Analysis of fMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach

IEEE Transactions on Biomedical Engineering

Volumn 58, Issue 11, 2011, Pages 3184-3196

  Zhang, Jiang ^a,b   Tuo, Xianguo ^c   Yuan, Zhen ^d   Liao, Wei ^a   Chen, Huafu ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b SOUTHWEST JIAOTONG UNIVERSITY   (China)

^c CHENGDU UNIVERSITY OF TECHNOLOGY   (China)

^d University of Florida   (United States)

Author keywords

Functional magnetic resonance imaging (fMRI); hierarchical clustering (HC); k centers clustering; principal component analysis (PCA); supervised affinity propagation clustering analysis (SAPC)

Indexed keywords

AFFINITY PROPAGATION; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HIERARCHICAL CLUSTERING; K-CENTERS CLUSTERING; PRINCIPAL COMPONENT ANALYSIS (PCA);

ACTIVATION ANALYSIS; DATA FLOW ANALYSIS; DESIGN; INTEGRATED CONTROL; INTEGRATION; MAGNETIC RESONANCE IMAGING; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; RESONANCE; TIME SERIES; TIME SERIES ANALYSIS;

CLUSTER ANALYSIS;

ALGORITHM; ARTICLE; BRAIN FUNCTION; FUNCTIONAL MAGNETIC RESONANCE IMAGING; MATHEMATICAL ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SIMULATION;

ALGORITHMS; BRAIN; BRAIN MAPPING; CLUSTER ANALYSIS; COMPUTER SIMULATION; HUMANS; MAGNETIC RESONANCE IMAGING; PHOTIC STIMULATION; PRINCIPAL COMPONENT ANALYSIS; ROC CURVE; SIGNAL PROCESSING, COMPUTER-ASSISTED; SIGNAL-TO-NOISE RATIO; TASK PERFORMANCE AND ANALYSIS;

EID: 80054853130     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2011.2165542     Document Type: Article

References (46)

