Early Diagnosis of Alzheimer’s Disease Based on Deep Learning and GWAS

  • Dan Pan
  • Yin Huang
  • An ZengEmail author
  • Longfei Jia
  • Xiaowei Song
  • for Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1072)


Alzheimer’s disease (AD) is a typical irreversible neurodegenerative disease. At present, the pathogenesis of AD remains elusive and the effective treatment of AD is still a challenge for clinicians. Therefore, early diagnosis is of great importance for the development of new drugs to prevent the progression of AD. With the rapid advancement of neuroimaging technology and deep learning, more and more researchers have turned to deep learning to analyze the brain images for early diagnosis of AD. Plus, studies have demonstrated that it is very likely that the genetic makeup of an individual may influence his/her susceptibility to AD traits. Researchers have begun to identify the genetic biomarkers associated to AD and evaluate the effects of genes upon the changes in the structure and function of the brain of AD patients. In this study, an ensemble model of multi-slice classifiers based on convolutional neural network (CNN) was proposed to make an early diagnosis of AD and at the same time to identify the significant brain regions related to AD. The morphological data of these identified brain regions and the genotype were utilized to carry out genome-wide association studies (GWAS) to explore the potential genetic biomarkers of AD.


Alzheimer’s disease (AD) Magnetic resonance imaging (MRI) Convolutional neural network (CNN) Genome-wide association study (GWAS) 



This study was supported by NSF of China (grant No. 61976058 and 61772143), NSF of Guangzhou city (grant No. 201601010034 and 201804010278), the Fund for Opening Project of Guangdong Key Laboratory of Big Data Analysis and Processing (grant No. 201801) and the Special Fund for Public Interest Research and Capacity Building Project of Guangdong province (grant No. 2015A030401107). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.


  1. 1.
    Ulep, M.G., Saraon, S.K., McLea, S.: Alzheimer disease. J. Nurse Pract. 14(3), 129–135 (2018)CrossRefGoogle Scholar
  2. 2.
    Prince, M.J.: World Alzheimer Report 2015: The Global Impact of Dementia: An Aalysis of Prevalence, Incidence, Cost and Trends. Alzheimer’s Disease International, London (2015)Google Scholar
  3. 3.
    Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 1015–1018. IEEE, Beijing, April 2014Google Scholar
  4. 4.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015). Scholar
  5. 5.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(Dec), 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Hinton, G.E.: Deep belief networks. Scholarpedia 4(5), 5947 (2009)CrossRefGoogle Scholar
  7. 7.
    LeCun Y.: LeNet-5, convolutional neural networks (2015).
  8. 8.
    Wang, S.H., Phillips, P., Sui, Y., et al.: Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J. Med. Syst. 42(5), 85 (2018)CrossRefGoogle Scholar
  9. 9.
    Huang, H., Shen, L.I., Thompson, P.M., et al.: Imaging genomics. In: Pacific Symposium, vol. 23, p. 304 (2018)Google Scholar
  10. 10.
    Salvatore, C., Cerasa, A., Battista, P., et al.: Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Front. Neurosci. 9, 307 (2015)CrossRefGoogle Scholar
  11. 11.
    Fan, L., et al.: The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016)CrossRefGoogle Scholar
  12. 12.
    Fischl, B.: FreeSurfer. Neuroimage 62(2), 774–781 (2012)CrossRefGoogle Scholar
  13. 13.
    Chang, C.C., Chow, C.C., Tellier, L.C.A.M., Vattikuti, S., Purcell, S.M., Lee, J.J.: Second generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015)CrossRefGoogle Scholar
  14. 14.
    Liu, X., Cheng, R., Verbitsky, M., et al.: Genome-wide association study identifies candidate genes for Parkinson’s disease in an Ashkenazi Jewish population. BMC Med. Genet. 12(1), 104 (2011)CrossRefGoogle Scholar
  15. 15.
    Pankratz, N., Wilk, J.B., Latourelle, J.C., et al.: Genomewide association study for susceptibility genes contributing to familial Parkinson disease. Hum. Genet. 124(6), 593–605 (2009). Scholar
  16. 16.
    Galichon, P., Mesnard, L., Hertig, A., et al.: Unrecognized sequence homologies may confound genome-wide association studies. Nucleic Acids Res. 40(11), 4774–4782 (2012)CrossRefGoogle Scholar
  17. 17.
    Jean, P.S.: Genes associated with schizophrenia identified using a whole genome scan. U.S. Patent Application 11/970,611 (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dan Pan
    • 1
    • 2
  • Yin Huang
    • 3
  • An Zeng
    • 3
    • 4
    Email author
  • Longfei Jia
    • 3
  • Xiaowei Song
    • 5
  • for Alzheimer’s Disease Neuroimaging Initiative (ADNI)
  1. 1.Guangdong Construction PolytechnicGuangzhouPeople’s Republic of China
  2. 2.Guangzhou Dazhi Networks Technology Co. Ltd.GuangzhouPeople’s Republic of China
  3. 3.Guangdong University of TechnologyGuangzhouPeople’s Republic of China
  4. 4.Guangdong Key Laboratory of Big Data Analysis and ProcessingGuangzhouPeople’s Republic of China
  5. 5.ImageTech LabSimon Fraser UniversityVancouverCanada

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