International Journal of Biomedical Imaging
Volume 2008 (2008), Article ID 423192, 9 pages
doi:10.1155/2008/423192
Research Article

Exploring the Anatomical Basis of Effective Connectivity Models with DTI-Based Fiber Tractography

1Helmholtz Institute, Universiteit Utrecht, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands
2F.C. Donders Centre for Cognitive Neuroimaging, Radboud University Nijmegen, P.O. Box 9102, 6500 HC Nijmegen, The Netherlands

Received 31 August 2007; Revised 26 November 2007; Accepted 17 December 2007

Academic Editor: Habib Benali

Copyright © 2008 Hubert M. J. Fonteijn et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. J. Friston, A. P. Holmes, K. J. Worsley, J.-P. Poline, C. D. Frith, and R. S. J. Frackowiak, “Statistical parametric maps in functional imaging: a general linear approach,” Human Brain Mapping, vol. 2, no. 4, pp. 189–210, 1995. View at Publisher · View at Google Scholar
  2. B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magnetic Resonance in Medicine, vol. 34, no. 4, pp. 537–541, 1995. View at Publisher · View at Google Scholar
  3. K. J. Friston, C. D. Frith, P. F. Liddle, and R. S. J. Frackowiak, “Functional connectivity: the principal-component analysis of large (PET) data sets,” Journal of Cerebral Blood Flow & Metabolism, vol. 13, no. 1, pp. 5–14, 1993.
  4. M. D. Greicius, B. Krasnow, A. L. Reiss, and V. Menon, “Functional connectivity in the resting brain: a network analysis of the default mode hypothesis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 1, pp. 253–258, 2003. View at Publisher · View at Google Scholar · View at PubMed
  5. K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” NeuroImage, vol. 19, no. 4, pp. 1273–1302, 2003. View at Publisher · View at Google Scholar
  6. A. R. McIntosh, C. L. Grady, L. G. Ungerleider, J. V. Haxby, S. I. Rapoport, and B. Horwitz, “Network analysis of cortical visual pathways mapped with PET,” Journal of Neuroscience, vol. 14, no. 2, pp. 655–666, 1994.
  7. K. J. Friston, “Functional and effective connectivity in neuroimaging: a synthesis,” Human Brain Mapping, vol. 2, no. 1-2, pp. 56–78, 1994. View at Publisher · View at Google Scholar
  8. A. R. McIntosh and F. Gonzalez-Lima, “Structural equation modeling and its application to network analysis in functional brain imaging,” Human Brain Mapping, vol. 2, no. 1-2, pp. 2–22, 1994. View at Publisher · View at Google Scholar
  9. P. J. Basser, J. Mattiello, and D. LeBihan, “MR diffusion tensor spectroscopy and imaging,” Biophysical Journal, vol. 66, no. 1, pp. 259–267, 1994.
  10. P. J. Basser and C. Pierpaoli, “Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI,” Journal of Magnetic Resonance, Series B, vol. 111, no. 3, pp. 209–219, 1996. View at Publisher · View at Google Scholar
  11. T. E. Conturo, N. F. Lori, T. S. Cull, et al., “Tracking neuronal fiber pathways in the living human brain,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 18, pp. 10422–10427, 1999. View at Publisher · View at Google Scholar
  12. S. Mori and P. C. M. van Zijl, “Fiber tracking: principles and strategies—a technical review,” NMR in Biomedicine, vol. 15, no. 7-8, pp. 468–480, 2002. View at Publisher · View at Google Scholar · View at PubMed
  13. T. E. J. Behrens, H. Johansen-Berg, M. W. Woolrich, et al., “Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging,” Nature Neuroscience, vol. 6, no. 7, pp. 750–757, 2003. View at Publisher · View at Google Scholar · View at PubMed
