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Increasing Power to Predict Mild Cognitive Impairment Conversion to Alzheimer’s Disease Using Hippocampal Atrophy Rate and Statistical Shape Models

  • Kelvin K. Leung
  • Kai-Kai Shen
  • Josephine Barnes
  • Gerard R. Ridgway
  • Matthew J. Clarkson
  • Jurgen Fripp
  • Olivier Salvado
  • Fabrice Meriaudeau
  • Nick C. Fox
  • Pierrick Bourgeat
  • Sébastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6362)

Abstract

Identifying mild cognitive impairment (MCI) subjects who will convert to clinical Alzheimer’s disease (AD) is important for therapeutic decisions, patient counselling and clinical trials. Hippocampal volume and rate of atrophy predict clinical decline at the MCI stage and progression to AD. In this paper, we create p-maps from the differences in the shape of the hippocampus between 60 normal controls and 60 AD subjects using statistical shape models, and generate different regions of interest (ROI) by thresholding the p-maps at different significance levels. We demonstrate increased statistical power to classify 86 MCI converters and 128 MCI stable subjects using the hippocampal atrophy rates calculated by the boundary shift integral within these ROIs.

Keywords

Mild Cognitive Impairment Hippocampal Volume Hippocampal Atrophy Statistical Shape Model Mild Cognitive Impairment Subject 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., Kokmen, E.: Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56(3), 303–308 (1999)CrossRefGoogle Scholar
  2. 2.
    Apostolova, L.G., Dutton, R.A., Dinov, I.D., Hayashi, K.M., Toga, A.W., Cummings, J.L., Thompson, P.M.: Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch. Neurol. 63(5), 693–699 (2006)CrossRefGoogle Scholar
  3. 3.
    Chtelat, G., Fouquet, M., Kalpouzos, G., Denghien, I., la Sayette, V.D., Viader, F., Mzenge, F., Landeau, B., Baron, J.C., Eustache, F., Desgranges, B.: Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry. Neuropsychologia 46(6), 1721–1731 (2008)CrossRefGoogle Scholar
  4. 4.
    Chupin, M., Garardin, E., Cuingnet, R., Boutet, C., Lemieux, L., Lehericy, S., Benali, H., Garnero, L., Colliot, O.: Alzheimer’s Disease Neuroimaging Initiative: Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19(6), 579–587 (2009)CrossRefGoogle Scholar
  5. 5.
    Thompson, P.M., Hayashi, K.M., Zubicaray, G.I.D., Janke, A.L., Rose, S.E., Semple, J., Hong, M.S., Herman, D.H., Gravano, D., Doddrell, D.M., Toga, A.W.: Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage 22(4), 1754–1766 (2004)CrossRefGoogle Scholar
  6. 6.
    Hua, X., Lee, S., Yanovsky, I., Leow, A.D., Chou, Y.Y., Ho, A.J., Gutman, B., Toga, A.W., Jack, C.R., Bernstein, M.A., Reiman, E.M., Harvey, D.J., Kornak, J., Schuff, N., Alexander, G.E., Weiner, M.W., Thompson, P.M.: Alzheimer’s Disease Neuroimaging Initiative: 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 48(4), 668–681 (2009)CrossRefGoogle Scholar
  7. 7.
    Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Avedissian, C., Madsen, S.K., Parikshak, N., Hua, X., Toga, A.W., Jack, C.R., Schuff, N., Weiner, M.W., Thompson, P.M.: Alzheimer’s Disease Neuroimaging Initiative: Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Hum. Brain Mapp. 30(9), 2766–2788 (2009)CrossRefGoogle Scholar
  8. 8.
