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Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer’s Disease

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Human Brain and Artificial Intelligence (HBAI 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1072))

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Abstract

Computer-aided diagnosis (CAD) systems for medical images are seen as effective tools to improve the efficiency of diagnosis and prognosis of Alzheimer’s disease (AD). The current state-of-the-art models for many images analyzing tasks are based on Convolutional Neural Networks (CNN). However, the lack of training data is a common challenge in applying CNN to the diagnosis of AD and its prodromal stages. Another challenge for CAD applications is the controversy between the requiring of longitudinal cortical structural information for higher diagnosis/prognosis accuracy and the computing ability for processing varied imaging features. To address these two challenges, we propose a novel computer-aided AD diagnosis system CNN-Stochastic Coordinate Coding (MSCC) which integrates CNN with transfer learning strategy, a novel MSCC algorithm and our effective AD-related biomarkers–multivariate morphometry statistics (MMS). We applied the novel CNN-MSCC system on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to predict future cognitive clinical measures with baseline Hippocampal/Ventricle MMS features and cortical thickness. The experimental results showed that CNN-MSCC achieved superior results. The proposed system may aid in expediting the diagnosis of AD progress, facilitating earlier clinical intervention, and resulting in improved clinical outcomes.

Q. Dong and J. Zhang—Authors contributed equally.

Acknowledgments: 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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Acknowledgement

Algorithm development and image analysis for this study was funded, in part, by the National Institute on Aging (RF1AG051710 to QD, JZ, PMT, JY and YW, R01EB025-032 to YW, R01HL128818 to QD and YW, R01AG031581 and P30AG19610 to RJC, U54EB020403 to PMT and YW), the National Science Foundation (IIS-1421165 to JZ and YW), and Arizona Alzheimer’s Consortium (JZ, RJC and YW). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

Data collection and sharing for this project was funded by the 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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Authors and Affiliations

  1. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

    Qunxi Dong, Jie Zhang, Qingyang Li & Yalin Wang

  2. Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA

    Pau M. Thompson

  3. Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA

    Richard J. Caselli

  4. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Jieping Ye

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Correspondence to Qunxi Dong .

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Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    An Zeng

  2. Guangdong Construction Polytechnic, Guangzhou, China

    Dan Pan

  3. South China Normal University, Guangzhou, China

    Tianyong Hao

  4. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. University of Notre Dame, South Bend, IN, USA

    Yiyu Shi

  6. Simon Fraser University, Vancouver, BC, Canada

    Xiaowei Song

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Dong, Q. et al. (2019). Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer’s Disease. 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_2

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  • DOI: https://doi.org/10.1007/978-981-15-1398-5_2

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