Joint sparse representation of brain activity patterns in multi-task fMRI data

IEEE Transactions on Medical Imaging

Volumn 34, Issue 1, 2015, Pages 2-12

  Ramezani, M ^a   Marble, K ^b   Trang, H ^b   Johnsrude, I S ^b   Abolmaesumi, P ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b QUEEN S UNIVERSITY   (Canada)

Author keywords

Brain activations; functional magnetic resonance imaging (fMRI); sparsity; Speech perception

Indexed keywords

ACTIVATION ANALYSIS; BRAIN; CHEMICAL ACTIVATION; INDEPENDENT COMPONENT ANALYSIS; JOB ANALYSIS; MAGNETIC RESONANCE IMAGING; MULTIVARIANT ANALYSIS; NEUROPHYSIOLOGY;

BRAIN ACTIVATION; BRAIN ACTIVITY PATTERNS; FUNCTIONAL MAGNETIC RESONANCE IMAGING; INDIVIDUAL DIFFERENCES; MULTI VARIATE ANALYSIS; SPARSE REPRESENTATION; SPARSITY; SPEECH PERCEPTION;

FUNCTIONAL NEUROIMAGING;

ADULT; ARTICLE; COMPREHENSION; CONTRAST TO NOISE RATIO; ELECTROENCEPHALOGRAM; EXECUTIVE FUNCTION; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; HEMODYNAMICS; HUMAN; IMAGE SUBTRACTION; INDEPENDENT COMPONENT ANALYSIS; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL ANALYSIS; SIMULATION; SPEECH PERCEPTION; AGED; ANATOMY AND HISTOLOGY; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; FACTUAL DATABASE; IMAGE PROCESSING; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; TASK PERFORMANCE; YOUNG ADULT;

ADULT; AGED; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; DATABASES, FACTUAL; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; SPEECH PERCEPTION; TASK PERFORMANCE AND ANALYSIS; YOUNG ADULT;

EID: 84920163945     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2014.2340816     Document Type: Article

References (62)

