaging. Human Brain Mapping, 2, 2-22. https://doi.org/10.1002/hbm.460020104
- Ni, S. and Sun, D. (2005). Bayesian estimates for vector autoregressive models. Journal of Business and Economic Statistics, 23, 105-117. https://doi.org/10.1198/073500104000000622
- Nichols, T. and Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: a comparative review. Statistical Methods in Medical Research, 12, 419-446. https://doi.org/10.1191/0962280203sm341ra
- Opgen-Rhein, R. and Strimmer, K. (2007). Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinform, 8, S3.
- Penny, W. D., Stephan, K. E., Mechelli, A., and Friston, K. J. (2004). Modelling functional integration: a comparison of structural equation and dynamic causal and models. NeuroImage, 23, 264-274. https://doi.org/10.1016/j.neuroimage.2004.07.041
- Schafer, J. and Strimmer, K. (2005). A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology, 4, 32.
- Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., and Woolrich, M. W. (2011). Network modelling methods for FMRI. NeuroImage, 54, 875-891. https://doi.org/10.1016/j.neuroimage.2010.08.063
- Staum, M. (1995). Physiognomy and phrenology at the Paris Athenee. Journal of the History of Ideas, 56, 443-462. https://doi.org/10.2307/2710035
- Stephan, K. E. and Friston, K. J. (2011). Analyzing effective connectivity with fMRI. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 446-459.
- Strimmer, K. (2008). ftrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics, 24, 1461-1462. https://doi.org/10.1093/bioinformatics/btn209
- Zhou, D., Thompson, W. K., and Siegle, G. (2009). MATLAB toolbox for functional connectivity. Neuroimage, 47, 1590-1607. https://doi.org/10.1016/j.neuroimage.2009.05.089