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BioMed Research International
Volume 2014 (2014), Article ID 421743, 8 pages
http://dx.doi.org/10.1155/2014/421743
Research Article

Automated Identification of Dementia Using FDG-PET Imaging

1Shaanxi Provincial Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
2Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia
3Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia
4Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
5Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China

Received 30 April 2013; Revised 1 October 2013; Accepted 17 November 2013; Published 2 February 2014

Academic Editor: Annalena Venneri

Copyright © 2014 Yong Xia 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. American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Association, Washington, DC, USA, 4th edition, 1994.
  2. International AsD, World Alzheimer Report 2009 Executive Summary, 2009.
  3. R. Brookmeyer, E. Johnson, K. Ziegler-Graham, and H. M. Arrighi, “Forecasting the global burden of Alzheimer's disease,” Alzheimer's and Dementia, vol. 3, no. 3, pp. 186–191, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. M. D. Devous Sr., “Functional brain imaging in the dementias: role in early detection, differential diagnosis, and longitudinal studies,” European Journal of Nuclear Medicine, vol. 29, no. 12, pp. 1685–1696, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Sjögren and C. Andersen, “Frontotemporal dementia—a brief review,” Mechanisms of Ageing and Development, vol. 127, no. 2, pp. 180–187, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. 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
  7. D. Chan, N. C. Fox, R. Jenkins, R. I. Scahill, W. R. Crum, and M. N. Rossor, “Rates of global and regional cerebral atrophy in AD and frontotemporal dementia,” Neurology, vol. 57, no. 10, pp. 1756–1763, 2001. View at Google Scholar · View at Scopus
  8. N. C. Fox and J. M. Schott, “Imaging cerebral atrophy: normal ageing to Alzheimer's disease,” Lancet, vol. 363, no. 9406, pp. 392–394, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. S. M. Resnick, D. L. Pham, M. A. Kraut, A. B. Zonderman, and C. Davatzikos, “Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain,” Journal of Neuroscience, vol. 23, no. 8, pp. 3295–3301, 2003. View at Google Scholar · View at Scopus
  10. M. Grossman, C. McMillan, P. Moore et al., “What's in a name: voxel-based morphometric analyses of MRI and naming difficulty in Alzheimer's disease, frontotemporal dementia and corticobasal degeneration,” Brain, vol. 127, no. 3, pp. 628–649, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, “Alzheimer's disease and models of computation: imaging, classification, and neural models,” Journal of Alzheimer's Disease, vol. 7, no. 3, pp. 187–199, 2005. View at Google Scholar · View at Scopus
  12. C. Hinrichs, V. Singh, G. Xu, and S. C. Johnson, “Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population,” NeuroImage, vol. 55, no. 2, pp. 574–589, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Caroli, A. Prestia, K. Chen et al., “Summary metrics to assess Alzheimer disease-related hypometabolic pattern with18F-FDG PET: head-to-head comparison,” Journal of Nuclear Medicine, vol. 53, no. 4, pp. 592–600, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Higdon, N. L. Foster, R. A. Koeppe et al., “A comparison of classification methods for differentiating fronto-temporal dementia from Alzheimer's disease using FDG-PET imaging,” Statistics in Medicine, vol. 23, no. 2, pp. 315–326, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Pagani, V. A. Kovalev, R. Lundqvist, H. Jacobsson, S. A. Larsson, and L. Thurfjell, “A new approach for improving diagnostic accuracy in Alzheimer's disease and frontal lobe dementia utilising the intrinsic properties of the SPET dataset,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 30, no. 11, pp. 1481–1488, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Davatzikos, S. M. Resnick, X. Wu, P. Parmpi, and C. M. Clark, “Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI,” NeuroImage, vol. 41, no. 4, pp. 1220–1227, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Hinrichs, V. Singh, G. Xu et al., “MKL for robust Multi-modality AD Classification,” in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '09), vol. 5762, pp. 786–794, 2009.