1. H. Luo and S. Puthusserypady, "Estimation of the hemodynamic responseof fMRI Data using RBF neural network," IEEE Trans. Biomed. Eng.,vol. 54, no. 8, pp. 1371-1381, Aug. 2007.
2. K. J. Friston, A. P. Holmes, K. J. Worsley, J. P. Poline, C. D. Frith, andR. S. J. Frackowiak, "Statistical parametric maps in functional imaging: Ageneral linear approach," Hum. Brain Mapp., vol. 2, pp. 189-210, 1995.
3. G. M. Boynton, S. A. Engel, G. H. Glover, and D. J. Heeger, "Linearsystems analysis of functional magnetic resonance imaging in humanV1," J. Neurosci., vol. 16, pp. 4207-4221, 1996.
4. S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, "Brain magneticresonance imaging with contrast dependent on blood oxygenation," Proc.Nat. Acad. Sci., vol. 87, pp. 9868-9872, 1990.
5. P.McCullagh and J. A. Nelder, "Generalized linear models," in Number 37in Monographs on Statistics and Applied Probability, 2nd ed. London,U.K.: Chapman & Hall, 1989.
6. K. Worsley and K. J. Friston, "Analysis of fMRI time-series revisited-Again," NeuroImage, vol. 2, pp. 173-181, 1995.
7. J. Xiong, J.-H. Gao, J. L. Lancaster, and P. T. Fox, "Assessment andoptimization of functional MRI analyses," Hum. Brain Mapp., vol. 4,pp. 153-167, 1996.
8. I. T. Jolliffe, Principal Component Analysis, 2nd ed. New York:Springer, 2002.
9. A. Hyvärinen and E. Oja, "Independent component analysis: Algorithmsand applications," Neural Netw., vol. 13, pp. 411-430, 2000.
10. Z. Zhou, Y. Chen,M. Ding, P. Wright, Z. Lu, and Y. Liu, "Analyzing brainnetworks with PCA and conditional Granger causality," Hum. BrainMapp., vol. 30, no. 7, pp. 2197-2206, 2009.
11. J. J. Sychra, P. A. Bandettini, N. Bhattacharya, and Q. Lin, "Syntheticimages by subspace transforms I. Principal components images and relatedfilters," Med. Phys., vol. 21, no. 2, pp. 193-201, 1994.
12. W. Backfrieder, R. Baumgartner, M. Samal, E. Moser, and H. Bergmann,"Quantification of I intensity variations in functional MR images usingrotated principal components," Phys. Med. Biol., vol. 41, pp. 1425-1438,1996.
13. M. J. McKeown, S. Makeig, G. G. Brown, T.-P. Jung, S. S. Kindermann,A. J. Bell, and T. J. Sejnowski, "Analysis of fMRI data by blind separationinto independent spatial components," Hum. BrainMapp., vol. 6, pp. 160-188, 1998.
14. M. J. McKeown, T.-P. Jung, S. Makeig, G. Brown, S. S. Kindermann,T.-W. Lee, and T. J. Sejnowski, "Spatially independent activity patternsin functional MRI data during the Stroop color-naming task," Proc. Nat.Acad. Sci., vol. 95, pp. 803-810, 1998.
15. Z. Zhou, M. Ding, Y. Chen, P. Wright, Z. Lu, and Y. Liu, "Detecting directionalinfluence in fMRI connectivity analysis using PCA based Grangercausality," Brain Res., vol. 1289, pp. 22-29, 2009.
16. J. R. Sato, C. E. Thomaz, E. F. Cardoso, A. Fujita, M. Martin, andE. Amaro, Jr, "Hyperplane navigation: A method to set individual scoresin fMRI group datasets," NeuroImage, vol. 42, pp. 1473-1480, 2008.
17. J. MacQueen, "Some methods for classification and analysis of multivariateobservations," in Proc. 5th Berkeley Symp. Math. Statistics Probability,1967, vol. 1, pp. 281-297.
18. G. Scarth, M. McIntyre, B. Wowk, and R. L. Somorjai, "Detection ofnovelty in functional images using fuzzy clustering," in Proc. 3rd Annu.Meet. Int. Soc. Magn. Reson. Med., Nice, France, 1995, p. 238.
19. W. Liao, H. F. Chen, Q. Yang, and X. Lei, "Analysis of fMRI data usingimproved self-organizing mapping and spatio-temporal metric hierarchicalclustering," IEEE Trans. Med. Imag., vol. 27, no. 10, pp. 1472-1483,Oct. 2008.
20. C. Goutte, P. Toft, E. Rostrup, F. A. Nielsen, and L. K. Hansen, "Onclustering fMRI time series," NeuroImage, vol. 9, pp. 298-310, 1999.
21. G. Marrelec, H. Benali, P. Ciuciu, M. Pélégrini-Issac, and J.-B. Poline,"Robust Bayesian estimation of the hemodynamic response function inevent-related BOLD fMRI usinusing basic physiological information,"Hum. Brain Mapp., vol. 19, pp. 1-17, 2003.
22. H. Chen, H. Yuan, D. Yao, L. Chen, and W. Chen, "An integrated neighborhoodcorrelation and hierarchical clustering approach of functionalMRI," IEEE Trans. Biomed. Eng., vol. 53, no. 3, pp. 452-458, Mar.2006.
