Asian Journal of Information Technology
1607 - 1620
Functional Mribold Interpretation of Brain Functions Using
Independent Component Analysis
Bai, B., P. Kantor, A. Shoukoufandeh and D. Silver, 2007. FMRI brain image retrieval based on ICA components. Proceedings of the ENC 2007 Eighth Mexican International Conference on Current Trends in Computer Science, September 24-28, 2007, IEEE, Michoacan, Mexico, ISBN: 978-0-7695-2899-1, pp: 10-17.
Bloch, C., A. Kaiser, E. Kuenzli, D. Zappatore and S. Haller et al
., 2009. The age of second language acquisition determines the variability in activation elicited by narration in three languages in Brocas and Wernickes area. Neuropsychologia, 47: 625-633.CrossRef | PubMed | Direct Link |
Calhoun, V., G. Pearlson and T. Adali, 2004. Independent component analysis applied to FMRI data: A generative model for validating results. J. VLSI. Signal Process. Syst. Signal Image Video Technol., 37: 281-291.CrossRef | Direct Link |
Calhoun, V.D. and T. Adali, 2006. Unmixing fMRI with independent component analysis. IEEE. Eng. Med. Biol. Mag., 25: 79-90.CrossRef | Direct Link |
Calhoun, V.D., T. Adali, G.D. Pearlson and J.J. Pekar, 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp., 14: 140-151.CrossRef | Direct Link |
Calhoun, V.D., T. Adali, G.D. Pearlson and J.J. Pekar, 2001. Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum. Brain Mapp., 13: 43-53.CrossRef | PubMed | Direct Link |
Calhoun, V.D., T. Adali, M.C. Stevens, K.A. Kiehl and J.J. Pekar, 2005. Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis. Neuroimage, 25: 527-538.CrossRef | PubMed | Direct Link |
Calhoun, V.D., T. Adali, V.B. McGinty, J.J. Pekar and T.D. Watson et al
., 2001. FMRI activation in a visual-perception task: Network of areas detected using the general linear model and independent components analysis. NeuroImage, 14: 1080-1088.CrossRef | PubMed | Direct Link |
Correa, N., T. Adalı and V.D. Calhoun, 2007. Performance of blind source separation algorithms for fMRI analysis using a group ICA method. Magn. Reson. Imaging, 25: 684-694.Direct Link |
Cox, D.D. and R.L. Savoy, 2003. Functional Magnetic Resonance Imaging (FMRI) brain reading: Detecting and classifying distributed patterns of FMRI activity in human visual cortex. Neuroimage, 19: 261-270.CrossRef | PubMed | Direct Link |
Dea, J.T., M. Anderson, E. Allen, V.D. Calhoun and T. Adalı, 2011. IVA for multi-subject FMRI analysis: a comparative study using a new simulation toolbox. Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, September 18-21, 2011, IEEE, Santander, Spain, ISBN: 978-1-4577-1621-8, pp: 1-6.
Dronkers, N.F., O. Plaisant, I.M.T. Zizen and E.A. Cabanis, 2007. Paul Brocas historic cases: High resolution MR imaging of the brains of Leborgne and Lelong. Brain, 130: 1432-1441.PubMed | Direct Link |
Esposito, F., E. Formisano, E. Seifritz, R. Goebel and R. Morrone et al
., 2002. Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?. Hum. Brain Mapp., 16: 146-157.CrossRef | PubMed | Direct Link |
Esposito, F., T. Scarabino, A. Hyvarinen, J. Himberg and E. Formisano et al
., 2005. Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 25: 193-205.CrossRef | Direct Link |
Friston, K.J., C.D. Frith, R. Turner and R.S. Frackowiak, 1995. Characterizing evoked hemodynamics with FMRI. Neuroimage, 2: 157-165.CrossRef | Direct Link |
Genovese, C.R., N.A. Lazar and T. Nichols, 2002. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage, 15: 870-878.CrossRef | Direct Link |
Ghasemi, M. and A. Mahloojifar, 2010. FMRI data analysis by blind source separation algorithms: A comparison study for nongaussian properties. Proceedings of the 2010 18th Iranian Conference on Electrical Engineering, May 11-13, 2010, IEEE, Isfahan, Iran, ISBN: 978-1-4244-6760-0, pp: 13-17.
