Ver registro no DEDALUS
Exportar registro bibliográfico

Metrics


Metrics:

DWT-CEM: an algorithm for scale-temporal clustering in fMRI (2007)

  • Authors:
  • USP affiliated authors: AMARO JÚNIOR, EDSON - FM
  • USP Schools: FM
  • DOI: 10.1007/s00422-007-0154-4
  • Subjects: ESTATÍSTICA (TENDÊNCIAS); ALGORITMOS E ESTRUTURAS DE DADOS; IMAGEM POR RESSONÂNCIA MAGNÉTICA
  • Language: Inglês
  • Imprenta:
  • Source:
  • Acesso online ao documento

    DOI or search this record in
    Informações sobre o DOI: 10.1007/s00422-007-0154-4 (Fonte: oaDOI API)
    • Este periódico é de assinatura
    • Este artigo NÃO é de acesso aberto
    • Cor do Acesso Aberto: closed
    Informações sobre o Citescore
  • Título: Biological Cybernetics

    ISSN: 0340-1200

    Citescore - 2017: 1.93

    SJR - 2017: 0.667

    SNIP - 2017: 1.093


  • Exemplares físicos disponíveis nas Bibliotecas da USP
    BibliotecaCód. de barrasNúm. de chamada
    FM2484080-10BCSEP 341 2007
    How to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      SATO, João Ricardo; MORETTIN, Pedro Alberto; BRAMMER, Michal John; et al. DWT-CEM: an algorithm for scale-temporal clustering in fMRI. Biological cybernetics, Berlin, v. 97, n. 1, p. 33-45, 2007. DOI: 10.1007/s00422-007-0154-4.
    • APA

      Sato, J. R., Morettin, P. A., Brammer, M. J., Fujita, A., Amaro Junior, E., & Miranda, J. M. (2007). DWT-CEM: an algorithm for scale-temporal clustering in fMRI. Biological cybernetics, 97( 1), 33-45. doi:10.1007/s00422-007-0154-4
    • NLM

      Sato JR, Morettin PA, Brammer MJ, Fujita A, Amaro Junior E, Miranda JM. DWT-CEM: an algorithm for scale-temporal clustering in fMRI. Biological cybernetics. 2007 ; 97( 1): 33-45.
    • Vancouver

      Sato JR, Morettin PA, Brammer MJ, Fujita A, Amaro Junior E, Miranda JM. DWT-CEM: an algorithm for scale-temporal clustering in fMRI. Biological cybernetics. 2007 ; 97( 1): 33-45.

