Table of Contents Author Guidelines Submit a Manuscript
International Journal of Alzheimer’s Disease
Volume 2014 (2014), Article ID 278096, 12 pages
http://dx.doi.org/10.1155/2014/278096
Research Article

High-Dimensional Medial Lobe Morphometry: An Automated MRI Biomarker for the New AD Diagnostic Criteria

1Départment de Radiologie, Faculté de Médecine, Université Laval, Quebec, QC, Canada G1V 0A6
2Institut Universitaire de Santé Mentale de Québec, 2601 de la Canardiére/F-3582, Quebec, QC, Canada G1J 2G3

Received 24 March 2014; Accepted 25 July 2014; Published 31 August 2014

Academic Editor: Lucilla Parnetti

Copyright © 2014 Simon Duchesne 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. 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
  2. G. M. McKhann, D. S. Knopman, H. Chertkow et al., “The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease,” Alzheimer's and Dementia, vol. 7, no. 3, pp. 263–269, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. M. S. Albert, S. T. DeKosky, D. Dickson et al., “The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease,” Alzheimer's & Dementia, vol. 7, no. 3, pp. 270–279, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Braak and E. Braak, “Neuropathological stageing of Alzheimer-related changes,” Acta Neuropathologica, vol. 82, no. 4, pp. 239–259, 1991. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Braak, E. Braak, and J. Bohl, “Staging of Alzheimer-related cortical destruction,” European Neurology, vol. 33, no. 6, pp. 403–408, 1993. View at Publisher · View at Google Scholar · View at Scopus
  6. B. Dubois and M. L. Albert, “Amnestic MCI or prodromal Alzheimer's disease?” The Lancet Neurology, vol. 3, no. 4, pp. 246–248, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. M. L. Ries, C. M. Carlsson, H. A. Rowley et al., “Magnetic resonance imaging characterization of brain structure and function in mild cognitive impairment: a review,” Journal of the American Geriatrics Society, vol. 56, no. 5, pp. 920–934, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Bozzali, M. Cercignani, and C. Caltagirone, “Brain volumetrics to investigate aging and the principal forms of degenerative cognitive decline: a brief review,” Magnetic Resonance Imaging, vol. 26, no. 7, pp. 1065–1070, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. G. B. Frisoni and C. R. Jack, “Harmonization of magnetic resonance-based manual hippocampal segmentation: a mandatory step for wide clinical use,” Alzheimer's & Dementia, vol. 7, no. 2, pp. 171–174, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. A. R. Khan, N. Cherbuin, W. Wen, K. J. Anstey, P. Sachdev, and M. F. Beg, “Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): validation on hippocampus segmentation,” NeuroImage, vol. 56, no. 1, pp. 126–139, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. C. A. Bishop, M. Jenkinson, J. Andersson, J. Declerck, and D. Merhof, “Novel Fast Marching for Automated Segmentation of the Hippocampus (FMASH): method and validation on clinical data,” NeuroImage, vol. 55, no. 3, pp. 1009–1019, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Coupe, J. V. Manjon, V. Fonov et al., “Nonlocal patch-based label fusion for hippocampus segmentation. Medical image computing and computer-assisted intervention,” in Proceedings of the MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 13, pp. 129–136, 2010.
  13. D. L. Collins and J. C. Pruessner, “Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion,” NeuroImage, vol. 52, no. 4, pp. 1355–1366, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Pluta, B. B. Avants, S. Glynn, S. Awate, J. C. Gee, and J. A. Detre, “Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation,” Hippocampus, vol. 19, no. 6, pp. 565–571, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Chupin, A. Hammers, R. S. N. Liu et al., “Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation,” NeuroImage, vol. 46, no. 3, pp. 749–761, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Barnes, J. Foster, R. G. Boyes et al., “A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus,” NeuroImage, vol. 40, no. 4, pp. 1655–1671, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Orru, W. Pettersson-Yeo, A. F. Marquand, G. Sartori, and A. Mechelli, “Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review,” Neuroscience & Biobehavioral Reviews, vol. 36, no. 4, pp. 1140–1152, 2012. View at Publisher · View at Google Scholar
  18. R. Cuingnet, E. Gerardin, J. Tessieras et al., “Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database,” NeuroImage, vol. 56, no. 2, pp. 766–781, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. 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
  20. J. H. Jhoo, D. Y. Lee, I. H. Choo et al., “Discrimination of normal aging, MCI and AD with multimodal imaging measures on the medial temporal lobe,” Psychiatry Research, vol. 183, no. 3, pp. 237–243, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. 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
  22. J. Koikkalainen, J. Lötjönen, L. Thurfjell, D. Rueckert, G. Waldemar, and H. Soininen, “Multi-template tensor-based morphometry: application to analysis of Alzheimer's disease,” NeuroImage, vol. 56, no. 3, pp. 1134–1144, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. M. López, J. Ramírez, J. M. Górriz et al., “Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease,” Neurocomputing, vol. 74, pp. 1260–1271, 2011. View at Google Scholar
  24. C. Misra, Y. Fan, and C. Davatzikos, “Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI,” NeuroImage, vol. 44, no. 4, pp. 1415–1422, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. 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
  26. E. Westman, A. Simmons, Y. Zhang et al., “Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls,” NeuroImage, vol. 54, no. 2, pp. 1178–1187, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. D. Zhang, Y. Wang, L. Zhou, H. Yuan, and D. Shen, “Multimodal classification of Alzheimer's disease and mild cognitive impairment,” NeuroImage, vol. 55, no. 3, pp. 856–867, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. 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
