Table of Contents Author Guidelines Submit a Manuscript
International Journal of Alzheimer’s Disease
Volume 2011 (2011), Article ID 914085, 8 pages
http://dx.doi.org/10.4061/2011/914085
Research Article

Morphological Factor Estimation via High-Dimensional Reduction: Prediction of MCI Conversion to Probable AD

1Départment de Radiologie, Faculté de Médecine, Université Laval, Québec, Canada G1K 7P4
2Centre de Recherche Université Laval Robert-Giffard, Québec, Canada G1J 2G3

Received 24 December 2010; Accepted 27 April 2011

Academic Editor: Katsuya Urakami

Copyright © 2011 Simon Duchesne and Abderazzak Mouiha. 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. Z. Lao, D. Shen, Z. Xue, B. Karacali, S. M. Resnick, and C. Davatzikos, “Morphological classification of brains via high-dimensional shape transformations and machine learning methods,” NeuroImage, vol. 21, no. 1, pp. 46–57, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Davatzikos, Y. Fan, X. Wu, D. Shen, and S. M. Resnick, “Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging,” Neurobiology of Aging, vol. 29, no. 4, pp. 514–523, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Duchesne, A. Caroli, C. Geroldi, C. Barillot, G. B. Frisoni, and D. L. Collins, “MRI-based automated computer classification of probable AD versus normal controls,” IEEE Transactions on Medical Imaging, vol. 27, no. 4, Article ID 4479633, pp. 509–520, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Fan, N. Batmanghelich, C. M. Clark, and C. Davatzikos, “Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline,” NeuroImage, vol. 39, no. 4, pp. 1731–1743, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Klöppel, C. M. Stonnington, C. Chu et al., “Automatic classification of MR scans in Alzheimer's disease,” Brain, vol. 131, no. 3, pp. 681–689, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Duchesne, C. Bocti, K. De Sousa, G. B. Frisoni, H. Chertkow, and D. L. Collins, “Amnestic MCI future clinical status prediction using baseline MRI features,” Neurobiology of Aging, vol. 31, no. 9, pp. 1606–1617, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. P. Vemuri, J. L. Gunter, M. L. Senjem et al., “Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies,” NeuroImage, vol. 39, no. 3, pp. 1186–1197, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Fan, S. M. Resnick, X. Wu, and C. Davatzikos, “Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study,” NeuroImage, vol. 41, no. 2, pp. 277–285, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Duchesne, “Quantitative evaluation of Alzheimer's disease,” in SPIE—Medical Imaging, SPIE Society, Orlando, Fla, USA, 2009. View at Google Scholar
  10. J. C. Mazziotta, A. W. Toga, A. Evans, P. Fox, and J. Lancaster, “A probabilistic atlas of the human brain: theory and rationale for its development : the International Consortium for Brain Mapping (ICBM),” NeuroImage, vol. 2, no. 2, pp. 89–101, 1995. View at Publisher · View at Google Scholar · View at Scopus
  11. G. McKhann, D. Drachman, and M. Folstein, “Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer's disease,” Neurology, vol. 34, no. 7, pp. 939–944, 1984. View at Google Scholar · View at Scopus
  12. S. Galluzzi, C. Testa, M. Boccardi et al., “The Italian brain normative archive of structural MR scans: norms for medial temporal atrophy and white matter lesions,” Aging—Clinical and Experimental Research, vol. 21, no. 4–5, pp. 266–276, 2009. View at Google Scholar · View at Scopus
  13. M. F. Folstein, S. E. Folstein, and P. R. McHugh, ““Mini mental state”. A practical method for grading the cognitive state of patients for the clinician,” Journal of Psychiatric Research, vol. 12, no. 3, pp. 189–198, 1975. View at Publisher · View at Google Scholar · View at Scopus
  14. R. C. Petersen, R. Doody, A. Kurz et al., “Current concepts in mild cognitive impairment,” Archives of Neurology, vol. 58, no. 12, pp. 1985–1992, 2001. View at Google Scholar · View at Scopus