  18. 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
  19. L. Wen, M. Bewley, S. Eberl, M. Fulham, and D. Feng, “Classification of dementia from FDG-PET parametric images using data mining,” in Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '08), pp. 412–415, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. Xia, L. Wen, S. Eberl, M. Fulham, and D. Feng, “Genetic algorithm-based PCA eigenvector selection and weighting for automated identification of dementia using FDG-PET imaging,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2008, pp. 4812–4815, 2008. View at Google Scholar · View at Scopus
  21. Y. Xia, Z. Zhang, L. Wen et al., “GA and AdaBoost-based feature selection and combination for automated identification of dementia using FDG-PET imaging,” in Intelligent Science and Intelligent Data Engineering, Y. Zhang, Z. H. Zhou, C. Zhang, and Y. Li, Eds., pp. 128–135, Springer, Berlin, Germany, 2012. View at Google Scholar
  22. S. Eberl, A. R. Anayat, R. R. Fulton, P. K. Hooper, and M. J. Fulham, “Evaluation of two population-based input functions for quantitative neurological FDG PET studies,” European Journal of Nuclear Medicine, vol. 24, no. 3, pp. 299–304, 1997. View at Publisher · View at Google Scholar · View at Scopus
  23. G. D. Hutchins, J. E. Holden, and R. A. Koeppe, “Alternative approach to single-scan estimation of cerebral glucose metabolic rate using glucose analogs, with particular application to ischemia,” Journal of Cerebral Blood Flow and Metabolism, vol. 4, no. 1, pp. 35–40, 1984. View at Google Scholar · View at Scopus
  24. N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou et al., “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,” NeuroImage, vol. 15, no. 1, pp. 273–289, 2002. View at Publisher · View at Google Scholar · View at Scopus
  25. D. L. Collins, A. P. Zijdenbos, V. Kollokian et al., “Design and construction of a realistic digital brain phantom,” IEEE Transactions on Medical Imaging, vol. 17, no. 3, pp. 463–468, 1998. View at Google Scholar · View at Scopus
  26. R. S. J. Frackowiak, J. T. Ashburner, W. D. Penny et al., Human Brain Function, Elsevier Academic Press, Amsterdam, The Netherlands, 2004.
  27. J. Talairach and P. Tournoux, Co-Planar Stereotaxic Atlas of the Human Brain, vol. 122, Thieme, New York, NY, USA, 1988.
  28. Y. Xia, L. Wen, S. Eberl, M. Fulham, and D. Feng, “Genetic algorithm-based PCA eigenvector selection and weighting for automated identification of dementia using FDG-PET imaging,” in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '08), pp. 4812–4815, August 2008. View at Scopus
  29. C. Chang and C. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. B. Schölkopf and A. J. Smola, “Learning with kernels: support vector machines, regularization, optimization, and beyond,” in Adaptive Computation and Machine Learning, vol. 18, pp. 1–626, MIT Press, Cambridge, Mass, USA, 2002. View at Google Scholar
  31. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” Journal of Machine Learning Research, vol. 5, pp. 27–72, 2004. View at Google Scholar · View at Scopus
  32. D. S. Knopman, S. T. DeKosky, J. L. Cummings et al., “Practice parameter: diagnosis of dementia (an evidence-based review): report of the quality standards subcommittee of the american academy of neurology,” Neurology, vol. 56, no. 9, pp. 1143–1153, 2001. View at Google Scholar · View at Scopus
  33. L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996. View at Google Scholar · View at Scopus
  34. V. N. Vapnik, Statitical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998.
  35. N. Zhou and L. Wang, “A modified T-test feature selection method and its application on the HapMap genotype data,” Genomics, Proteomics and Bioinformatics, vol. 5, no. 3-4, pp. 242–249, 2007. View at Publisher · View at Google Scholar · View at Scopus
  36. E. Gerardin, G. Chételat, M. Chupin et al., “Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging,” NeuroImage, vol. 47, no. 4, pp. 1476–1486, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Theodoridis and K. Koutroumbas, Pattern Recognition, Elsevier, Singapore, 4th edition, 2009.
  38. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar
  39. W. E. Klunk, H. Engler, A. Nordberg et al., “Imaging brain amyloid in Alzheimer's disease with Pittsburgh compound-B,” Annals of Neurology, vol. 55, no. 3, pp. 306–319, 2004. View at Publisher · View at Google Scholar · View at Scopus