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Local dimension-reduced dynamical spatio-temporal models for resting state network estimation (2015)

  • Authors:
  • USP affiliated authors: BACCALA, LUÍZ ANTONIO - EP
  • USP Schools: EP
  • DOI: 10.1007/s40708-015-0011-5
  • Subjects: PROCESSAMENTO DE SINAIS; NEUROCIÊNCIAS
  • Language: Inglês
  • Imprenta:
  • Source:
    • Título do periódico: Brain Informatics
    • Volume/Número/Paginação/Ano: v. 2, n. 2, p 53–63, June 2015
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    Informações sobre o DOI: 10.1007/s40708-015-0011-5 (Fonte: oaDOI API)
    • Este periódico é de acesso aberto
    • Este artigo é de acesso aberto
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    • Cor do Acesso Aberto: gold
    • Licença: cc-by

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    • ABNT

      VIEIRA, Gilson; AMARO, Edson; BACCALÁ, Luiz Antonio. Local dimension-reduced dynamical spatio-temporal models for resting state network estimation. Brain Informatics[S.l.], Springer, v. 2, n. 2, 2015. Disponível em: < ttps://doi.org/10.1007/s40708-015-0011-5 > DOI: 10.1007/s40708-015-0011-5.
    • APA

      Vieira, G., Amaro, E., & Baccalá, L. A. (2015). Local dimension-reduced dynamical spatio-temporal models for resting state network estimation. Brain Informatics, 2( 2). doi:10.1007/s40708-015-0011-5
    • NLM

      Vieira G, Amaro E, Baccalá LA. Local dimension-reduced dynamical spatio-temporal models for resting state network estimation [Internet]. Brain Informatics. 2015 ; 2( 2):Available from: ttps://doi.org/10.1007/s40708-015-0011-5
    • Vancouver

      Vieira G, Amaro E, Baccalá LA. Local dimension-reduced dynamical spatio-temporal models for resting state network estimation [Internet]. Brain Informatics. 2015 ; 2( 2):Available from: ttps://doi.org/10.1007/s40708-015-0011-5

