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Intra and inter subject analyses of brain functional Magnetic Resonance Images (fMRI)

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Handbook of Biomedical Imaging

Abstract

This chapter proposes a review of the most prominent issues in analysing brain functional Magnetic Resonance data. It introduces the domain for readers with no or little knowledge in the field. The introduction places the context and orients the reader in the many questions put to the data, and summarizes the currently most commonly applied approach. The second section deals with intra subject data analysis, emphasizing hemodynamic response estimation issues. The third section describes current approaches and advances in analysing group data in a standard coordinate system. The last section proposes new spatial models for group analyses. Overall, the chapter gives a brief overview of the field and details some specific advances that are important for application studies in cognitive neurosciences.

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References

  1. J. Fodor, “Let your brain alone.”, London review of books, 1999.

    Google Scholar 

  2. R. M.E., M. AM, S. AZ, P. WJ, G. DA, and S. GL., “Blood flow and oxygen delivery to human brain during functional activity: theoretical modeling and experimental data”, Proc Natl Acad Sci, vol. 98(12), pp. 6859–64, June 2001.

    Google Scholar 

  3. S. Ogawa, T. Lee, A. Kay, and D. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation”, Proc. Natl. Acad. Sci. USA, vol. 87, pp. 9868–9872, 1990.

    Article  Google Scholar 

  4. J. Talairach and P. Tournoux, Co–Planar Stereotaxic Atlas of the Human Brain. 3-Dimensional Proportional System: An Approach to Cerebral Imaging, Thieme Medical Publishers, Inc., Georg Thieme Verlag, Stuttgart, New York, 1988.

    Google Scholar 

  5. K. Worsley, C. Liao, J. Aston, V. Petre, G. Duncan, F. Morales, and A. Evans, “A general statistical analysis for fMRI data”, Neuroimage, vol. 15, pp. 1–15, Jan. 2002.

    Article  Google Scholar 

  6. J.-F. Mangin, D. Rivière, O. Coulon, C. Poupon, A. Cachia, Y. Cointepas, J.-B. Poline, D. L. Bihan, J. Régis, and D. Papadopoulos-Orfanos, “Coordinate-based versus structural approaches to brain image analysis”, Artificial Intelligence in Medicine, vol. 30, pp. 177–197, 2004.

    Article  Google Scholar 

  7. G. Yovel and N. Kanwisher, “Face perception: domain specific, not process specific.”, Neuron, vol. 44, pp. 747–8, Dec. 2004.

    Article  Google Scholar 

  8. G. H. Glover, “Deconvolution of impulse response in event-related BOLD fMRI”, Neuroimage, vol. 9, pp. 416–429, 1999.

    Article  Google Scholar 

  9. W. D. Penny, S. Kiebel, and K. J. Friston, “Variational Bayesian inference for fMRI time series”, Neuroimage, vol. 19, pp. 727–741, 2003.

    Article  Google Scholar 

  10. M. Woolrich, M. Jenkinson, J. Brady, and S. Smith, “Fully Bayesian spatio-temporal modelling of fMRI data”, IEEE Trans. Med. Imag., vol. 23, pp. 213–231, Feb. 2004.

    Article  Google Scholar 

  11. D. A. Handwerker, J. M. Ollinger,, and M. D’Esposito, “Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses”, Neuroimage, vol. 21, pp. 1639–1651, 2004.

    Article  Google Scholar 

  12. G. K. Aguirre, E. Zarahn, and M. D’Esposito, “The variability of human BOLD hemodynamic responses”, Neuroimage, vol. 7, pp. 574, 1998.

    Google Scholar 

  13. F. M. Miezin, L. Maccotta, J. M. Ollinger, S. E. Petersen, and R. L. Buckner, “Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing”, Neuroimage, vol. 11, pp. 735–759, 2000.

    Article  Google Scholar 

  14. J. Neumann and G. Lohmann, “Bayesian second-level analysis of functional magnetic resonance images”, Neuroimage, vol. 20, pp. 1346–1355, 2003.

    Article  Google Scholar 

  15. S. Makni, P. Ciuciu, J. Idier, and J.-B. Poline, “Joint detection-estimation of brain activity in functional MRI: a multichannel deconvolution solution”, IEEE Trans. Signal Processing, vol. 53, pp. 3488–3502, Sep. 2005.

