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Research Article

Early prediction of progression to Alzheimer’s disease using multi-modality neuroimages by a novel ordinal learning model ADPacer

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References

  • Alzheimer Association. (2022). 2022 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 18(4), 700–789. https://doi.org/10.1002/alz.12638
  • Cai, K., Xu, H., Guan, H., Zhu, W., Jiang, J., Cui, Y., Zhang, J., Liu, T., & Wen, W. (2017). Identification of early-stage Alzheimer’s disease using Sulcal morphology and other common neuroimaging indices. PLoS One. 12(1), e0170875. https://doi.org/10.1371/journal.pone.0170875
  • Chu, W., & Ghahramani, Z. (2005). Gaussian processes for ordinal regression. Journal of Machine Learning Research, 6(35), 1019–1041.
  • Chu, W., & Keerthi, S. S. (2007). Support vector ordinal regression. Neural Computation, 19(3), 792–815. https://doi.org/10.1162/neco.2007.19.3.792
  • Desikan, R. S., Cabral, H. J., Settecase, F., Hess, C. P., Dillon, W. P., Glastonbury, C. M., Weiner, M. W., Schmansky, N. J., Salat, D. H., & Fischl, B.; Alzheimer’s Disease Neuroimaging Initiative. (2010). Automated MRI measures predict progression to Alzheimer’s disease. Neurobiology of Aging, 31(8), 1364–1374. https://doi.org/10.1016/j.neurobiolaging.2010.04.023
  • Doyle, O. M., Westman, E., Marquand, A. F., Mecocci, P., Vellas, B., Tsolaki, M., Kłoszewska, I., Soininen, H., Lovestone, S., Williams, S. C. R., & Simmons, A. (2014). Predicting progression of Alzheimer’s disease using ordinal regression. PLoS One. 9(8), e105542. https://doi.org/10.1371/journal.pone.0105542
  • Ellis, K. A., Bush, A. I., Darby, D., De Fazio, D., Foster, J., Hudson, P., Lautenschlager, N. T., Lenzo, N., Martins, R. N., Maruff, P., Masters, C., Milner, A., Pike, K., Rowe, C., Savage, G., Szoeke, C., Taddei, K., Villemagne, V., Woodward, M., & Ames, D.; AIBL Research Group. (2009). The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: Methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. International Psychogeriatrics, 21(4), 672–687. https://doi.org/10.1017/S1041610209009405
  • Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
  • Fleisher, A. S., Chen, K., Liu, X., Roontiva, A., Thiyyagura, P., Ayutyanont, N., Joshi, A. D., Clark, C. M., Mintun, M. A., Pontecorvo, M. J., Doraiswamy, P. M., Johnson, K. A., Skovronsky, D. M., & Reiman, E. M. (2011). Using positron emission tomography and florbetapir F 18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer disease. Archives of Neurology, 68(11), 1404–1411. https://doi.org/10.1001/archneurol.2011.150
  • Harrell, F. E. (2015). Ordinal logistic regression, in Regression modeling strategies (pp. 311–325). Springer.
  • Holtzman, D. M., Morris, J. C., & Goate, A. M. (2011). Alzheimer’s disease: The challenge of the second century. Science Translational Medicine, 3(77), 77sr1. https://doi.org/10.1126/scitranslmed.3002369
  • Hornung, R. (2020). Ordinal forests. Journal of Classification, 37(1), 4–17. https://doi.org/10.1007/s00357-018-9302-x
  • Jacobs, H. I. L., van Boxtel, M. P. J., Jolles, J., Verhey, F. R. J., & Uylings, H. B. M. (2012). Parietal cortex matters in Alzheimer’s disease: An overview of structural, functional and metabolic findings. Neuroscience and Biobehavioral Reviews, 36(1), 297–309. https://doi.org/10.1016/j.neubiorev.2011.06.009
  • Klunk, W. E., Koeppe, R. A., Price, J. C., Benzinger, T. L., Devous, M. D., Jagust, W. J., Johnson, K. A., Mathis, C. A., Minhas, D., Pontecorvo, M. J., Rowe, C. C., Skovronsky, D. M., & Mintun, M. A. (2015). The Centiloid Project: Standardizing quantitative amyloid plaque estimation by PET. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 11(1), 1–15. https://doi.org/10.1016/j.jalz.2014.07.003
  • Liu, M., Zhang, J., Yap, P.-T., & Shen, D. (2017). View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data. Medical Image Analysis, 36, 123–134. https://doi.org/10.1016/j.media.2016.11.002
  • Liu, X., Chen, K., Weidman, D., Wu, T., Lure, F., & Li, J.; Alzheimer’s Disease Neuroimaging Initiative. (2021). A novel transfer learning model for predictive analytics using incomplete multimodality data. IISE Transactions, 53(9), 1010–1022. https://doi.org/10.1080/24725854.2020.1798569
  • Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., Galluzzi, S., Marizzoni, M., & Frisoni, G. B. (2016). Brain atrophy in Alzheimer’s disease and aging. Ageing Research Reviews, 30, 25–48. https://doi.org/10.1016/j.arr.2016.01.002