1. N. K. Logothetis, "What we can do and what we cannot do with fMRI," Nature, vol. 453, pp. 869-878, 2008.
2. V. D. Calhoun, T. Adali, K. A. Kiehl, R. Astur, J. J. Pekar, and G. D. Pearlson, "A method for multitask fMRI data fusion applied to schizophrenia," Hum. Brain Mapp., vol. 27, pp. 598-610, 2006, 07.
3. J. Sui, T. Adali, Y. Li, H. Yang, and V. D. Calhoun, "A review of multivariatemethods in brain imaging data fusion," in Proc. SPIEMed. Imag., 2010, pp. 1-11.
4. B. Mijovi?, K. Vanderperren, N. Novitskiy, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, J.Wagemans, and S. Van Huffel, "The "why" and "how" of JointICA: Results from a visual detection task," Neuroimage, vol. 60, pp. 1171-1185, 2012.
5. Z. Long, K. Chen, X.Wu, E. Reiman, D. Peng, and L. Yao, "Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques," Hum. Brain Mapp., vol. 30, pp. 417-431, 2009.
6. Z. Long, R. Li, M. Hui, Z. Jin, and L. Yao, "An improvement of independent component analysis with projection method applied to multitask fMRI data," Comput. Biol. Med., vol. 43, pp. 200-210, 2013.
7. J. Sui, T. Adali,G.Pearlson, H. Yang,S.R.Sponheim, T.White, andV. D. Calhoun, "A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia," Neuroimage, vol. 51, pp. 123-134, 2010.
8. V. D. Calhoun and T. Adali, "Feature-based fusion of medical imaging data," IEEE Trans. Inf. Tech. Biomed., vol. 13, no. 5, pp. 711-720, Sep. 2009.
9. K. Choi, Z. Yang, X. Hu, and H. Mayberg, "A combined functionalstructural connectivity analysis of major depression using joint independent components analysis," in PsychiatricMRI/MRS, Toronto, ON, Canada, May 2008, p. 3555.
10. K. Specht, R. Zahn, K. Willmes, S. Weis, C. Holtel, B. J. Krause, H. Herzog, and W. Huber, "Joint independent component analysis of structural and functional images reveals complex patterns of functional reorganisation in stroke aphasia," Neuroimage, vol. 47, pp. 2057-2063, 2009.
11. V. D. Calhoun, T. Adali, N. Giuliani, J. Pekar, K. Kiehl, and G. Pearlson, "Method for multimodal analysis of independent source differences in schizophrenia: Combining gray matter structural and auditory oddball functional data," Hum. Brain Mapp., vol. 27, pp. 47-62, 2006.
12. J.Liu,G.Pearlson, A.Windemuth,G. Ruano,N. I. P. Bizzozero, andV. Calhoun, "Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA," Hum. Brain Mapp., vol. 30, pp. 241-255, 2009.
13. J. Sui, T. Adali, G. D. Pearlson, V. P. Clark, and V. D. Calhoun, "A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework," Hum. Brain Mapp., vol. 30, pp. 2953-2970, 2009.
14. I. Daubechies, E. Roussos, S. Takerkart, M. Benharrosh, C. Golden, K. D'Ardenne, W. Richter, J. D. Cohen, and J. Haxby, "Independent component analysis for brain fMRI does not select for independence," Proc. Nat. Acad. Sci., vol. 106, p. 10415, 2009.
15. M. J. McKeown and T. J. Sejnowski, "Independent component analysis of fMRI data: Examining the assumptions," Hum. Brain Mapp., vol. 6, pp. 368-372, 1998.
16. G. Varoquaux, M. Keller, J. Poline, P. Ciuciu, and B. Thirion, "ICAbased sparse features recovery from fMRI datasets," in Proc. IEEE Int. Symp. Biomed. Imag., 2010, pp. 1177-1180.
17. S. Ma, X. L. Li, N. M. Correa, T. Adali, and V. D. Calhoun, "Independent subspace analysis with prior information for fMRI data," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., 2010, pp. 1922-1925.
18. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, Nov. 2006.
19. Q. Barthélemy, C. Gouy-Pailler, Y. Isaac, A. Souloumiac, A. Larue, and J. I. Mars, "Multivariate temporal dictionary learning for EEG," J. Neurosci. Methods, vol. 215, no. 1, pp. 19-28, 2013.
20. Y. Shin, S. Lee, J. Lee, and H. Lee, "Sparse representation-based classification scheme for motor imagery-based brain-Computer interface systems," J. Neural Eng., vol. 9, p. 056002, 2012.
21. B. Hamner, R. Chavarriaga, and J. d. R. Millán, "Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces," in Workshop Structured Sparsity: Learn. Inference, ICML 2011, 2011.