  14. S. Lehericy, M. Ducros, A. Krainik, et al., “3-D diffusion tensor axonal tracking shows distinct SMA and pre-SMA projections to the human striatum,” Cerebral Cortex, vol. 14, no. 12, pp. 1302–1309, 2004. View at Publisher · View at Google Scholar · View at PubMed
  15. C. Büchel, J. T. Coull, and K. J. Friston, “The predictive value of changes in effective connectivity for human learning,” Science, vol. 283, no. 5407, pp. 1538–1541, 1999. View at Publisher · View at Google Scholar
  16. P. Fletcher, C. Büchel, O. Josephs, K. Friston, and R. Dolan, “Learning-related neuronal responses in prefrontal cortex studied with functional neuroimaging,” Cerebral Cortex, vol. 9, no. 2, pp. 168–178, 1999. View at Publisher · View at Google Scholar
  17. E. Koechlin, C. Ody, and F. Kouneiher, “The architecture of cognitive control in the human prefrontal cortex,” Science, vol. 302, no. 5648, pp. 1181–1185, 2003. View at Publisher · View at Google Scholar · View at PubMed
  18. J. B. Rowe, K. E. Stephan, K. Friston, R. S. J. Frackowiak, and R. E. Passingham, “The prefrontal cortex shows context-specific changes in effective connectivity to motor or visual cortex during the selection of action or colour,” Cerebral Cortex, vol. 15, no. 1, pp. 85–95, 2005. View at Publisher · View at Google Scholar · View at PubMed
  19. H. Kondo, M. Morishita, N. Osaka, M. Osaka, H. Fukuyama, and H. Shibasaki, “Functional roles of the cingulo-frontal network in performance on working memory,” NeuroImage, vol. 21, no. 1, pp. 2–14, 2004. View at Publisher · View at Google Scholar
  20. A. R. McIntosh, R. E. Cabeza, and N. J. Lobaugh, “Analysis of neural interactions explains the activation of occipital cortex by an auditory stimulus,” Journal of Neurophysiology, vol. 80, no. 5, pp. 2790–2796, 1998.
  21. D. A. Seminowicz, H. S. Mayberg, A. R. McIntosh, et al., “Limbic-frontal circuitry in major depression: a path modeling metanalysis,” NeuroImage, vol. 22, no. 1, pp. 409–418, 2004. View at Publisher · View at Google Scholar · View at PubMed
  22. N. S. White and M. T. Alkire, “Impaired thalamocortical connectivity in humans during general-anesthetic- induced unconsciousness,” NeuroImage, vol. 19, no. 2, pp. 402–411, 2003. View at Publisher · View at Google Scholar
  23. T. G. Reese, O. Heid, R. M. Weisskoff, and V. J. Wedeen, “Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo,” Magnetic Resonance in Medicine, vol. 49, no. 1, pp. 177–182, 2003. View at Publisher · View at Google Scholar · View at PubMed
  24. D. K. Jones, M. A. Horsfield, and A. Simmons, “Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging,” Magnetic Resonance in Medicine, vol. 42, no. 3, pp. 515–525, 1999. View at Publisher · View at Google Scholar
  25. D. C. Alexander, C. Pierpaoli, P. J. Basser, and J. C. Gee, “Spatial transformations of diffusion tensor magnetic resonance images,” IEEE Transactions on Medical Imaging, vol. 20, no. 11, pp. 1131–1139, 2001. View at Publisher · View at Google Scholar · View at PubMed
  26. H.-J. Park, M. Kubicki, M. E. Shenton, et al., “Spatial normalization of diffusion tensor MRI using multiple channels,” NeuroImage, vol. 20, no. 4, pp. 1995–2009, 2003. View at Publisher · View at Google Scholar
  27. V. Glausche, “Diffusion Toolbox,” 2007.
  28. H. Jiang and S. Mori, “DTI-Studio,” 2007.
  29. S. Mori, B. J. Crain, V. P. Chacko, and P. C. M. van Zijl, “Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging,” Annals of Neurology, vol. 45, no. 2, pp. 265–269, 1999. View at Publisher · View at Google Scholar