23. E. Dimitriadou, M. Barth, C.Windischberger, K. Hornik, and E.Mose, "Aquantitative comparison of functional MRI cluster analysis," Artif. Intell.Med., vol. 31, pp. 57-71, 2004.
24. A. Baune, F. T. Sommer, M. Erb, D. Wildgruber, B. Kardatzki, G. Palm,and W. Grodd, "Dynamical cluster analysis of cortical fMRI activation, "NeuroImage, vol. 9, pp. 477-489, 1999.
25. K. H. Chuang, M. J. Chiu, C. C. Lin, and J. H. Chen, "Model-free functionalMRI analysis using Kohonen clustering neural network and fuzzyclustering," IEEE Trans. Med. Imag., vol. 18, no. 12, pp. 1117-1128,Dec. 1999.
26. R. B. Baumgartner, C. Windischberger, and E. Moser, "Quantification infunctional Magnetic Resonance Imaging: Fuzzy clustering vs. correlationanalysis," Magn. Reson. Imag., vol. 16, no. 2, pp. 115-125, 1998.
27. P. Filzmoser, R. Baumgartner, and E. Moser, "A hierarchical clusteringmethod for analyzing functional MR images," Magn. Reson. Imag.,vol. 17, no. 6, pp. 817-826, 1999.
28. E. Moser, R. Baumgartner, M. Barth, and C.Windischberger, "Explorativesignal processing in functionalMR imaging," Int. J. Imag. Syst. Technol.,vol. 10, pp. 166-176, 1999.
29. R. Baumgartner, L. Ryner, W. Richter, R. Summers, M. Jarmasz, andR. Somorjai, "Comparison of two exploratory data analysis methods forfMRI: Fuzzy clustering vs. principal component analysis," Magn. Reson.Imag., vol. 18, pp. 89-94, 2000.
30. E. Dimitriadou, M. Barth, C. Windischberger, K. Hornik, and E. Moser,"A quantitative comparison of functional MRI cluster analysis," Artif.Intell. Med., vol. 31, pp. 51-71, 2004.
31. M. Barth, M. Diemling, and E. Moser, "Modulation of signal changes ingradient-recalled echo functionalMRI with increasing echo time correlatewith model calculations," Magn. Reson. Imag., vol. 15, pp. 745-752, 1997.
32. A. Mezer, Y. Yovel, O. Pasternak, T. Gorfine, and Y. Assaf, "Cluster analysisof resting-state fMRI time series," NeuroImage, vol. 45, pp. 1117-1125, 2009.
33. A. K. Jain and R. C.Dubes, Algorithms for ClusteringData. EnglewoodCliffs, N.J.: Prentice-Hall, 1988.
34. H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithmwith applications in computer vision," IEEE Trans. Pattern Anal.Mach. Intell., vol. 21, no. 5, pp. 450-465, May 1999.
35. J. C. Bezdek, Pattern Recognition With Fuzzy Objective Functions Algorithms.New York: Plenum, 1981.
36. B. J. Frey and D. Dueck, "Clustering by passing messages between datapoints," Science, vol. 315, no. 5814, pp. 972-976, 2007.
37. T. E. Nichols1 and A. P. Holmes, "Nonparametric permutation tests forfunctional neuroimaging: A primer with examples," Hum. Brain Mapp.,vol. 15, pp. 1-25, 2001.
38. B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap. BocaRaton, FL: CRC Press, 1993, pp. 45-139.
39. F. J. Bryan and Manly, Randomization, Bootstrap and Monte Carlo Methodsin Biology. Boca Raton, FL: CRC Press, 2006, pp. 41-79.
40. J. Zhang, H. Chen, F. Fang, and W. Liao, "Convolution power spectrumanalysis for fMRI data based on prior image signal," IEEE Trans. Biomed.Eng., vol. 57, no. 2, pp. 343-352, Feb. 2010.
41. R. Guan, X. Shi, M. Marchese, C. Yang, and Y. Liang, "Text clusteringwith seeds affinity propagation," IEEE Trans. Knowl. Data Eng., vol. 23,no. 4, pp. 627-637, Apr. 2011.
42. L. Kaufman and P. Rousseeuw, Finding Groups in Data: An Introductionto Cluster Analysis. London, U.K: Wiley, 1990.
43. P. Berkhin, "A survey of clustering data mining techniques," in GroupingMultidimensional Data: Recent Advances in Clustering. Berlin, Germany:Springer, 2006, pp. 25-71.
44. K. J. Friston, P. Jezzard, and R. Turner, "Analysis of functional MRI timeseries," Hum. Brain Mapp., vol. 1, pp. 153-171, 1994.
45. K. J. Friston, A. P. Holmes, J. B. Poline, P. J. Grasby, S. C. Williams,R. S. Frackowiak, and R. Turner, "Analysis of fMRI time-series revisited,"NeuroImage, vol. 2, pp. 45-53, 1995.
46. M. R. Anderberg, Cluster Analysis for Applications in Hierarchical ClusteringMethods. New York: Academic, 1973, p. 131.