22. B. Gaonkar, K. Pohl, and C. Davatzikos, "Pattern based morphometry," in Medical Image Computing and Computer-Assisted Intervention-( MICCAI). New York: Springer, 2011, pp. 459-466.
23. L. Su, L.Wang, F. Chen, H. Shen, B. Li, and D. Hu, "Sparse representation of brain aging: Extracting covariance patterns from structural MRI," PloS One, vol. 7, p. e36147, 2012.
24. Y. Li, P. Namburi, Z. Yu, C. Guan, J. Feng, and Z. Gu, "Voxel selection in fMRI data analysis based on sparse representation," IEEE Trans. Biomed. Eng., vol. 56, no. 10, pp. 2439-2451, Oct. 2009.
25. Y. Li, J. Long, L. He, H. Lu, Z. Gu, and P. Sun, "A sparse representation- based algorithm for pattern localization in brain imaging data analysis," PloS One, vol. 7, p. e50332, 2012.
26. M. Ramezani, P. Abolmaesumi, K. Marble, H. MacDonald, and I. Johnsrude, "Classification of individuals based on sparse representation of brain cognitive patterns: A functional MRI study," in Proc. 34th IEEE Int. Eng. Med. Biol. Conf., San Diego, CA, 2012, pp. 2688-2691.
27. E. Roussos, S. Roberts, and I. Daubechies, "Variational bayesian learning of sparse representations and its application in functional neuroimaging," in Machine Learning and Interpretation in Neuroimaging. New York: Springer, 2012, pp. 218-225.
28. K. Lee, S. Tak, and J. C. Ye, "A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion," IEEE Trans. Med. Imag., vol. 30, no. 5, pp. 1076-1089-1, May 2011.
29. H. Eavani, R. Filipovych, C. Davatzikos, T. D. Satterthwaite, R. E. Gur, and R. C. Gur, "Sparse dictionary learning of resting state fMRI networks," in Pattern Recognit. Neuroimag., 2012, pp. 73-76.
30. V. Abolghasemi, S. Ferdowsi, and S. Sanei, "Fast and incoherent dictionary learning algorithms with application to fMRI," Signal, Image and Video Processing, pp. 1-12, 2013.
31. Y. Kim, J. Kim, and J. Lee, "Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data," Neuroimage, 2012.
32. N. Wang, W. Zeng, and L. Chen, "SACICA: A sparse approximation coefficients based ICA model for functional magnetic resonance imaging data analysis," J. Neurosci. Methods, vol. 216, no. 1, pp. 49-61, 2013.
33. M. Ramezani, P. Abolmaesumi, K. Marble, H. MacDonald, and I. Johnsrude, "Joint sparse representation of brain activity patterns related to perceptual and cognitive components of a speech comprehension task," in Pattern Recognit. Neuroimag., 2012, pp. 29-32.
34. M.H.Davis, M. A. Ford, F.Kherif, and I. S. Johnsrude, "Does semantic context benefit speech understanding through ?top-down' processes? Evidence from time-resolved sparse fMRI," J. Cogn. Neurosci., vol. 23, no. 12, pp. 1-3932, 2011.
35. H. MacDonald, "Behavioural and neuroimaging studies of the influence of semantic context on the perception of speech in noise," M.S. thesis, Queen's Univ., Kingston, ON, Canada, 2008.
36. M. Mesulam, "From sensation to cognition," Brain, vol. 121, pp. 1013-1052, 1998.
37. I. Tosic and P. Frossard, "Dictionary learning," IEEE Signal Process. Mag., vol. 28, pp. 27-38, 2011.
38. Y. O. Li, T. Adali, and V. D. Calhoun, "Estimating the number of independent components for functional magnetic resonance imaging data," Hum. Brain Mapp., vol. 28, pp. 1251-1266, 2007.
39. Y. C. Pati, R. Rezaiifar, and P. Krishnaprasad, "Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition," Signals, Syst. Comput., vol. 1, pp. 40-44, 1993.
40. M. W. Cole and W. Schneider, "The cognitive control network: Integrated cortical regions with dissociable functions," Neuroimage, vol. 37, pp. 343-360, 2007.
41. E.M. Berntsen, I. Rasmussen, P. Samuelsen, J. Xu, O. Haraldseth, J. Lagopoulos, and G. S. Malhi, "Putting the brain in jeopardy: A novel comprehensive and expressive language task?," Acta Neuropsychiatrica, vol. 18, pp. 115-119, 2006.