  30. A. R. McIntosh, F. L. Bookstein, J. V. Haxby, and C. L. Grady, “Spatial pattern analysis of functional brain images using partial least squares,” NeuroImage, vol. 3, no. 3, pp. 143–157, 1996. View at Publisher · View at Google Scholar · View at PubMed
  31. B. A. Vogt and D. N. Pandya, “Cingulate cortex of the rhesus monkey: II. Corical afferents,” Journal of Comparative Neurology, vol. 262, no. 2, pp. 271–289, 1987. View at Publisher · View at Google Scholar · View at PubMed
  32. P. J. Orioli and P. L. Strick, “Cerebellar connections with the motor cortex and the arcuate premotor area: an analysis employing retrograde transneuronal transport of WGA-HRP,” Journal of Comparative Neurology, vol. 288, no. 4, pp. 612–626, 1989. View at Publisher · View at Google Scholar · View at PubMed
  33. K. M. Jansons and D. C. Alexander, “Persistent angular structure: new insights from diffusion magnetic resonance imaging data,” Inverse Problems, vol. 19, no. 5, pp. 1031–1046, 2003. View at Publisher · View at Google Scholar · View at MathSciNet
  34. M. R. Wiegell, H. B. W. Larsson, and V. J. Wedeen, “Fiber crossing in human brain depicted with diffusion tensor MR imaging,” Radiology, vol. 217, no. 3, pp. 897–903, 2000.
  35. W. D. Penny, K. E. Stephan, A. Mechelli, and K. J. Friston, “Comparing dynamic causal models,” NeuroImage, vol. 22, no. 3, pp. 1157–1172, 2004. View at Publisher · View at Google Scholar · View at PubMed
  36. T. E. J. Behrens, H. J. Berg, S. Jbabdi, M. F. S. Rushworth, and M. W. Woolrich, “Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?,” NeuroImage, vol. 34, no. 1, pp. 144–155, 2007. View at Publisher · View at Google Scholar · View at PubMed
  37. T. Hosey, G. Williams, and R. Ansorge, “Inference of multiple fiber orientations in high angular resolution diffusion imaging,” Magnetic Resonance in Medicine, vol. 54, no. 6, pp. 1480–1489, 2005. View at Publisher · View at Google Scholar · View at PubMed
  38. J.-D. Tournier, F. Calamante, D. G. Gadian, and A. Connelly, “Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution,” NeuroImage, vol. 23, no. 3, pp. 1176–1185, 2004. View at Publisher · View at Google Scholar · View at PubMed
  39. D. S. Tuch, “Q-ball imaging,” Magnetic Resonance in Medicine, vol. 52, no. 6, pp. 1358–1372, 2004. View at Publisher · View at Google Scholar · View at PubMed
  40. T. E. J. Behrens, M. W. Woolrich, M. Jenkinson, et al., “Characterization and propagation of uncertainty in diffusion-weighted MR imaging,” Magnetic Resonance in Medicine, vol. 50, no. 5, pp. 1077–1088, 2003. View at Publisher · View at Google Scholar · View at PubMed
  41. O. Friman, G. Farnebäck, and C.-F. Westin, “A Bayesian approach for stochastic white matter tractography,” IEEE Transactions on Medical Imaging, vol. 25, no. 8, pp. 965–978, 2006. View at Publisher · View at Google Scholar
  42. M. A. Koch, D. G. Norris, and M. Hund-Georgiadis, “An investigation of functional and anatomical connectivity using magnetic resonance imaging,” NeuroImage, vol. 16, no. 1, pp. 241–250, 2002. View at Publisher · View at Google Scholar · View at PubMed
  43. M. Lazar and A. L. Alexander, “Bootstrap white matter tractography (BOOT-TRAC),” NeuroImage, vol. 24, no. 2, pp. 524–532, 2005. View at Publisher · View at Google Scholar · View at PubMed
  44. G. J. M. Parker, H. A. Haroon, and C. A. M. Wheeler-Kingshott, “A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements,” Journal of Magnetic Resonance Imaging, vol. 18, no. 2, pp. 242–254, 2003. View at Publisher · View at Google Scholar · View at PubMed