Just, M.A., P.A. Carpenter, T.A. Keller, W.F. Eddy and K.R. Thulborn, 1996. Brain activation modulated by sentence comprehension. Sci., 274: 114-116.Direct Link |
Kelly, R.E., G.S. Alexopoulos, Z. Wang, F.M. Gunning and C.F. Murphy et al
., 2010. Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data. J. Neurosci. Methods, 189: 233-245.CrossRef | PubMed | Direct Link |
Kwong, K.K., J.W. Belliveau, D.A. Chesler, I.E. Goldberg and R.M. Weisskoff et al
., 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. National Acad. Sci., 89: 5675-5679.CrossRef | Direct Link |
Lai, S.H. and M. Fang, 1999. A novel local PCA-based method for detecting activation signals in fMRI. Magn. Reson. Imaging, 17: 827-836.CrossRef | PubMed | Direct Link |
Lee, C.W., D.Y. Chen, C.W. Wu and J.H. Chen, 2007. Comparing the spatial and temporal reproducibility of brain activation using three fMRI techniques: BOLD, FAIR and VASO. Proceedings of the NFSI-ICFBI 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, October 12-14, 2007, IEEE, Hangzhou , China, ISBN: 978-1-4244-0949-5, pp: 258-261.
Lee, S., F. Zelaya, Y. Samarasinghe, S.A. Amiel and M.J. Brammer, 2011. Data-driven fMRI group classification using connected components and Gaussian process classifiers. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 22-27, 2011, IEEE, Prague, Czech Republic, ISBN: 978-1-4577-0538-0, pp: 717-720.
Li, H., T. Adal, N. Correa, P.A. Rodriguez and V.D. Calhoun, 2010. Flexible complex ICA of FMRI data. Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, March 14-19, 2010, IEEE, Dallas, Texas, ISBN: 978-1-4244-4295-9, pp: 2050-2053.
Maaten, L.V.D., E. Postma and J.V.D. Herik, 2009. Dimensionality reduction: A comparative. J. Mach. Learn. Res., 10: 66-71.Direct Link |
McKeown, M.J. and T.J. Sejnowski, 1998. Independent component analysis of fMRI data: Examining the assumptions. Hum. Brain Mapp., 6: 368-372.Direct Link |
McKeown, M.J., T.P. Jung, S. Makeig, G. Brown and S.S. Kindermann et al
., 1998. Spatially independent activity patterns in functional MRI data during the stroop color-naming task. Proc. National Acad. Sci., 95: 803-810.Direct Link |
McKeown, M.J., V. Varadarajan, S. Huettel and G. McCarthy, 2002. Deterministic and stochastic features of fMRI data: Implications for analysis of event-related experiments. J. Neurosci. Methods, 118: 103-113.CrossRef | PubMed | Direct Link |
Naik, G.R. and D.K. Kumar, 2011. An overview of independent component analysis and its applications. Inf., 35: 63-81.
Richards, T.L. and V.W. Berninger, 2008. Abnormal fMRI connectivity in children with dyslexia during a phoneme task: Before but not after treatment. J. Neurolinguistics, 21: 294-304.CrossRef | PubMed | Direct Link |
Rissanen, J., 1978. Modeling by shortest data description. Automatica, 14: 465-471.CrossRef | Direct Link |
Schopf, V., C. Windischberger, C.H. Kasess, R. Lanzenberger and E. Moser, 2010. Group ICA of resting-state data: A comparison. Magn. Reson. Mate. Phys. Biol. Med., 23: 317-325.CrossRef | Direct Link |
Spiers, H.J. and E.A. Maguire, 2007. Decoding human brain activity during real-world experiences. Trends Cognit. Sci., 11: 356-365.CrossRef | PubMed | Direct Link |
Sui, J., T. Adali, G.D. Pearlson and V.D. Calhoun, 2009. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage, 46: 73-86.CrossRef | PubMed |
Svensen, M., F. Kruggel and H. Benali, 2002. ICA of fMRI group study data. NeuroImage, 16: 551-563.CrossRef | PubMed | Direct Link |
Thirion, B., P. Pinel, A. Tucholka, A. Roche and P. Ciuciu et al
., 2007. Structural analysis of FMRI data revisited: Improving the sensitivity and reliability of FMRI group studies. IEEE. Trans. Med. Imaging, 26: 1256-1269.CrossRef | PubMed | Direct Link |
Tsatsishvili, V., F. Cong, T. Puolivali, V. Alluri and P. Toiviainen et al
., 2013. Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, April 24-26, 2013, Jyvaskyla University, Bruges, Belgium, ISBN: 978-2-87419-081-0, pp: 137-142.
Turner, G.H. and D.B. Twieg, 2005. Study of temporal stationarity and spatial consistency of FMRI noise using independent component analysis. IEEE. Trans. Med. Imaging, 24: 712-718.CrossRef | PubMed | Direct Link |
Weissenbacher, A., C. Kasess, F. Gerstl, R. Lanzenberger and E. Moser et al
., 2009. Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies. Neuroimage, 47: 1408-1416.CrossRef | PubMed | Direct Link |
Woods, R.P., 1996. Modeling for intergroup comparisons of imaging data. Neuroimage, 4: S84-S94.CrossRef | PubMed | Direct Link |
Zhang, J., X. Tuo, Z. Yuan, W. Liao and H. Chen, 2011. Analysis of fMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach. IEEE. Trans. Biomed. Eng., 58: 3184-3196.CrossRef | PubMed | Direct Link |