Abstract
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.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.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.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Acknowledgments
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 (www.fnih.org). 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.
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Pan, D., Huang, Y., Zeng, A., Jia, L., Song, X., for Alzheimer’s Disease Neuroimaging Initiative (ADNI). (2019). Early Diagnosis of Alzheimer’s Disease Based on Deep Learning and GWAS. In: Zeng, A., Pan, D., Hao, T., Zhang, D., Shi, Y., Song, X. (eds) Human Brain and Artificial Intelligence. HBAI 2019. Communications in Computer and Information Science, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-15-1398-5_4
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