Abstract
Alzheimer’s disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation.
For the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.ucla.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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References
Alzheimer’s, Association, Thies, W., Bleiler, L.: Alzheimer’s disease facts and figures. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association 7, 208–244 (2011)
Petersen, R.C., Smith, G.E., Waring, S.C., et al.: Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology 56, 303–308 (1999)
Stephan, B.C., Hunter, S., Harris, D., et al.: The neuropathological profile of mild cognitive impairment (MCI): a systematic review. Mol. Psychiatry 17, 1056–1076 (2012)
Petersen, R.C., Roberts, R.O., Knopman, D.S., et al.: Mild cognitive impairment: ten years later. Archives of Neurology 66, 1447–1455 (2009)
Wang, H., Nie, F., Huang, H., Kim, S., Nho, K., et al.: Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics 28, 229–237 (2012)
Meda, S.A., Narayanan, B., Liu, J., Perrone-Bizzozero, N.I., et al.: A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer’s disease in the ADNI cohort. NeuroImage 60, 1608–1621 (2012)
Risacher, S.L., Kim, S., et al.: The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI). Frontiers in Aging Neuroscience 5, 11 (2013)
Zhou, D., Bousquet, O., Weston, J., Scholkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems (NIPS), vol. 16, pp. 321–328 (2004)
Belkin, M., Matveeva, I., Niyogi, P.: Regularization and Semi-supervised Learning on Large Graphs. In: Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), vol. 3120, pp. 624–638. Springer, Heidelberg (2004)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the Twenty-first International Conference on Machine Learning (ICML), pp. 912–919. AAAI Press, Washington, DC (2003)
Chapelle, O., Weston, J., Scholkopf, B.: Cluster kernels for semi-supervised learning. In: Advances in Neural Information Processing Systems (NIPS), vol. 15, pp. 585–592 (2003)
Kim, D., Shin, H., Song, Y.S., Kim, J.H.: Synergistic effect of different levels of genomic data for cancer clinical outcome prediction. J. Biomed Inform. 45, 1191–1198 (2012)
Tsuda, K., Shin, H., Scholkopf, B.: Fast protein classification with multiple networks. Bioinformatics 21(suppl. 2), ii59–ii65 (2005)
Shin, H., Tsuda, K.: Prediction of Protein Function from Networks. In: Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning, ch. 20, pp. 339–352. MIT Press (2006)
Spellman, P.T., Sherlock, G., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998)
Segal, E., Shapira, M., et al.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34, 166–176 (2003)
Ohn, J.H., Kim, J., Kim, J.H.: Genomic characterization of perturbation sensitivity. Bioinformatics 23, i354–i358 (2007)
Risacher, S.L., Shen, L., West, J.D., et al.: Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiology of Aging 31, 1401–1418 (2010)
Risacher, S.L., Saykin, A.J., West, J.D., et al.: Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Current Alzheimer Research 6, 347–361 (2009)
Weiner, M.W., Veitch, D.P., et al.: The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association 8, S1–S68 (2012)
Chung, F.R.K.: Spectral Graph Theory. Regional Conference Series in Mathematics, vol. 92 (1997)
Shin, H., Lisewski, A.M., Lichtarge, O.: Graph sharpening plus graph integration: a synergy that improves protein functional classification. Bioinformatics 23, 3217–3224 (2007)
Gribskov, M., Robinson, N.L.: Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Comput. Chem. 20, 25–33 (1996)
Jafari, P., et al.: An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Med. Inform. Decis. Mak. 6, 27 (2006)
Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
Scahill, R.I., Schott, J.M., et al.: Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluid-registered serial MRI. Proc Natl. Acad. Sci. U. S. A. 99, 4703–4707 (2002)
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Kim, D. et al. (2013). A Graph-Based Integration of Multimodal Brain Imaging Data for the Detection of Early Mild Cognitive Impairment (E-MCI). In: Shen, L., Liu, T., Yap, PT., Huang, H., Shen, D., Westin, CF. (eds) Multimodal Brain Image Analysis. MBIA 2013. Lecture Notes in Computer Science, vol 8159. Springer, Cham. https://doi.org/10.1007/978-3-319-02126-3_16
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DOI: https://doi.org/10.1007/978-3-319-02126-3_16
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