    Haller, J.W., Banerjee, A., Christensen, G.E., Gado, M., Joshi, S., Miller, M.I., Sheline, Y., Vannier, M.W., Csernansky, J.G.: Three-dimensional hippocampal MR morphometry with high-dimensional transformation of a neuroanatomic atlas. Radiology 202(2), 504–510 (1997)Google Scholar
  9. 9.
    Schuff, N., Woerner, N., Boreta, L., Kornfield, T., Shaw, L.M., Trojanowski, J.Q., Thompson, P.M., Jack, C.R., Weiner, M.W., Initiative, A.D.N.: MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain 132(Pt 4), 1067–1077 (2009)Google Scholar
  10. 10.
    Davies, R.H., Twining, C.J., Taylor, C.: Groupwise surface correspondence by optimization: representation and regularization. Med. Image Anal. 12(6), 787–796 (2008)CrossRefGoogle Scholar
  11. 11.
    Freeborough, P., Fox, N.: The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Transactions in Medical Imaging 16(5), 623–629 (1997)CrossRefGoogle Scholar
  12. 12.
    Barnes, J., Foster, J., Boyes, R.G., Pepple, T., Moore, E.K., Schott, J.M., Frost, C., Scahill, R.I., Fox, N.C.: A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus. Neuroimage 40(4), 1655–1671 (2008)CrossRefGoogle Scholar
  13. 13.
    Hobbs, N.Z., Henley, S.M.D., Wild, E.J., Leung, K.K., Frost, C., Barker, R.A., Scahill, R.I., Barnes, J., Tabrizi, S.J., Fox, N.C.: Automated quantification of caudate atrophy by local registration of serial MRI: evaluation and application in Huntington’s disease. Neuroimage 47(4), 1659–1665 (2009)CrossRefGoogle Scholar
  14. 14.
    Leung, K.K., Clarkson, M.J., Bartlett, J.W., Clegg, S., Jack, C.R., Weiner, M.W., Fox, N.C., Ourselin, S.: Alzheimer’s Disease Neuroimaging Initiative: Robust atrophy rate measurement in Alzheimer’s disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection. Neuroimage 50(2), 516–523 (2010)CrossRefGoogle Scholar
  15. 15.
    Wang, L., Swank, J.S., Glick, I.E., Gado, M.H., Miller, M.I., Morris, J.C., Csernansky, J.G.: Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging. Neuroimage 20(2), 667–682 (2003)CrossRefGoogle Scholar
  16. 16.
    Mueller, S.G., Weiner, M.W.: Selective effect of age, Apo e4, and Alzheimer’s disease on hippocampal subfields. Hippocampus 19(6), 558–564 (2009)CrossRefGoogle Scholar
  17. 17.
    Konrad, C., Ukas, T., Nebel, C., Arolt, V., Toga, A.W., Narr, K.L.: Defining the human hippocampus in cerebral magnetic resonance images–an overview of current segmentation protocols. Neuroimage 47(4), 1185–1195 (2009)CrossRefGoogle Scholar
  18. 18.
    Leung, K.K., Barnes, J., Ridgway, G.R., Bartlett, J.W., Clarkson, M.J., Macdonald, K., Schuff, N., Fox, N.C., Ourselin, S.: Alzheimer’s Disease Neuroimaging Initiative: Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer’s disease. Neuroimage 51(4), 1345–1359 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kelvin K. Leung
    • 1
    • 2
  • Kai-Kai Shen
    • 3
    • 4
  • Josephine Barnes
    • 2
  • Gerard R. Ridgway
    • 1
    • 2
  • Matthew J. Clarkson
    • 1
    • 2
  • Jurgen Fripp
    • 3
  • Olivier Salvado
    • 3
  • Fabrice Meriaudeau
    • 4
  • Nick C. Fox
    • 2
  • Pierrick Bourgeat
    • 3
  • Sébastien Ourselin
    • 1
    • 2
  1. 1.Centre for Medical Image ComputingUniversity College LondonUK
  2. 2.Dementia Research CentreUCL Institute of NeurologyLondonUK
  3. 3.Australian eHealth Research CentreCSIRO ICT CentreAustralia
  4. 4.Université de Bourgogne, Le2i UMR CNRS 5158France

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