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

  • Baiying Lei
  • Wen Hou
  • Wenbin Zou
  • Xia Li
  • Cishen Zhang
  • Tianfu Wang
ORIGINAL RESEARCH
  • 42 Downloads

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.

Keywords

Alzheimer’s disease Correntropy Ensemble learning Longitudinal score prediction Spatial–temporal constraint 

Notes

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.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical EngineeringShenzhen UniversityShenzhenChina
  2. 2.Shenzhen Key Lab of Advanced Telecommunication and Information Processing, College of Information EngineeringShenzhen UniversityShenzhenChina
  3. 3.Faculty of Science, Engineering & TechnologySwinburne University of TechnologyMelbourneAustralia

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