  • Rosenberg, P. B., Wong, D. F., Edell, S. L., Ross, J. S., Joshi, A. D., Brašić, J. R., Zhou, Y., Raymont, V., Kumar, A., Ravert, H. T., Dannals, R. F., Pontecorvo, M. J., Skovronsky, D. M., & Lyketsos, C. G. (2013). Cognition and amyloid load in Alzheimer disease imaged with florbetapir F 18 (AV-45) positron emission tomography. The American Journal of Geriatric Psychiatry, 21(3), 272–278. https://doi.org/10.1016/j.jagp.2012.11.016
  • Schwarz, C. G., Gunter, J. L., Wiste, H. J., Przybelski, S. A., Weigand, S. D., Ward, C. P., Senjem, M. L., Vemuri, P., Murray, M. E., Dickson, D. W., Parisi, J. E., Kantarci, K., Weiner, M. W., Petersen, R. C., & Jack, C. R.; Alzheimer’s Disease Neuroimaging Initiative. (2016). A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer’s disease severity. NeuroImage. Clinical, 11, 802–812. https://doi.org/10.1016/j.nicl.2016.05.017
  • Shen, H. T., Zhu, X., Zhang, Z., Wang, S.-H., Chen, Y., Xu, X., & Shao, J. (2021). Heterogeneous data fusion for predicting mild cognitive impairment conversion. Information Fusion, 66, 54–63. https://doi.org/10.1016/j.inffus.2020.08.023
  • Su, Y., Blazey, T. M., Snyder, A. Z., Raichle, M. E., Marcus, D. S., Ances, B. M., Bateman, R. J., Cairns, N. J., Aldea, P., Cash, L., Christensen, J. J., Friedrichsen, K., Hornbeck, R. C., Farrar, A. M., Owen, C. J., Mayeux, R., Brickman, A. M., Klunk, W., Price, J. C., … Benzinger, T. L. S.; Dominantly Inherited Alzheimer Network. (2015). Partial volume correction in quantitative amyloid imaging. Neuroimage, 107, 55–64. https://doi.org/10.1016/j.neuroimage.2014.11.058
  • Su, Y., D'Angelo, G. M., Vlassenko, A. G., Zhou, G., Snyder, A. Z., Marcus, D. S., Blazey, T. M., Christensen, J. J., Vora, S., Morris, J. C., Mintun, M. A., & Benzinger, T. L. S. (2013). Quantitative analysis of PiB-PET with freesurfer ROIs. PLoS One. 8(11), e73377. https://doi.org/10.1371/journal.pone.0073377
  • Su, Y., Flores, S., Wang, G., Hornbeck, R. C., Speidel, B., Joseph-Mathurin, N., Vlassenko, A. G., Gordon, B. A., Koeppe, R. A., Klunk, W. E., Jack, C. R., Farlow, M. R., Salloway, S., Snider, B. J., Berman, S. B., Roberson, E. D., Brosch, J., Jimenez-Velazques, I., van Dyck, C. H., … Benzinger, T. L. S. (2019). Comparison of Pittsburgh compound B and florbetapir in cross‐sectional and longitudinal studies. Alzheimer’s & Dementia (Amsterdam, Netherlands), 11(1), 180–190. https://doi.org/10.1016/j.dadm.2018.12.008
  • van Oostveen, W. M., & de Lange, E. C. M. (2021). Imaging techniques in Alzheimer’s disease: A review of applications in early diagnosis and longitudinal monitoring. International Journal of Molecular Sciences, 22(4), 2110. https://doi.org/10.3390/ijms22042110
  • Xu, L., Wu, X., Li, R., Chen, K., Long, Z., Zhang, J., Guo, X., & Yao, L.; Alzheimer’s Disease Neuroimaging Initiative. (2016). Prediction of progressive mild cognitive impairment by multi-modal neuroimaging biomarkers. Journal of Alzheimer’s Disease: JAD, 51(4), 1045–1056. https://doi.org/10.3233/JAD-151010
  • Zhang, S., Smailagic, N., Hyde, C., Noel-Storr, A. H., Takwoingi, Y., McShane, R., & Feng, J.; Cochrane Dementia and Cognitive Improvement Group. (2014). 11 C‐PIB‐PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database of Systematic Reviews, 7, CD010386. https://doi.org/10.1002/14651858.CD010386.pub2
  • Zhang, T., & Shi, M. (2020). Multi-modal neuroimaging feature fusion for diagnosis of Alzheimer’s disease. Journal of Neuroscience Methods, 341, 108795. https://doi.org/10.1016/j.jneumeth.2020.108795
  • Zhou, T., Liu, M., Thung, K. H., & Shen, D. (2019). Latent representation learning for Alzheimer’s disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Transactions on Medical Imaging, 38(10), 2411–2422. https://doi.org/10.1109/TMI.2019.2913158
  • Zhou, T., Thung, K. H., Liu, M., Shi, F., Zhang, C., & Shen, D. (2020). Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Medical Image Analysis, 60, 101630. https://doi.org/10.1016/j.media.2019.101630
  • Zhou, T., Thung, K. H., Zhu, X., & Shen, D. (2019). Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis. Human Brain Mapping, 40(3), 1001–1016. https://doi.org/10.1002/hbm.24428
  • Zhu, Q., Yuan, N., Huang, J., Hao, X., & Zhang, D. (2019). Multi-modal AD classification via self-paced latent correlation analysis. Neurocomputing, 355, 143–154. https://doi.org/10.1016/j.neucom.2019.04.066

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