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Longitudinal score prediction for Alzheimer’s disease based on ensemble correntropy and spatial–temporal constraint

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Abstract

Neuroimaging data has been widely used to predict clinical scores for automatic diagnosis of Alzheimer’s disease (AD). For accurate clinical score prediction, one of the major challenges is high feature dimension of the imaging data. To address this issue, this paper presents an effective framework using a novel feature selection model via sparse learning. In contrast to previous approaches focusing on a single time point, this framework uses information at multiple time points. Specifically, a regularized correntropy with the spatial–temporal constraint is used to reduce the adverse effect of noise and outliers, and promote consistent and robust selection of features by exploring data characteristics. Furthermore, ensemble learning of support vector regression (SVR) is exploited to accurately predict AD scores based on the selected features. The proposed approach is extensively evaluated on the Alzheimer’s disease neuroimaging initiative (ADNI) dataset. Our experiments demonstrate that the proposed approach not only achieves promising regression accuracy, but also successfully recognizes disease-related biomarkers.

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Acknowledgements

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.

Funding

This study was funded partly by National Natural Science Foundation of China (Nos. 61771321, 61501305 and 81771922), National Natural Science Foundation of Guangdong Province (Nos.2017A030313377 and 2016A030313047), Shenzhen Key Basic Research Project (Nos. KQJSCX20170327151357330, JCYJ20170302153337765, JCYJ20160307154003475 JCYJ20150525092940982 and 201502007), Shenzhen Peacock Plan (NO. KQTD2016053112051497), the Interdisciplinary Innovation Team of Shenzhen University, and the National Natural Science Foundation of Shenzhen University (Nos. 827 − 000152, 2016077 and 201565 and 2016089). 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.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. HoffmannLa 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.; MesoScale 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 (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Authors and Affiliations

  1. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China

    Baiying Lei & Tianfu Wang

  2. Shenzhen Key Lab of Advanced Telecommunication and Information Processing, College of Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Wen Hou, Wenbin Zou & Xia Li

  3. Faculty of Science, Engineering & Technology, Swinburne University of Technology, Melbourne, Australia

    Cishen Zhang

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  1. Baiying Lei
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  2. Wen Hou
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  3. Wenbin Zou
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  4. Xia Li
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Correspondence to Wenbin Zou.

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Lei, B., Hou, W., Zou, W. et al. Longitudinal score prediction for Alzheimer’s disease based on ensemble correntropy and spatial–temporal constraint. Brain Imaging and Behavior 13, 126–137 (2019). https://doi.org/10.1007/s11682-018-9834-z

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