42. J. L. Vincent, A. Z. Snyder, M. D. Fox, B. J. Shannon, J. R. Andrews, M. E. Raichle, and R. L. Buckner, "Coherent spontaneous activity identifies a hippocampal-parietal memory network," J. Neurophysiol., vol. 96, pp. 3517-3531, 2006.
43. B. Wandell, S. Dumoulin, and A. Brewer, "Visual areas in humans," Encyclopedia Neurosci., vol. 10, pp. 251-257, 2009.
44. E. Formisano, D. Kim, F. Di Salle, P. van de Moortele, K. Ugurbil, and R. Goebel, "Mirror-symmetric tonotopic maps in human primary auditory cortex," Neuron, vol. 40, pp. 859-869, 2003.
45. E. B. Erhardt, E. A. Allen, Y. Wei, T. Eichele, and V. D. Calhoun, "SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability," Neuroimage, vol. 59, pp. 4160-4167, 2012.
46. K. J. Friston, A. P. Holmes, K. J. Worsley, J. Poline, C. D. Frith, and R. S. Frackowiak, "Statistical parametric maps in functional imaging: A general linear approach," Hum. Brain Mapp., vol. 2, pp. 189-210, 1994.
47. H. Gudbjartsson and S. Patz, "The Rician distribution of noisy MRI data," Magn. Reson. Med., vol. 34, pp. 910-914, 1995.
48. D. A. Hall, M. P. Haggard, M. A. Akeroyd, A. R. Palmer, A. Q. Summerfield, M. R. Elliott, E. M. Gurney, and R. W. Bowtell, "Sparse temporal sampling in auditory fMRI," Hum. Brain Mapp., vol. 7, pp. 213-223, 1999.
49. T. M. Talavage, W. B. Edmister, P. J. Ledden, and R. M. Weisskoff, "Quantitative assessment of auditory cortex responses induced by imager acoustic noise," Hum. Brain Mapp., vol. 7, pp. 79-88, 1999.
50. M. R. Schroeder, "Reference signal for signal quality studies," J. Acoust. Soc. Am., vol. 44, pp. 1735-1736, 1968.
51. H. MacDonald, M. H. Davis, K. Pichora-Fuller, and I. S. Johnsrude, "Contextual influences: Perception of sentences in noise is facilitated similarly in young and older listeners by meaningful semantic context; neural correlates explored via functional magnetic resonance imaging (fMRI)," J. Acoust. Soc. Am, vol. 123, pp. 3887-3887, 2008.
52. K. Friston, J. Ashburner, C. Frith, J. Poline, J. Heather, and R. S. Frackowiak, "Spatial registration and normalization of images," Hum. Brain Mapp., vol. 3, pp. 165-189, 1995.
53. A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, and G. Marchal, "Automated multi-modality image registration based on information theory," in Inf. Process. Med. Imag., 1995, pp. 263-274.
54. J. Ashburner and K. J. Friston, "Unified segmentation," Neuroimage, vol. 26, pp. 839-851, 2005.
55. A. M. Tahmasebi, M. H. Davis, C. J. Wild, J. M. Rodd, H. Hakyemez, P. Abolmaesumi, and I. S. Johnsrude, "Is the link between anatomical structure and function equally strong at all cognitive levels of processing?," Cerebral Cortex, 2011.
56. M. H. Davis and I. S. Johnsrude, "Hierarchical processing in spoken language comprehension," J. Neurosci., vol. 23, p. 3423, 2003.
57. J.M. Rodd,M. H. Davis, and I. S. Johnsrude, "The neural mechanisms of speech comprehension: fMRI studies of semantic ambiguity," Cerebral Cortex, vol. 15, p. 1261, 2005.
58. J. E. Peelle, I. S. Johnsrude, and M. H. Davis, "Hierarchical processing for speech in human auditory cortex and beyond," Front. Human Neurosci., vol. 4, 2010.
59. K. Okada, F. Rong, J. Venezia, W. Matchin, I. Hsieh, K. Saberi, J. T. Serences, and G. Hickok, "Hierarchical organization of human auditory cortex: Evidence from acoustic invariance in the response to intelligible speech," Cerebral Cortex, vol. 20, pp. 2486-2495, 2010.
60. M. Ramezani, P. Abolmaesumi, K. Marble, H. MacDonald, and I. Johnsrude, "Fusion analysis of functional MRI data for classification of individuals based on patterns of activation," Brain Imag. Behav., 2014.
61. R. Rubinstein, M. Zibulevsky, and M. Elad, "Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit," CS Technion, p. 40, 2008.
62. V. D. Calhoun, V. K. Potluru, R. Phlypo, R. F. Silva, B. A. Pearlmutter, A. Caprihan, S. M. Plis, and T. Adal?, "Independent component analysis for brain fMRI does indeed select for maximal independence," PloS One, vol. 8, p. e73309, 2013.