    Article  MathSciNet  Google Scholar 

  16. T. Vincent, P. Ciuciu, and J. Idier, “Spatial mixture modelling for the joint detection-estimation of brain activity in fMRI”, in 32th Proc. IEEE ICASSP, Honolulu, Hawaii, Apr. 2007, vol. I, pp. 325–328.

    Google Scholar 

  17. B. Thirion, G. Flandin, P. Pinel, A. Roche, P. Ciuciu, and J.-B. Poline, “Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets”, Hum. Brain Mapp., vol. 27, pp. 678–693, Aug. 2006.

    Article  Google Scholar 

  18. B. Thyreau, B. Thirion, G. Flandin, and J.-B. Poline, “Anatomo-functional description of the brain: a probabilistic approach”, in Proc. 31th Proc. IEEE ICASSP, Toulouse, France, May 2006, vol. V, pp. 1109–1112.

    Google Scholar 

  19. P. Good, Permutation, Parametric, and Bootstrap Tests of Hypotheses, Springer, 3rd edition edition, 2005.

    Google Scholar 

  20. S. Mériaux, A. Roche, B. Thirion, and G. Dehaene-Lambertz, “Robust statistics for nonparametric group analysis in fMRI”, in Proc. 3th Proc. IEEE ISBI, Arlington, VA, Apr. 2006, pp. 936–939.

    Google Scholar 

  21. J. Ashburner, “A fast diffeomorphic image registration algorithm.”, Neuroimage, vol. 38, pp. 95–113, Oct 2007.

    Article  Google Scholar 

  22. B. Fischl, N. Rajendran, E. Busa, J. Augustinack, O. Hinds, B. T. T. Yeo, H. Mohlberg, K. Amunts, and K. Zilles, “Cortical folding patterns and predicting cytoarchitecture.”, Cereb Cortex, Dec 2007.

    Google Scholar 

  23. D. Rivière, J.-F. Mangin, D. Papadopoulos-Orfanos, J.-M. Martinez, V. Frouin, and J. Régis, “Automatic recognition of cortical sulci of the human brain using a congregation of neural networks”, Medical Image Analysis, vol. 6, pp. 77–92, 2002.

    Article  Google Scholar 

  24. B. Fischl, M. I. Sereno, R. B. Tootell, and A. M. Dale, “High-resolution intersubject averaging and a coordinate system for the cortical surface.”, Hum Brain Mapp, vol. 8, pp. 272–284, 1999.

    Article  Google Scholar 

  25. M. Brett, I. Johnsrude, and A. Owen, “The problem of functional localization in the human brain.”, Nature Reviews Neuroscience, vol. 3, pp. 243–249, Mar. 2002.

    Article  Google Scholar 

  26. R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, B. T. Hyman, M. S. Albert, and R. J. Killiany, “An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest.”, Neuroimage, vol. 31, pp. 968–980, July 2006.

    Article  Google Scholar 

  27. G. Flandin, F. Kherif, X. Pennec, G. Malandain, N. Ayache, and J.-B. Poline, “Improved detection sensitivity of functional MRI data using a brain parcellation technique”, in Proc. 5th MICCAI, Tokyo, Japan, Sep. 2002, LNCS 2488 (Part I), pp. 467–474, Springer Verlag.

    Google Scholar 

  28. O. Coulon, J.-F. Mangin, J.-B. Poline, M. Zilbovicius, D. Roumenov, Y. Samson, V. Frouin, and I. Bloch, “Structural group analysis of functional activation maps”, Neuroimage, vol. 11, pp. 767–782, 2000.

    Article  Google Scholar 

  29. B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.-F. Mangin, and J.-B. Poline, “Structural analysis of fMRI data revisited: Improving the sensitivity and reliability of fMRI group studies”, IEEE Trans. Med. Imag., vol. 26, pp. 1256–1269, Sep. 2007.

    Article  Google Scholar 

  30. B. Thirion, P. Pinel, and J.-B. Poline, “Finding landmarks in the functional brain: detection and use for group characterization.”, Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv, vol. 8, pp. 476–483, 2005.

    Google Scholar 

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Poline, J.B., Ciuciu, P., Roche, A., Thirion, B. (2015). Intra and inter subject analyses of brain functional Magnetic Resonance Images (fMRI). In: Paragios, N., Duncan, J., Ayache, N. (eds) Handbook of Biomedical Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09749-7_23

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  • DOI: https://doi.org/10.1007/978-0-387-09749-7_23

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09748-0

  • Online ISBN: 978-0-387-09749-7

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