  15. R. C. Petersen, “Mild cognitive impairment as a diagnostic entity,” Journal of Internal Medicine, vol. 256, no. 3, pp. 183–194, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Dubois, H. H. Feldman, C. Jacova et al., “Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria,” The Lancet Neurology, vol. 6, no. 8, pp. 734–746, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Erkinjuntti, D. Inzitari, L. Pantoni et al., “Research criteria for subcortical vascular dementia in clinical trials,” Journal of Neural Transmission, Supplement, no. 59, pp. 23–30, 2000. View at Google Scholar · View at Scopus
  18. I. G. McKeith, C. G. Ballard, R. H. Perry et al., “Prospective validation of consensus criteria for the diagnosis of dementia with Lewy bodies,” Neurology, vol. 54, no. 5, pp. 1050–1058, 2000. View at Google Scholar · View at Scopus
  19. D. S. Knopman, B. F. Boeve, J. E. Parisi et al., “Antemortem diagnosis of frontotemporal lobar degeneration,” Annals of Neurology, vol. 57, no. 4, pp. 480–488, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. G. B. Frisoni, W. J. P. Henneman, M. W. Weiner et al., “The pilot European Alzheimer's disease neuroimaging initiative of the European Alzheimer's disease consortium,” Alzheimer's and Dementia, vol. 4, no. 4, pp. 255–264, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, “A nonparametric method for automatic correction of intensity nonuniformity in MRI data,” IEEE Transactions on Medical Imaging, vol. 17, no. 1, pp. 87–97, 1998. View at Google Scholar · View at Scopus
  22. P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot, “An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images,” IEEE Transactions on Medical Imaging, vol. 27, no. 4, Article ID 4359947, pp. 425–441, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. D. L. Collins, P. Neelin, T. M. Peters, and A. C. Evans, “Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space,” Journal of Computer Assisted Tomography, vol. 18, no. 2, pp. 192–205, 1994. View at Google Scholar · View at Scopus
  24. D. L. Collins and A. C. Evans, “Animal: validation and application of nonlinear registration-based segmentation,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 11, no. 8, pp. 1271–1294, 1997. View at Google Scholar · View at Scopus
  25. M. K. Chung, K. J. Worsley, T. Paus et al., “A unified statistical approach to deformation-based morphometry,” NeuroImage, vol. 14, no. 3, pp. 595–606, 2001. View at Publisher · View at Google Scholar · View at Scopus
  26. J. Koikkalainen et al., “Estimation of disease state using statistical information from medical imaging data,” in Medical Image Computing and Computer Assisted Intervention—From Statistical Atlases to Personnalized Models Workshop, MICCAI Society, Copenhagen, Denmark, 2006. View at Google Scholar
  27. H. Braak and E. Braak, “Neuropathological stageing of Alzheimer-related changes,” Acta Neuropathologica, vol. 82, no. 4, pp. 239–259, 1991. View at Google Scholar · View at Scopus
  28. D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner, “Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,” Journal of Cognitive Neuroscience, vol. 19, no. 9, pp. 1498–1507, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. S. G. Mueller, M. W. Weiner, L. J. Thal et al., “Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's disease neuroimaging initiative (ADNI),” Alzheimer's and Dementia, vol. 1, no. 1, pp. 55–66, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. L. O. Wahlund, P. Julin, S. E. Johansson, and P. Scheltens, “Visual rating and volumetry of the medial temporal lobe on magnetic resonance imaging in dementia: a comparative study,” Journal of Neurology Neurosurgery and Psychiatry, vol. 69, no. 5, pp. 630–635, 2000. View at Publisher · View at Google Scholar · View at Scopus
  31. L. O. Wahlund and K. Blennow, “Cerebrospinal fluid biomarkers for disease stage and intensity in cognitively impaired patients,” Neuroscience Letters, vol. 339, no. 2, pp. 99–102, 2003. View at Publisher · View at Google Scholar · View at Scopus