  29. X. Hua, S. Lee, I. Yanovsky et al., “Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects,” NeuroImage, vol. 48, no. 4, pp. 668–681, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. 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, pp. 509–520, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Duchesne and A. Mouiha, “Morphological factor estimation via high-dimensional reduction: prediction of MCI conversion to probable AD,” International Journal of Alzheimer's Disease, vol. 2011, Article ID 914085, 8 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. L. K. Ferreira, B. S. Diniz, O. V. Forlenza, G. F. Busatto, and M. V. Zanetti, “Neurostructural predictors of Alzheimer's disease: a meta-analysis of VBM studies,” Neurobiology of Aging, vol. 32, no. 10, pp. 1733–1741, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. D. H. Salat, J. J. Chen, A. J. van der Kouwe, D. N. Greve, B. Fischl, and H. D. Rosas, “Hippocampal degeneration is associated with temporal and limbic gray matter/white matter tissue contrast in Alzheimer's disease,” NeuroImage, vol. 54, no. 3, pp. 1795–1802, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. G. Chetelat and J. Baron, “Early diagnosis of Alzheimer's disease: contribution of structural neuroimaging,” NeuroImage, vol. 18, no. 2, pp. 525–541, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. X. Hua, A. D. Leow, S. Lee et al., “3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry,” NeuroImage, vol. 41, no. 1, pp. 19–34, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. 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
  37. L. G. Nyul and J. K. Udupa, “On standardizing the MR image intensity scale,” Magnetic Resonance in Medicine, vol. 42, no. 6, pp. 1072–1081, 1999. View at Publisher · View at Google Scholar
  38. 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
  39. C. R. Jack Jr., M. A. Bernstein, B. J. Borowski et al., “Update on the magnetic resonance imaging core of the Alzheimer's disease neuroimaging initiative,” Alzheimer's and Dementia, vol. 6, no. 3, pp. 212–220, 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. C. R. Jack Jr., M. A. Bernstein, N. C. Fox et al., “The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods,” Journal of Magnetic Resonance Imaging, vol. 27, no. 4, pp. 685–691, 2008. View at Publisher · View at Google Scholar · View at Scopus
  41. L. G. Nyul, J. K. Udupa, and X. Zhang, “New variants of a method of MRI scale standardization,” IEEE Transactions on Medical Imaging, vol. 19, no. 2, pp. 143–150, 2000. View at Publisher · View at Google Scholar · View at Scopus
  42. N. Robitaille and S. Duchesne, MR Intensity Standardisation: Initial Results on the ADNI Dataset, Alzheimer's Association, Honolulu, Hawaii, USA, 2010.
  43. M. Pelaez-Coca, M. Bossa, and S. Olmos, “Discrimination of AD and normal subjects from MRI: anatomical versus statistical regions,” Neuroscience Letters, vol. 487, no. 1, pp. 113–117, 2011. View at Publisher · View at Google Scholar · View at Scopus
  44. 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 clinicia,” Journal of Psychiatric Research, vol. 12, no. 3, pp. 189–198, 1975. View at Publisher · View at Google Scholar
  45. J. C. Morris, “Clinical dementia rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type,” International Psychogeriatrics, vol. 9, supplement 1, pp. 173–176, 1997. View at Publisher · View at Google Scholar · View at Scopus
  46. D. Wechsler, WMS-R Wechsler Memory Scale—Revised Manual, The Psychological Corporation, Harcourt Brace Jovanovich, New York, NY, USA, 1987.
  47. G. McKhann, D. Drachman, M. Folstein et al., “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, pp. 939–944, 1984. View at Google Scholar
  48. J. G. Sied, 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 Publisher · View at Google Scholar · View at Scopus
  49. 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, pp. 425–441, 2008. View at Publisher · View at Google Scholar · View at Scopus
  50. 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 Publisher · View at Google Scholar · View at Scopus
  51. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 187–198, 1997. View at Publisher · View at Google Scholar · View at Scopus
  52. 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
  53. V. Fonov, A. Evans, R. C. McKinstry, C. R. Almli, and D. L. Collins, “Unbiased nonlinear average age-appropriate brain templates from birth to adulthood,” Neuroimage, vol. 47, p. S102, 2009. View at Publisher · View at Google Scholar
  54. N. Robitaille, A. Mouiha, B. Crépeault, F. Valdivia, and S. Duchesne, “Tissue-based MRI intensity standardization: application to multicentric datasets,” International Journal of Biomedical Imaging, vol. 2012, Article ID 347120, 11 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  55. A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, “Morphometric analysis of white matter lesions in MR images: method and validation,” IEEE Transactions on Medical Imaging, vol. 13, no. 4, pp. 716–724, 1994. View at Publisher · View at Google Scholar · View at Scopus
  56. A. P. Zijdenbos and B. M. Dawant, “Brain segmentation and white matter lesion detection in MR images,” Critical Reviews in Biomedical Engineering, vol. 22, no. 5-6, pp. 401–465, 1994. View at Google Scholar · View at Scopus
  57. H. Braak and E. Braak, “Evolution of the neuropathology of Alzheimer's disease,” Acta Neurologica Scandinavica, vol. 93, no. 165, pp. 3–12, 1996. View at Publisher · View at Google Scholar · View at Scopus
  58. O. Kohannim, X. Hua, D. P. Hibar et al., “Boosting power for clinical trials using classifiers based on multiple biomarkers,” Neurobiology of Aging, vol. 31, no. 8, pp. 1429–1442, 2010. View at Publisher · View at Google Scholar · View at Scopus
  59. Y. Cui, B. Liu, S. Luo et al., “Identification of conversion from mild cognitive impairment to alzheimer's disease using multivariate predictors,” PLoS ONE, vol. 6, no. 7, Article ID e21896, 2011. View at Publisher · View at Google Scholar · View at Scopus