    Referências citadas na obra
    Aston JA, Gunn RN, Hinz R, Turkheimer FE (2005) Wavelet variance components in image space for spatiotemporal neuroimaging data. Neuroimage 25(1):159–168
    Baumgartner R, Ryner L, Richter W, Summers R, Jarmsaz M, Somorjai R (2000) Comparision of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. Magn Reson Imaging 18:89–94
    Biswal B, Yetking FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541
    Biswal B, Ulmer JL (1999) Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. J Comput Assist Tomogr 23(2):265–271
    Brammer MJ, Bullmore ET, Simmons A, Williams SC, Grasby PM, Howard RJ, Woodruff PW, Rabe-Hesketh S (1997) Generic brain activation mapping in functional magnetic resonance imaging: a nonparametric approach. Magn Reson Imaging 15(7): 763–770
    Bullmore E, Long C, Suckling J, Fadili J, Calvert G, Zelaya F, Carpenter TA, Brammer M (2001) Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains. Hum Brain Mapp 12(2):61–78
    Bullmore E, Fadili J, Breakspear M, Salvador R, Suckling J, Brammer M (2003) Wavelets and statistical analysis of functional magnetic resonance images of the human brain. Stat Methods Med Res 12(5):375–399
    Bullmore E, Fadili J, Maxim V, Sendur L, Whitcher B, Suckling J, Brammer M, Breakspear M (2004) Wavelets and functional magnetic resonance imaging of the human brain. Neuroimage 23(Suppl 1):S234–S249
    Celeux G, Govaert G (1992) A classication EM algorithm for clustering and two stochastic versions. Comput Statist Data Anal 14(3): 315–332
    Celeux G, Govaert G (1995) Gaussian parsimonious clustering models. Pattern Recognition 28:781–793
    Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA 103(37): 13848–13853
    Daubechies I (1992) Ten lectures on wavelets. SIAM, Philadelphia PA
    De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM (2006) fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage 29(4): 1359–1367
    Dimitriadou E, Barth M, Windischberger C, Hornik K, Moser E (2004) A quantitative comparison of functional MRI cluster analysis. Artif Intell Med 31(1):57–71
    Fadili MJ, Bullmore ET (2003) Wavelet-generalized least squares: a new BLU estimator of linear regression models with 1/f errors. Neuroimage 15(1):217–232
    Fadili MJ, Ruan S, Bloyet D, Mazoyer B (2001) On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series. Med Image Anal 5(1):55–67
    Gao JH, Yee SH (2003) Iterative temporal clustering analysis for the detection of multiple response peaks in fMRI. Magn Reson Imaging 21(1):51–53
    Hannan EJ, Quinn BG (1979) The determination of the order of an autoregression. J Roy Statis Soc Ser B 41:190–195
    Harris KD, Henze DA, Csicsvari J, Hirase H, Buzsaki G (2000) Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J Neurophysiol 84(1):401–414
    Haughton D (1988) On the choice of model to fit data from an exponential family. Ann Statist 16:342–355
    Hosking JRM (1981) Fractional differencing. Biometrika 68:165–176
    Jahanian H, Hossein-Zadeh GA, Soltanian-Zadeh H, Ardekani BA (2004) Controlling the false positive rate in fuzzy clustering using randominzation: application to fMRI activation detection. Magn Reson Imaging 22:631–638
    Jia Z, Xu S (2005) Clustering expressed genes on the basis of their association with a quantitative phenotype. Genet Res 86(3):193–207
    Kohonen T (1995) Self-organizing maps. Springer, Berlin
    Long C, Brown EN, Manoach D, Solo V (2004) Spatiotemporal wavelet analysis for functional MRI. Neuroimage 23(2):500–516
    McQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proc Fifth Berkeley Symposium on Math Stat and Prob, vol 1, pp 281–296
    Mourao-Miranda J, Bokde AL, Born C, Hampel H, Stetter M (2005) Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. Neuroimage 28(4):980–995
    Ogawa S, Lee TM et al. (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 87(24):9868–9872
    Pan W (2007) Incorporating gene functions as priors in model-based clustering of microarray gene expression data. Bioinformatics (in press)
    Shimizu Y, Barth M, Windischberger C, Moser E, Thurner S (2004) Wavelet-based multifractal analysis of fMRI time series. Neuroimage 22(3):1195–1202
    Schwarz G (1978) Estimating the dimension of a model. Ann Statist 6:461–464
    Strainer JC, Ulmer JL, Yetkin FZ, Haughton VM, Daniels DL, Millen SJ (1997) Functional MR of the primary auditory cortex: an analysis of pure tone activation and tone discrimination. AJNR Am J Neuroradiol 18(4):601–610
    Tjaden B (2006) An approach for clustering gene expression data with error information. BMC Bioinformatics 7:17
    Van De Ville D, Blu T, Unser M (2004) Integrated wavelet processing and spatial statistical testing of fMRI data. Neuroimage 23(4):1472–1485
    Vidakovic B (1999) Statistical modeling by wavelets. Wiley Series in Probability and Statistics. ISBN: 0471293652
    Yee SH, Gao JH (2002) Improved detection of time windows of brain responses in fMRI using modified temporal clustering analysis. Magn Reson Imaging 20(1):17–26
    Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL (2001) Model-based clustering and data transformations for gene expression data. Bioinformatics 17(10):977–987