    Referências citadas na obra
    Baccala LA, de Brito CSN, Takahashi DY, Sameshima K (2013) Unified asymptotic theory for all partial directed coherence forms. Philos Trans R Soc A 371(1997):20120158
    Bach F, Jenatton R, Mairal J, Obozinski G (2011) Structured sparsity through convex optimization. arXiv e-print arXiv:1109.2397
    Beckmann CF, Smith SM (2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 23(2):137–152
    Biswal BB, Mennes M, Zuo X-N, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski A-M, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Ktter R, Li S-J, Lin C-P, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, McMahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SARB, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng G-J, Veijola J, Villringer A, Walter M, Wang L, Weng X-C, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang Y-F, Zhang H-Y, Castellanos FX, Milham MP (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107(10):4734–4739
    Blumensath T, Jbabdi S, Glasser MF, Van Essen DC, Ugurbil K, Behrens TEJ, Smith SM (2013) Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. NeuroImage 76:313–324
    Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20(1):33–61
    Combettes PL, Wajs VR (2005) Signal recovery by proximal forward-backward splitting. Multiscale Model Simul 4(4):1168–1200
    Cortes J (2009) Distributed kriged kalman filter for spatial estimation. IEEE Trans Autom Control 54(12):2816–2827
    Cressie N, Wikle CK (2011) Statistics for spatio-temporal data. Wiley, Hoboken
    Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci 103(37):13848–13853
    Daubechies I, Roussos E, Takerkart S, Benharrosh M, Golden C, D’Ardenne K, Richter W, Cohen JD, Haxby J (2009) Independent component analysis for brain fMRI does not select for independence. Proc Natl Acad Sci USA 106(26):10415–10422
    Daubechies I, Defrise M, De Mol C (2003) An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. arXiv e-print math/0307152
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(1):1–38
    Dewar M, Scerri K, Kadirkamanathan V (2009) Data-driven spatio-temporal modeling using the integro-difference equation. IEEE Trans Signal Process 57(1):83–91
    Donoho DL, Johnstone IM, Kerkyacharian G, Picard D (1995) Wavelet shrinkage: asymptopia? J R Stat Soc Ser B (Methodol) 57(2):301–369 ArticleType: research-article/Full publication date: 1995/Copyright 1995 Royal Statistical Society
    Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S, Glasser MF, Miller KL, Ugurbil K, Yacoub E (2010) Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One 5(12):e15710
    Figueiredo MAT, Nowak RD (2003) An EM algorithm for wavelet-based image restoration. IEEE Trans Image Process 12(8):906–916
    Friston KJ, Frith CD, Liddle PF, Frackowiak RSJ (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13(1):5–14
    Georgiev P, Theis F, Cichocki A, Bakardjian H (2007) Sparse component analysis: a new tool for data mining. In: Pardalos PM, Boginski VL, Vazacopoulos A (eds) Data mining in biomedicine, number 7 in Springer optimization and its applications. Springer, New York, pp 91–116
    Kellermann T, Regenbogen C, De Vos M, Mnang C, Finkelmeyer A, Habel U (2012) Effective connectivity of the human cerebellum during visual attention. J Neurosci 32(33):11453–11460
    Lohmann G, Volz KG, Ullsperger M (2007) Using non-negative matrix factorization for single-trial analysis of fMRI data. Neuroimage 37(4):1148–1160
    Macher K, Bhringer A, Villringer A, Pleger B (2014) Cerebellar-parietal connections underpin phonological storage. J Neurosci 34(14):5029–5037
    Mallat SG (2009) A wavelet tour of signal processing the sparse way. Elsevier/Academic Press, Amsterdam; Boston
    Mardia KV, Goodall C, Redfern EJ, Alonso FJ (1998) The kriged kalman filter. Test 7(2):217–282
    Rauch HE, Striebel CT, Tung F (1965) Maximum likelihood estimates of linear dynamic systems. J Am Inst Aeronaut Astronaut 3(8):1445–1450
    Scerri K, Dewar M, Kadirkamanathan V (2009) Estimation and model selection for an IDE-based spatio-temporal model. IEEE Trans Signal Process 57(2):482–492
    Shumway RH, Stoffer DS (1982) An approach to time series smoothing and forecasting using the em algorithm. J Time Ser Anal 3(4):253–264
    Stoodley CJ, Schmahmann JD (2009) Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 44(2):489–501
    Theophilides CN, Ahearn SC, Grady S, Merlino M (2003) Identifying west nile virus risk areas: the dynamic continuous-area space-time system. Am J Epidemiol 157(9):843–854
    Tibshirani R (1994) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267–288
    Vieira G, Amaro E, Baccala LA (2014) Local dimension-reduced dynamical spatio-temporal models for resting state network estimation. In: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Kobsa A, Mattern F, Mitchell JC, Naor M, Nierstrasz O, Pandu Rangan C, Steffen B, Terzopoulos D, Tygar D, Weikum G, lezak D, Tan A-H, Peters JF, Schwabe L (eds) Brain informatics and health. Springer International Publishing, Cham, pp 436–446
    Vieira G, Amaro E, Baccala LA (2014) Local sparse component analysis for blind source separation: an application to resting state fmri. In: Proceedings of IEEE EMBS conference, IEEE
    Wikle CK, Cressie N (1999) A dimension-reduced approach to space-time kalman filtering. Biometrika 86(4):815–829
    Woolrich MW, Jenkinson M, Michael Brady J, Smith SM (2004) Fully bayesian spatio-temporal modeling of FMRI data. IEEE Trans Med Imaging 23(2):213–231
    Wright SJ, Nowak RD, Figueiredo MAT (2009) Sparse reconstruction by separable approximation. IEEE Trans Signal Process 57(7):2479–2493
    Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc 68(1):49–67
    Zalesky A, Fornito A, Harding IH, Cocchi L, Ycel M, Pantelis C, Bullmore ET (2010) Whole-brain anatomical networks: Does the choice of nodes matter? NeuroImage 50(3):970–983
    Zhao P, Rocha G (2009) The composite absolute penalties family for grouped and hierarchical variable selection. Ann Stat 37(6A):3468–3497 arXiv e-print arXiv:0909.0411
    Zibulevsky M, Pearlmutter BA (2001) Blind source separation by sparse decomposition in a signal dictionary. Neural Comput 13(4):863–882